Efficient search of decision makers’ region of interest by using preference directions in multi-objective coevolutionary algorithm

Most multi-objective evolutionary algorithms (MOEAs) provide decision makers (DMs) with an overall trade-off Pareto front. However, in practice, DMs are generally interested in a specific subset of the Pareto front that satisfies their preferences; this subset is known as the region of interest (ROI...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhou, Dan [verfasserIn] Du, Jiqing [verfasserIn] Arai, Sachiyo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Multi-objective Decision makers Region of interest Preference direction Social leader

Übergeordnetes Werk:	Enthalten in: Swarm and evolutionary computation - Amsterdam [u.a.] : Elsevier, 2011, 81
Übergeordnetes Werk:	volume:81

DOI / URN:	10.1016/j.swevo.2023.101349

Katalog-ID:	ELV060590238

Internformat


LEADER	01000caa a22002652 4500
001	ELV060590238
003	DE-627
005	20230926164605.0
007	cr uuu---uuuuu
008	230718s2023 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1016/j.swevo.2023.101349 \|2 doi
035			\|a (DE-627)ELV060590238
035			\|a (ELSEVIER)S2210-6502(23)00122-0
040			\|a DE-627 \|b ger \|c DE-627 \|e rda
041			\|a eng
082	0	4	\|a 004 \|q VZ
100	1		\|a Zhou, Dan \|e verfasserin \|4 aut
245	1	0	\|a Efficient search of decision makers’ region of interest by using preference directions in multi-objective coevolutionary algorithm
264		1	\|c 2023
336			\|a nicht spezifiziert \|b zzz \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Most multi-objective evolutionary algorithms (MOEAs) provide decision makers (DMs) with an overall trade-off Pareto front. However, in practice, DMs are generally interested in a specific subset of the Pareto front that satisfies their preferences; this subset is known as the region of interest (ROI). Existing preference-based MOEAs may fail to provide satisfactory ROIs to DMs owing to inaccurate analysis of the DM’s preference information or weak diversity maintenance. This study proposes a multi-objective coevolutionary algorithm (MOCA) based on the DM’s preference direction (MOCA-PD), inspired by the mechanism of the social division of labor. To achieve the social goal of obtaining the ROI, a search center is generated by a preference model based on the DM’s preference direction. The preference model uses the search center as the criterion for the social selection of talents, with some solutions that meet the DM’s preferences selected as social leaders. To accelerate the realization of the social goal, the preference model assigns team members according to the leader’s ability; this is similar to how more capable leaders lead more team members in a society. Two teamwork operations are defined for team members to communicate (crossover and mutation) and generate offspring that are better aligned with the DM’s preferences. In addition, the diversity of social leaders is ensured by introducing an elite archive and ϵ -dominance mechanism. Experimental results on the ZDT, DTLZ, and MaF problems show that the proposed algorithm can effectively guide the population to the ROI, enabling DMs to make better and more reliable decisions.
650		4	\|a Multi-objective
650		4	\|a Decision makers
650		4	\|a Region of interest
650		4	\|a Preference direction
650		4	\|a Social leader
700	1		\|a Du, Jiqing \|e verfasserin \|4 aut
700	1		\|a Arai, Sachiyo \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Swarm and evolutionary computation \|d Amsterdam [u.a.] : Elsevier, 2011 \|g 81 \|h Online-Ressource \|w (DE-627)661267121 \|w (DE-600)2611387-9 \|w (DE-576)346017084 \|7 nnns
773	1	8	\|g volume:81
912			\|a GBV_USEFLAG_U
912			\|a GBV_ELV
912			\|a SYSFLAG_U
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 81

Indexfelder

author_variant	d z dz j d jd s a sa
matchkey_str	zhoudandujiqingaraisachiyo:2023----:fiinsacodcsomkrrgooitrsbuigrfrneietosnutoj
hierarchy_sort_str	2023
publishDate	2023
allfields	10.1016/j.swevo.2023.101349 doi (DE-627)ELV060590238 (ELSEVIER)S2210-6502(23)00122-0 DE-627 ger DE-627 rda eng 004 VZ Zhou, Dan verfasserin aut Efficient search of decision makers’ region of interest by using preference directions in multi-objective coevolutionary algorithm 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Most multi-objective evolutionary algorithms (MOEAs) provide decision makers (DMs) with an overall trade-off Pareto front. However, in practice, DMs are generally interested in a specific subset of the Pareto front that satisfies their preferences; this subset is known as the region of interest (ROI). Existing preference-based MOEAs may fail to provide satisfactory ROIs to DMs owing to inaccurate analysis of the DM’s preference information or weak diversity maintenance. This study proposes a multi-objective coevolutionary algorithm (MOCA) based on the DM’s preference direction (MOCA-PD), inspired by the mechanism of the social division of labor. To achieve the social goal of obtaining the ROI, a search center is generated by a preference model based on the DM’s preference direction. The preference model uses the search center as the criterion for the social selection of talents, with some solutions that meet the DM’s preferences selected as social leaders. To accelerate the realization of the social goal, the preference model assigns team members according to the leader’s ability; this is similar to how more capable leaders lead more team members in a society. Two teamwork operations are defined for team members to communicate (crossover and mutation) and generate offspring that are better aligned with the DM’s preferences. In addition, the diversity of social leaders is ensured by introducing an elite archive and ϵ -dominance mechanism. Experimental results on the ZDT, DTLZ, and MaF problems show that the proposed algorithm can effectively guide the population to the ROI, enabling DMs to make better and more reliable decisions. Multi-objective Decision makers Region of interest Preference direction Social leader Du, Jiqing verfasserin aut Arai, Sachiyo verfasserin aut Enthalten in Swarm and evolutionary computation Amsterdam [u.a.] : Elsevier, 2011 81 Online-Ressource (DE-627)661267121 (DE-600)2611387-9 (DE-576)346017084 nnns volume:81 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81
spelling	10.1016/j.swevo.2023.101349 doi (DE-627)ELV060590238 (ELSEVIER)S2210-6502(23)00122-0 DE-627 ger DE-627 rda eng 004 VZ Zhou, Dan verfasserin aut Efficient search of decision makers’ region of interest by using preference directions in multi-objective coevolutionary algorithm 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Most multi-objective evolutionary algorithms (MOEAs) provide decision makers (DMs) with an overall trade-off Pareto front. However, in practice, DMs are generally interested in a specific subset of the Pareto front that satisfies their preferences; this subset is known as the region of interest (ROI). Existing preference-based MOEAs may fail to provide satisfactory ROIs to DMs owing to inaccurate analysis of the DM’s preference information or weak diversity maintenance. This study proposes a multi-objective coevolutionary algorithm (MOCA) based on the DM’s preference direction (MOCA-PD), inspired by the mechanism of the social division of labor. To achieve the social goal of obtaining the ROI, a search center is generated by a preference model based on the DM’s preference direction. The preference model uses the search center as the criterion for the social selection of talents, with some solutions that meet the DM’s preferences selected as social leaders. To accelerate the realization of the social goal, the preference model assigns team members according to the leader’s ability; this is similar to how more capable leaders lead more team members in a society. Two teamwork operations are defined for team members to communicate (crossover and mutation) and generate offspring that are better aligned with the DM’s preferences. In addition, the diversity of social leaders is ensured by introducing an elite archive and ϵ -dominance mechanism. Experimental results on the ZDT, DTLZ, and MaF problems show that the proposed algorithm can effectively guide the population to the ROI, enabling DMs to make better and more reliable decisions. Multi-objective Decision makers Region of interest Preference direction Social leader Du, Jiqing verfasserin aut Arai, Sachiyo verfasserin aut Enthalten in Swarm and evolutionary computation Amsterdam [u.a.] : Elsevier, 2011 81 Online-Ressource (DE-627)661267121 (DE-600)2611387-9 (DE-576)346017084 nnns volume:81 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81
allfields_unstemmed	10.1016/j.swevo.2023.101349 doi (DE-627)ELV060590238 (ELSEVIER)S2210-6502(23)00122-0 DE-627 ger DE-627 rda eng 004 VZ Zhou, Dan verfasserin aut Efficient search of decision makers’ region of interest by using preference directions in multi-objective coevolutionary algorithm 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Most multi-objective evolutionary algorithms (MOEAs) provide decision makers (DMs) with an overall trade-off Pareto front. However, in practice, DMs are generally interested in a specific subset of the Pareto front that satisfies their preferences; this subset is known as the region of interest (ROI). Existing preference-based MOEAs may fail to provide satisfactory ROIs to DMs owing to inaccurate analysis of the DM’s preference information or weak diversity maintenance. This study proposes a multi-objective coevolutionary algorithm (MOCA) based on the DM’s preference direction (MOCA-PD), inspired by the mechanism of the social division of labor. To achieve the social goal of obtaining the ROI, a search center is generated by a preference model based on the DM’s preference direction. The preference model uses the search center as the criterion for the social selection of talents, with some solutions that meet the DM’s preferences selected as social leaders. To accelerate the realization of the social goal, the preference model assigns team members according to the leader’s ability; this is similar to how more capable leaders lead more team members in a society. Two teamwork operations are defined for team members to communicate (crossover and mutation) and generate offspring that are better aligned with the DM’s preferences. In addition, the diversity of social leaders is ensured by introducing an elite archive and ϵ -dominance mechanism. Experimental results on the ZDT, DTLZ, and MaF problems show that the proposed algorithm can effectively guide the population to the ROI, enabling DMs to make better and more reliable decisions. Multi-objective Decision makers Region of interest Preference direction Social leader Du, Jiqing verfasserin aut Arai, Sachiyo verfasserin aut Enthalten in Swarm and evolutionary computation Amsterdam [u.a.] : Elsevier, 2011 81 Online-Ressource (DE-627)661267121 (DE-600)2611387-9 (DE-576)346017084 nnns volume:81 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81
allfieldsGer	10.1016/j.swevo.2023.101349 doi (DE-627)ELV060590238 (ELSEVIER)S2210-6502(23)00122-0 DE-627 ger DE-627 rda eng 004 VZ Zhou, Dan verfasserin aut Efficient search of decision makers’ region of interest by using preference directions in multi-objective coevolutionary algorithm 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Most multi-objective evolutionary algorithms (MOEAs) provide decision makers (DMs) with an overall trade-off Pareto front. However, in practice, DMs are generally interested in a specific subset of the Pareto front that satisfies their preferences; this subset is known as the region of interest (ROI). Existing preference-based MOEAs may fail to provide satisfactory ROIs to DMs owing to inaccurate analysis of the DM’s preference information or weak diversity maintenance. This study proposes a multi-objective coevolutionary algorithm (MOCA) based on the DM’s preference direction (MOCA-PD), inspired by the mechanism of the social division of labor. To achieve the social goal of obtaining the ROI, a search center is generated by a preference model based on the DM’s preference direction. The preference model uses the search center as the criterion for the social selection of talents, with some solutions that meet the DM’s preferences selected as social leaders. To accelerate the realization of the social goal, the preference model assigns team members according to the leader’s ability; this is similar to how more capable leaders lead more team members in a society. Two teamwork operations are defined for team members to communicate (crossover and mutation) and generate offspring that are better aligned with the DM’s preferences. In addition, the diversity of social leaders is ensured by introducing an elite archive and ϵ -dominance mechanism. Experimental results on the ZDT, DTLZ, and MaF problems show that the proposed algorithm can effectively guide the population to the ROI, enabling DMs to make better and more reliable decisions. Multi-objective Decision makers Region of interest Preference direction Social leader Du, Jiqing verfasserin aut Arai, Sachiyo verfasserin aut Enthalten in Swarm and evolutionary computation Amsterdam [u.a.] : Elsevier, 2011 81 Online-Ressource (DE-627)661267121 (DE-600)2611387-9 (DE-576)346017084 nnns volume:81 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81
allfieldsSound	10.1016/j.swevo.2023.101349 doi (DE-627)ELV060590238 (ELSEVIER)S2210-6502(23)00122-0 DE-627 ger DE-627 rda eng 004 VZ Zhou, Dan verfasserin aut Efficient search of decision makers’ region of interest by using preference directions in multi-objective coevolutionary algorithm 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Most multi-objective evolutionary algorithms (MOEAs) provide decision makers (DMs) with an overall trade-off Pareto front. However, in practice, DMs are generally interested in a specific subset of the Pareto front that satisfies their preferences; this subset is known as the region of interest (ROI). Existing preference-based MOEAs may fail to provide satisfactory ROIs to DMs owing to inaccurate analysis of the DM’s preference information or weak diversity maintenance. This study proposes a multi-objective coevolutionary algorithm (MOCA) based on the DM’s preference direction (MOCA-PD), inspired by the mechanism of the social division of labor. To achieve the social goal of obtaining the ROI, a search center is generated by a preference model based on the DM’s preference direction. The preference model uses the search center as the criterion for the social selection of talents, with some solutions that meet the DM’s preferences selected as social leaders. To accelerate the realization of the social goal, the preference model assigns team members according to the leader’s ability; this is similar to how more capable leaders lead more team members in a society. Two teamwork operations are defined for team members to communicate (crossover and mutation) and generate offspring that are better aligned with the DM’s preferences. In addition, the diversity of social leaders is ensured by introducing an elite archive and ϵ -dominance mechanism. Experimental results on the ZDT, DTLZ, and MaF problems show that the proposed algorithm can effectively guide the population to the ROI, enabling DMs to make better and more reliable decisions. Multi-objective Decision makers Region of interest Preference direction Social leader Du, Jiqing verfasserin aut Arai, Sachiyo verfasserin aut Enthalten in Swarm and evolutionary computation Amsterdam [u.a.] : Elsevier, 2011 81 Online-Ressource (DE-627)661267121 (DE-600)2611387-9 (DE-576)346017084 nnns volume:81 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81
language	English
source	Enthalten in Swarm and evolutionary computation 81 volume:81
sourceStr	Enthalten in Swarm and evolutionary computation 81 volume:81
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Multi-objective Decision makers Region of interest Preference direction Social leader
dewey-raw	004
isfreeaccess_bool	false
container_title	Swarm and evolutionary computation
authorswithroles_txt_mv	Zhou, Dan @@aut@@ Du, Jiqing @@aut@@ Arai, Sachiyo @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	661267121
dewey-sort	14
id	ELV060590238
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV060590238</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230926164605.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230718s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.swevo.2023.101349</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV060590238</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S2210-6502(23)00122-0</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhou, Dan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Efficient search of decision makers’ region of interest by using preference directions in multi-objective coevolutionary algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Most multi-objective evolutionary algorithms (MOEAs) provide decision makers (DMs) with an overall trade-off Pareto front. However, in practice, DMs are generally interested in a specific subset of the Pareto front that satisfies their preferences; this subset is known as the region of interest (ROI). Existing preference-based MOEAs may fail to provide satisfactory ROIs to DMs owing to inaccurate analysis of the DM’s preference information or weak diversity maintenance. This study proposes a multi-objective coevolutionary algorithm (MOCA) based on the DM’s preference direction (MOCA-PD), inspired by the mechanism of the social division of labor. To achieve the social goal of obtaining the ROI, a search center is generated by a preference model based on the DM’s preference direction. The preference model uses the search center as the criterion for the social selection of talents, with some solutions that meet the DM’s preferences selected as social leaders. To accelerate the realization of the social goal, the preference model assigns team members according to the leader’s ability; this is similar to how more capable leaders lead more team members in a society. Two teamwork operations are defined for team members to communicate (crossover and mutation) and generate offspring that are better aligned with the DM’s preferences. In addition, the diversity of social leaders is ensured by introducing an elite archive and ϵ -dominance mechanism. Experimental results on the ZDT, DTLZ, and MaF problems show that the proposed algorithm can effectively guide the population to the ROI, enabling DMs to make better and more reliable decisions.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multi-objective</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Decision makers</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Region of interest</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Preference direction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Social leader</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Du, Jiqing</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Arai, Sachiyo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Swarm and evolutionary computation</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier, 2011</subfield><subfield code="g">81</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)661267121</subfield><subfield code="w">(DE-600)2611387-9</subfield><subfield code="w">(DE-576)346017084</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:81</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">81</subfield></datafield></record></collection>
author	Zhou, Dan
spellingShingle	Zhou, Dan ddc 004 misc Multi-objective misc Decision makers misc Region of interest misc Preference direction misc Social leader Efficient search of decision makers’ region of interest by using preference directions in multi-objective coevolutionary algorithm
authorStr	Zhou, Dan
ppnlink_with_tag_str_mv	@@773@@(DE-627)661267121
format	electronic Article
dewey-ones	004 - Data processing & computer science
delete_txt_mv	keep
author_role	aut aut aut
collection	elsevier
remote_str	true
illustrated	Not Illustrated
topic_title	004 VZ Efficient search of decision makers’ region of interest by using preference directions in multi-objective coevolutionary algorithm Multi-objective Decision makers Region of interest Preference direction Social leader
topic	ddc 004 misc Multi-objective misc Decision makers misc Region of interest misc Preference direction misc Social leader
topic_unstemmed	ddc 004 misc Multi-objective misc Decision makers misc Region of interest misc Preference direction misc Social leader
topic_browse	ddc 004 misc Multi-objective misc Decision makers misc Region of interest misc Preference direction misc Social leader
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Swarm and evolutionary computation
hierarchy_parent_id	661267121
dewey-tens	000 - Computer science, knowledge & systems
hierarchy_top_title	Swarm and evolutionary computation
isfreeaccess_txt	false
familylinks_str_mv	(DE-627)661267121 (DE-600)2611387-9 (DE-576)346017084
title	Efficient search of decision makers’ region of interest by using preference directions in multi-objective coevolutionary algorithm
ctrlnum	(DE-627)ELV060590238 (ELSEVIER)S2210-6502(23)00122-0
title_full	Efficient search of decision makers’ region of interest by using preference directions in multi-objective coevolutionary algorithm
author_sort	Zhou, Dan
journal	Swarm and evolutionary computation
journalStr	Swarm and evolutionary computation
lang_code	eng
isOA_bool	false
dewey-hundreds	000 - Computer science, information & general works
recordtype	marc
publishDateSort	2023
contenttype_str_mv	zzz
author_browse	Zhou, Dan Du, Jiqing Arai, Sachiyo
container_volume	81
class	004 VZ
format_se	Elektronische Aufsätze
author-letter	Zhou, Dan
doi_str_mv	10.1016/j.swevo.2023.101349
dewey-full	004
author2-role	verfasserin
title_sort	efficient search of decision makers’ region of interest by using preference directions in multi-objective coevolutionary algorithm
title_auth	Efficient search of decision makers’ region of interest by using preference directions in multi-objective coevolutionary algorithm
abstract	Most multi-objective evolutionary algorithms (MOEAs) provide decision makers (DMs) with an overall trade-off Pareto front. However, in practice, DMs are generally interested in a specific subset of the Pareto front that satisfies their preferences; this subset is known as the region of interest (ROI). Existing preference-based MOEAs may fail to provide satisfactory ROIs to DMs owing to inaccurate analysis of the DM’s preference information or weak diversity maintenance. This study proposes a multi-objective coevolutionary algorithm (MOCA) based on the DM’s preference direction (MOCA-PD), inspired by the mechanism of the social division of labor. To achieve the social goal of obtaining the ROI, a search center is generated by a preference model based on the DM’s preference direction. The preference model uses the search center as the criterion for the social selection of talents, with some solutions that meet the DM’s preferences selected as social leaders. To accelerate the realization of the social goal, the preference model assigns team members according to the leader’s ability; this is similar to how more capable leaders lead more team members in a society. Two teamwork operations are defined for team members to communicate (crossover and mutation) and generate offspring that are better aligned with the DM’s preferences. In addition, the diversity of social leaders is ensured by introducing an elite archive and ϵ -dominance mechanism. Experimental results on the ZDT, DTLZ, and MaF problems show that the proposed algorithm can effectively guide the population to the ROI, enabling DMs to make better and more reliable decisions.
abstractGer	Most multi-objective evolutionary algorithms (MOEAs) provide decision makers (DMs) with an overall trade-off Pareto front. However, in practice, DMs are generally interested in a specific subset of the Pareto front that satisfies their preferences; this subset is known as the region of interest (ROI). Existing preference-based MOEAs may fail to provide satisfactory ROIs to DMs owing to inaccurate analysis of the DM’s preference information or weak diversity maintenance. This study proposes a multi-objective coevolutionary algorithm (MOCA) based on the DM’s preference direction (MOCA-PD), inspired by the mechanism of the social division of labor. To achieve the social goal of obtaining the ROI, a search center is generated by a preference model based on the DM’s preference direction. The preference model uses the search center as the criterion for the social selection of talents, with some solutions that meet the DM’s preferences selected as social leaders. To accelerate the realization of the social goal, the preference model assigns team members according to the leader’s ability; this is similar to how more capable leaders lead more team members in a society. Two teamwork operations are defined for team members to communicate (crossover and mutation) and generate offspring that are better aligned with the DM’s preferences. In addition, the diversity of social leaders is ensured by introducing an elite archive and ϵ -dominance mechanism. Experimental results on the ZDT, DTLZ, and MaF problems show that the proposed algorithm can effectively guide the population to the ROI, enabling DMs to make better and more reliable decisions.
abstract_unstemmed	Most multi-objective evolutionary algorithms (MOEAs) provide decision makers (DMs) with an overall trade-off Pareto front. However, in practice, DMs are generally interested in a specific subset of the Pareto front that satisfies their preferences; this subset is known as the region of interest (ROI). Existing preference-based MOEAs may fail to provide satisfactory ROIs to DMs owing to inaccurate analysis of the DM’s preference information or weak diversity maintenance. This study proposes a multi-objective coevolutionary algorithm (MOCA) based on the DM’s preference direction (MOCA-PD), inspired by the mechanism of the social division of labor. To achieve the social goal of obtaining the ROI, a search center is generated by a preference model based on the DM’s preference direction. The preference model uses the search center as the criterion for the social selection of talents, with some solutions that meet the DM’s preferences selected as social leaders. To accelerate the realization of the social goal, the preference model assigns team members according to the leader’s ability; this is similar to how more capable leaders lead more team members in a society. Two teamwork operations are defined for team members to communicate (crossover and mutation) and generate offspring that are better aligned with the DM’s preferences. In addition, the diversity of social leaders is ensured by introducing an elite archive and ϵ -dominance mechanism. Experimental results on the ZDT, DTLZ, and MaF problems show that the proposed algorithm can effectively guide the population to the ROI, enabling DMs to make better and more reliable decisions.
collection_details	GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700
title_short	Efficient search of decision makers’ region of interest by using preference directions in multi-objective coevolutionary algorithm
remote_bool	true
author2	Du, Jiqing Arai, Sachiyo
author2Str	Du, Jiqing Arai, Sachiyo
ppnlink	661267121
mediatype_str_mv	c
isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1016/j.swevo.2023.101349
up_date	2024-07-06T16:36:09.061Z
_version_	1803848273431101440
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV060590238</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230926164605.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230718s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.swevo.2023.101349</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV060590238</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S2210-6502(23)00122-0</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhou, Dan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Efficient search of decision makers’ region of interest by using preference directions in multi-objective coevolutionary algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Most multi-objective evolutionary algorithms (MOEAs) provide decision makers (DMs) with an overall trade-off Pareto front. However, in practice, DMs are generally interested in a specific subset of the Pareto front that satisfies their preferences; this subset is known as the region of interest (ROI). Existing preference-based MOEAs may fail to provide satisfactory ROIs to DMs owing to inaccurate analysis of the DM’s preference information or weak diversity maintenance. This study proposes a multi-objective coevolutionary algorithm (MOCA) based on the DM’s preference direction (MOCA-PD), inspired by the mechanism of the social division of labor. To achieve the social goal of obtaining the ROI, a search center is generated by a preference model based on the DM’s preference direction. The preference model uses the search center as the criterion for the social selection of talents, with some solutions that meet the DM’s preferences selected as social leaders. To accelerate the realization of the social goal, the preference model assigns team members according to the leader’s ability; this is similar to how more capable leaders lead more team members in a society. Two teamwork operations are defined for team members to communicate (crossover and mutation) and generate offspring that are better aligned with the DM’s preferences. In addition, the diversity of social leaders is ensured by introducing an elite archive and ϵ -dominance mechanism. Experimental results on the ZDT, DTLZ, and MaF problems show that the proposed algorithm can effectively guide the population to the ROI, enabling DMs to make better and more reliable decisions.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multi-objective</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Decision makers</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Region of interest</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Preference direction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Social leader</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Du, Jiqing</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Arai, Sachiyo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Swarm and evolutionary computation</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier, 2011</subfield><subfield code="g">81</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)661267121</subfield><subfield code="w">(DE-600)2611387-9</subfield><subfield code="w">(DE-576)346017084</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:81</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">81</subfield></datafield></record></collection>
score	7.39777

Nicht das Richtige dabei?

Schreiben Sie uns!

Efficient search of decision makers’ region of interest by using preference directions in multi-objective coevolutionary algorithm

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?