A self-evolving fuzzy system online prediction-based dynamic multi-objective evolutionary algorithm
The changes of dynamic multi-objective optimization problems in decision space are usually nonlinear. However, the previous dynamic multi-objective evolutionary algorithms usually use linear prediction models to generate the initial population in the new environment, and some nonlinear prediction mo...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sun, Jing [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
|---|
| Schlagwörter: |
|---|
| Umfang: |
17 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study - Petrruzziello, Carmelina ELSEVIER, 2013, an international journal, New York, NY |
|---|---|
| Übergeordnetes Werk: |
volume:612 ; year:2022 ; pages:638-654 ; extent:17 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.ins.2022.08.072 |
|---|
| Katalog-ID: |
ELV059467088 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV059467088 | ||
| 003 | DE-627 | ||
| 005 | 20230626052942.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 221219s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.ins.2022.08.072 |2 doi | |
| 028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001960.pica |
| 035 | |a (DE-627)ELV059467088 | ||
| 035 | |a (ELSEVIER)S0020-0255(22)00982-3 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 610 |q VZ |
| 082 | 0 | 4 | |a 570 |q VZ |
| 084 | |a BIODIV |q DE-30 |2 fid | ||
| 084 | |a 35.70 |2 bkl | ||
| 084 | |a 42.12 |2 bkl | ||
| 084 | |a 42.15 |2 bkl | ||
| 100 | 1 | |a Sun, Jing |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a A self-evolving fuzzy system online prediction-based dynamic multi-objective evolutionary algorithm |
| 264 | 1 | |c 2022transfer abstract | |
| 300 | |a 17 | ||
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a The changes of dynamic multi-objective optimization problems in decision space are usually nonlinear. However, the previous dynamic multi-objective evolutionary algorithms usually use linear prediction models to generate the initial population in the new environment, and some nonlinear prediction models often have high computational cost. Therefore, it is difficult to quickly and accurately respond to nonlinear environmental changes. This paper presents a dynamic multi-objective evolutionary algorithm based on online prediction of self-evolving fuzzy system (SEFS). In this algorithm, the decomposition based multi-objective evolutionary algorithm (MOEA/D) acts as the static optimizer. When the environment changes, individuals are first put into an associate set of their corresponding weight vectors. Then, the time series of each variable is constructed based on the associate set, and the SEFS online prediction model is established. Finally, an environmental response strategy based on SEFS is designed to quickly generate an initial population with high performance in the new environment. The proposed algorithm is compared with seven state-of-the-art dynamic multi-objective evolutionary algorithms on 20 benchmark functions. Experimental results show that the proposed algorithm can quickly and accurately respond to nonlinear environmental changes, and has competitiveness. | ||
| 520 | |a The changes of dynamic multi-objective optimization problems in decision space are usually nonlinear. However, the previous dynamic multi-objective evolutionary algorithms usually use linear prediction models to generate the initial population in the new environment, and some nonlinear prediction models often have high computational cost. Therefore, it is difficult to quickly and accurately respond to nonlinear environmental changes. This paper presents a dynamic multi-objective evolutionary algorithm based on online prediction of self-evolving fuzzy system (SEFS). In this algorithm, the decomposition based multi-objective evolutionary algorithm (MOEA/D) acts as the static optimizer. When the environment changes, individuals are first put into an associate set of their corresponding weight vectors. Then, the time series of each variable is constructed based on the associate set, and the SEFS online prediction model is established. Finally, an environmental response strategy based on SEFS is designed to quickly generate an initial population with high performance in the new environment. The proposed algorithm is compared with seven state-of-the-art dynamic multi-objective evolutionary algorithms on 20 benchmark functions. Experimental results show that the proposed algorithm can quickly and accurately respond to nonlinear environmental changes, and has competitiveness. | ||
| 650 | 7 | |a Dynamic multi-objective optimization |2 Elsevier | |
| 650 | 7 | |a Time series |2 Elsevier | |
| 650 | 7 | |a Weight vectors |2 Elsevier | |
| 650 | 7 | |a Self-evolving fuzzy system |2 Elsevier | |
| 700 | 1 | |a Gan, Xingjia |4 oth | |
| 700 | 1 | |a Gong, Dunwei |4 oth | |
| 700 | 1 | |a Tang, Xiaoke |4 oth | |
| 700 | 1 | |a Dai, Hongwei |4 oth | |
| 700 | 1 | |a Zhong, Zhaoman |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science Inc |a Petrruzziello, Carmelina ELSEVIER |t Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study |d 2013 |d an international journal |g New York, NY |w (DE-627)ELV011843691 |
| 773 | 1 | 8 | |g volume:612 |g year:2022 |g pages:638-654 |g extent:17 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.ins.2022.08.072 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a FID-BIODIV | ||
| 912 | |a SSG-OLC-PHA | ||
| 936 | b | k | |a 35.70 |j Biochemie: Allgemeines |q VZ |
| 936 | b | k | |a 42.12 |j Biophysik |q VZ |
| 936 | b | k | |a 42.15 |j Zellbiologie |q VZ |
| 951 | |a AR | ||
| 952 | |d 612 |j 2022 |h 638-654 |g 17 | ||
| author_variant |
j s js |
|---|---|
| matchkey_str |
sunjingganxingjiagongdunweitangxiaokedai:2022----:sleovnfzyytmnierdcinaednmcutojc |
| hierarchy_sort_str |
2022transfer abstract |
| bklnumber |
35.70 42.12 42.15 |
| publishDate |
2022 |
| allfields |
10.1016/j.ins.2022.08.072 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001960.pica (DE-627)ELV059467088 (ELSEVIER)S0020-0255(22)00982-3 DE-627 ger DE-627 rakwb eng 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Sun, Jing verfasserin aut A self-evolving fuzzy system online prediction-based dynamic multi-objective evolutionary algorithm 2022transfer abstract 17 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The changes of dynamic multi-objective optimization problems in decision space are usually nonlinear. However, the previous dynamic multi-objective evolutionary algorithms usually use linear prediction models to generate the initial population in the new environment, and some nonlinear prediction models often have high computational cost. Therefore, it is difficult to quickly and accurately respond to nonlinear environmental changes. This paper presents a dynamic multi-objective evolutionary algorithm based on online prediction of self-evolving fuzzy system (SEFS). In this algorithm, the decomposition based multi-objective evolutionary algorithm (MOEA/D) acts as the static optimizer. When the environment changes, individuals are first put into an associate set of their corresponding weight vectors. Then, the time series of each variable is constructed based on the associate set, and the SEFS online prediction model is established. Finally, an environmental response strategy based on SEFS is designed to quickly generate an initial population with high performance in the new environment. The proposed algorithm is compared with seven state-of-the-art dynamic multi-objective evolutionary algorithms on 20 benchmark functions. Experimental results show that the proposed algorithm can quickly and accurately respond to nonlinear environmental changes, and has competitiveness. The changes of dynamic multi-objective optimization problems in decision space are usually nonlinear. However, the previous dynamic multi-objective evolutionary algorithms usually use linear prediction models to generate the initial population in the new environment, and some nonlinear prediction models often have high computational cost. Therefore, it is difficult to quickly and accurately respond to nonlinear environmental changes. This paper presents a dynamic multi-objective evolutionary algorithm based on online prediction of self-evolving fuzzy system (SEFS). In this algorithm, the decomposition based multi-objective evolutionary algorithm (MOEA/D) acts as the static optimizer. When the environment changes, individuals are first put into an associate set of their corresponding weight vectors. Then, the time series of each variable is constructed based on the associate set, and the SEFS online prediction model is established. Finally, an environmental response strategy based on SEFS is designed to quickly generate an initial population with high performance in the new environment. The proposed algorithm is compared with seven state-of-the-art dynamic multi-objective evolutionary algorithms on 20 benchmark functions. Experimental results show that the proposed algorithm can quickly and accurately respond to nonlinear environmental changes, and has competitiveness. Dynamic multi-objective optimization Elsevier Time series Elsevier Weight vectors Elsevier Self-evolving fuzzy system Elsevier Gan, Xingjia oth Gong, Dunwei oth Tang, Xiaoke oth Dai, Hongwei oth Zhong, Zhaoman oth Enthalten in Elsevier Science Inc Petrruzziello, Carmelina ELSEVIER Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study 2013 an international journal New York, NY (DE-627)ELV011843691 volume:612 year:2022 pages:638-654 extent:17 https://doi.org/10.1016/j.ins.2022.08.072 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 612 2022 638-654 17 |
| spelling |
10.1016/j.ins.2022.08.072 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001960.pica (DE-627)ELV059467088 (ELSEVIER)S0020-0255(22)00982-3 DE-627 ger DE-627 rakwb eng 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Sun, Jing verfasserin aut A self-evolving fuzzy system online prediction-based dynamic multi-objective evolutionary algorithm 2022transfer abstract 17 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The changes of dynamic multi-objective optimization problems in decision space are usually nonlinear. However, the previous dynamic multi-objective evolutionary algorithms usually use linear prediction models to generate the initial population in the new environment, and some nonlinear prediction models often have high computational cost. Therefore, it is difficult to quickly and accurately respond to nonlinear environmental changes. This paper presents a dynamic multi-objective evolutionary algorithm based on online prediction of self-evolving fuzzy system (SEFS). In this algorithm, the decomposition based multi-objective evolutionary algorithm (MOEA/D) acts as the static optimizer. When the environment changes, individuals are first put into an associate set of their corresponding weight vectors. Then, the time series of each variable is constructed based on the associate set, and the SEFS online prediction model is established. Finally, an environmental response strategy based on SEFS is designed to quickly generate an initial population with high performance in the new environment. The proposed algorithm is compared with seven state-of-the-art dynamic multi-objective evolutionary algorithms on 20 benchmark functions. Experimental results show that the proposed algorithm can quickly and accurately respond to nonlinear environmental changes, and has competitiveness. The changes of dynamic multi-objective optimization problems in decision space are usually nonlinear. However, the previous dynamic multi-objective evolutionary algorithms usually use linear prediction models to generate the initial population in the new environment, and some nonlinear prediction models often have high computational cost. Therefore, it is difficult to quickly and accurately respond to nonlinear environmental changes. This paper presents a dynamic multi-objective evolutionary algorithm based on online prediction of self-evolving fuzzy system (SEFS). In this algorithm, the decomposition based multi-objective evolutionary algorithm (MOEA/D) acts as the static optimizer. When the environment changes, individuals are first put into an associate set of their corresponding weight vectors. Then, the time series of each variable is constructed based on the associate set, and the SEFS online prediction model is established. Finally, an environmental response strategy based on SEFS is designed to quickly generate an initial population with high performance in the new environment. The proposed algorithm is compared with seven state-of-the-art dynamic multi-objective evolutionary algorithms on 20 benchmark functions. Experimental results show that the proposed algorithm can quickly and accurately respond to nonlinear environmental changes, and has competitiveness. Dynamic multi-objective optimization Elsevier Time series Elsevier Weight vectors Elsevier Self-evolving fuzzy system Elsevier Gan, Xingjia oth Gong, Dunwei oth Tang, Xiaoke oth Dai, Hongwei oth Zhong, Zhaoman oth Enthalten in Elsevier Science Inc Petrruzziello, Carmelina ELSEVIER Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study 2013 an international journal New York, NY (DE-627)ELV011843691 volume:612 year:2022 pages:638-654 extent:17 https://doi.org/10.1016/j.ins.2022.08.072 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 612 2022 638-654 17 |
| allfields_unstemmed |
10.1016/j.ins.2022.08.072 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001960.pica (DE-627)ELV059467088 (ELSEVIER)S0020-0255(22)00982-3 DE-627 ger DE-627 rakwb eng 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Sun, Jing verfasserin aut A self-evolving fuzzy system online prediction-based dynamic multi-objective evolutionary algorithm 2022transfer abstract 17 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The changes of dynamic multi-objective optimization problems in decision space are usually nonlinear. However, the previous dynamic multi-objective evolutionary algorithms usually use linear prediction models to generate the initial population in the new environment, and some nonlinear prediction models often have high computational cost. Therefore, it is difficult to quickly and accurately respond to nonlinear environmental changes. This paper presents a dynamic multi-objective evolutionary algorithm based on online prediction of self-evolving fuzzy system (SEFS). In this algorithm, the decomposition based multi-objective evolutionary algorithm (MOEA/D) acts as the static optimizer. When the environment changes, individuals are first put into an associate set of their corresponding weight vectors. Then, the time series of each variable is constructed based on the associate set, and the SEFS online prediction model is established. Finally, an environmental response strategy based on SEFS is designed to quickly generate an initial population with high performance in the new environment. The proposed algorithm is compared with seven state-of-the-art dynamic multi-objective evolutionary algorithms on 20 benchmark functions. Experimental results show that the proposed algorithm can quickly and accurately respond to nonlinear environmental changes, and has competitiveness. The changes of dynamic multi-objective optimization problems in decision space are usually nonlinear. However, the previous dynamic multi-objective evolutionary algorithms usually use linear prediction models to generate the initial population in the new environment, and some nonlinear prediction models often have high computational cost. Therefore, it is difficult to quickly and accurately respond to nonlinear environmental changes. This paper presents a dynamic multi-objective evolutionary algorithm based on online prediction of self-evolving fuzzy system (SEFS). In this algorithm, the decomposition based multi-objective evolutionary algorithm (MOEA/D) acts as the static optimizer. When the environment changes, individuals are first put into an associate set of their corresponding weight vectors. Then, the time series of each variable is constructed based on the associate set, and the SEFS online prediction model is established. Finally, an environmental response strategy based on SEFS is designed to quickly generate an initial population with high performance in the new environment. The proposed algorithm is compared with seven state-of-the-art dynamic multi-objective evolutionary algorithms on 20 benchmark functions. Experimental results show that the proposed algorithm can quickly and accurately respond to nonlinear environmental changes, and has competitiveness. Dynamic multi-objective optimization Elsevier Time series Elsevier Weight vectors Elsevier Self-evolving fuzzy system Elsevier Gan, Xingjia oth Gong, Dunwei oth Tang, Xiaoke oth Dai, Hongwei oth Zhong, Zhaoman oth Enthalten in Elsevier Science Inc Petrruzziello, Carmelina ELSEVIER Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study 2013 an international journal New York, NY (DE-627)ELV011843691 volume:612 year:2022 pages:638-654 extent:17 https://doi.org/10.1016/j.ins.2022.08.072 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 612 2022 638-654 17 |
| allfieldsGer |
10.1016/j.ins.2022.08.072 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001960.pica (DE-627)ELV059467088 (ELSEVIER)S0020-0255(22)00982-3 DE-627 ger DE-627 rakwb eng 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Sun, Jing verfasserin aut A self-evolving fuzzy system online prediction-based dynamic multi-objective evolutionary algorithm 2022transfer abstract 17 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The changes of dynamic multi-objective optimization problems in decision space are usually nonlinear. However, the previous dynamic multi-objective evolutionary algorithms usually use linear prediction models to generate the initial population in the new environment, and some nonlinear prediction models often have high computational cost. Therefore, it is difficult to quickly and accurately respond to nonlinear environmental changes. This paper presents a dynamic multi-objective evolutionary algorithm based on online prediction of self-evolving fuzzy system (SEFS). In this algorithm, the decomposition based multi-objective evolutionary algorithm (MOEA/D) acts as the static optimizer. When the environment changes, individuals are first put into an associate set of their corresponding weight vectors. Then, the time series of each variable is constructed based on the associate set, and the SEFS online prediction model is established. Finally, an environmental response strategy based on SEFS is designed to quickly generate an initial population with high performance in the new environment. The proposed algorithm is compared with seven state-of-the-art dynamic multi-objective evolutionary algorithms on 20 benchmark functions. Experimental results show that the proposed algorithm can quickly and accurately respond to nonlinear environmental changes, and has competitiveness. The changes of dynamic multi-objective optimization problems in decision space are usually nonlinear. However, the previous dynamic multi-objective evolutionary algorithms usually use linear prediction models to generate the initial population in the new environment, and some nonlinear prediction models often have high computational cost. Therefore, it is difficult to quickly and accurately respond to nonlinear environmental changes. This paper presents a dynamic multi-objective evolutionary algorithm based on online prediction of self-evolving fuzzy system (SEFS). In this algorithm, the decomposition based multi-objective evolutionary algorithm (MOEA/D) acts as the static optimizer. When the environment changes, individuals are first put into an associate set of their corresponding weight vectors. Then, the time series of each variable is constructed based on the associate set, and the SEFS online prediction model is established. Finally, an environmental response strategy based on SEFS is designed to quickly generate an initial population with high performance in the new environment. The proposed algorithm is compared with seven state-of-the-art dynamic multi-objective evolutionary algorithms on 20 benchmark functions. Experimental results show that the proposed algorithm can quickly and accurately respond to nonlinear environmental changes, and has competitiveness. Dynamic multi-objective optimization Elsevier Time series Elsevier Weight vectors Elsevier Self-evolving fuzzy system Elsevier Gan, Xingjia oth Gong, Dunwei oth Tang, Xiaoke oth Dai, Hongwei oth Zhong, Zhaoman oth Enthalten in Elsevier Science Inc Petrruzziello, Carmelina ELSEVIER Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study 2013 an international journal New York, NY (DE-627)ELV011843691 volume:612 year:2022 pages:638-654 extent:17 https://doi.org/10.1016/j.ins.2022.08.072 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 612 2022 638-654 17 |
| allfieldsSound |
10.1016/j.ins.2022.08.072 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001960.pica (DE-627)ELV059467088 (ELSEVIER)S0020-0255(22)00982-3 DE-627 ger DE-627 rakwb eng 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Sun, Jing verfasserin aut A self-evolving fuzzy system online prediction-based dynamic multi-objective evolutionary algorithm 2022transfer abstract 17 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The changes of dynamic multi-objective optimization problems in decision space are usually nonlinear. However, the previous dynamic multi-objective evolutionary algorithms usually use linear prediction models to generate the initial population in the new environment, and some nonlinear prediction models often have high computational cost. Therefore, it is difficult to quickly and accurately respond to nonlinear environmental changes. This paper presents a dynamic multi-objective evolutionary algorithm based on online prediction of self-evolving fuzzy system (SEFS). In this algorithm, the decomposition based multi-objective evolutionary algorithm (MOEA/D) acts as the static optimizer. When the environment changes, individuals are first put into an associate set of their corresponding weight vectors. Then, the time series of each variable is constructed based on the associate set, and the SEFS online prediction model is established. Finally, an environmental response strategy based on SEFS is designed to quickly generate an initial population with high performance in the new environment. The proposed algorithm is compared with seven state-of-the-art dynamic multi-objective evolutionary algorithms on 20 benchmark functions. Experimental results show that the proposed algorithm can quickly and accurately respond to nonlinear environmental changes, and has competitiveness. The changes of dynamic multi-objective optimization problems in decision space are usually nonlinear. However, the previous dynamic multi-objective evolutionary algorithms usually use linear prediction models to generate the initial population in the new environment, and some nonlinear prediction models often have high computational cost. Therefore, it is difficult to quickly and accurately respond to nonlinear environmental changes. This paper presents a dynamic multi-objective evolutionary algorithm based on online prediction of self-evolving fuzzy system (SEFS). In this algorithm, the decomposition based multi-objective evolutionary algorithm (MOEA/D) acts as the static optimizer. When the environment changes, individuals are first put into an associate set of their corresponding weight vectors. Then, the time series of each variable is constructed based on the associate set, and the SEFS online prediction model is established. Finally, an environmental response strategy based on SEFS is designed to quickly generate an initial population with high performance in the new environment. The proposed algorithm is compared with seven state-of-the-art dynamic multi-objective evolutionary algorithms on 20 benchmark functions. Experimental results show that the proposed algorithm can quickly and accurately respond to nonlinear environmental changes, and has competitiveness. Dynamic multi-objective optimization Elsevier Time series Elsevier Weight vectors Elsevier Self-evolving fuzzy system Elsevier Gan, Xingjia oth Gong, Dunwei oth Tang, Xiaoke oth Dai, Hongwei oth Zhong, Zhaoman oth Enthalten in Elsevier Science Inc Petrruzziello, Carmelina ELSEVIER Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study 2013 an international journal New York, NY (DE-627)ELV011843691 volume:612 year:2022 pages:638-654 extent:17 https://doi.org/10.1016/j.ins.2022.08.072 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 612 2022 638-654 17 |
| language |
English |
| source |
Enthalten in Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study New York, NY volume:612 year:2022 pages:638-654 extent:17 |
| sourceStr |
Enthalten in Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study New York, NY volume:612 year:2022 pages:638-654 extent:17 |
| format_phy_str_mv |
Article |
| bklname |
Biochemie: Allgemeines Biophysik Zellbiologie |
| institution |
findex.gbv.de |
| topic_facet |
Dynamic multi-objective optimization Time series Weight vectors Self-evolving fuzzy system |
| dewey-raw |
610 |
| isfreeaccess_bool |
false |
| container_title |
Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study |
| authorswithroles_txt_mv |
Sun, Jing @@aut@@ Gan, Xingjia @@oth@@ Gong, Dunwei @@oth@@ Tang, Xiaoke @@oth@@ Dai, Hongwei @@oth@@ Zhong, Zhaoman @@oth@@ |
| publishDateDaySort_date |
2022-01-01T00:00:00Z |
| hierarchy_top_id |
ELV011843691 |
| dewey-sort |
3610 |
| id |
ELV059467088 |
| 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">ELV059467088</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626052942.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221219s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.ins.2022.08.072</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001960.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV059467088</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0020-0255(22)00982-3</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">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.70</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.12</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.15</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Sun, Jing</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A self-evolving fuzzy system online prediction-based dynamic multi-objective evolutionary algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">17</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">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The changes of dynamic multi-objective optimization problems in decision space are usually nonlinear. However, the previous dynamic multi-objective evolutionary algorithms usually use linear prediction models to generate the initial population in the new environment, and some nonlinear prediction models often have high computational cost. Therefore, it is difficult to quickly and accurately respond to nonlinear environmental changes. This paper presents a dynamic multi-objective evolutionary algorithm based on online prediction of self-evolving fuzzy system (SEFS). In this algorithm, the decomposition based multi-objective evolutionary algorithm (MOEA/D) acts as the static optimizer. When the environment changes, individuals are first put into an associate set of their corresponding weight vectors. Then, the time series of each variable is constructed based on the associate set, and the SEFS online prediction model is established. Finally, an environmental response strategy based on SEFS is designed to quickly generate an initial population with high performance in the new environment. The proposed algorithm is compared with seven state-of-the-art dynamic multi-objective evolutionary algorithms on 20 benchmark functions. Experimental results show that the proposed algorithm can quickly and accurately respond to nonlinear environmental changes, and has competitiveness.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The changes of dynamic multi-objective optimization problems in decision space are usually nonlinear. However, the previous dynamic multi-objective evolutionary algorithms usually use linear prediction models to generate the initial population in the new environment, and some nonlinear prediction models often have high computational cost. Therefore, it is difficult to quickly and accurately respond to nonlinear environmental changes. This paper presents a dynamic multi-objective evolutionary algorithm based on online prediction of self-evolving fuzzy system (SEFS). In this algorithm, the decomposition based multi-objective evolutionary algorithm (MOEA/D) acts as the static optimizer. When the environment changes, individuals are first put into an associate set of their corresponding weight vectors. Then, the time series of each variable is constructed based on the associate set, and the SEFS online prediction model is established. Finally, an environmental response strategy based on SEFS is designed to quickly generate an initial population with high performance in the new environment. The proposed algorithm is compared with seven state-of-the-art dynamic multi-objective evolutionary algorithms on 20 benchmark functions. Experimental results show that the proposed algorithm can quickly and accurately respond to nonlinear environmental changes, and has competitiveness.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Dynamic multi-objective optimization</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Time series</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Weight vectors</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Self-evolving fuzzy system</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gan, Xingjia</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gong, Dunwei</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tang, Xiaoke</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dai, Hongwei</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhong, Zhaoman</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science Inc</subfield><subfield code="a">Petrruzziello, Carmelina ELSEVIER</subfield><subfield code="t">Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study</subfield><subfield code="d">2013</subfield><subfield code="d">an international journal</subfield><subfield code="g">New York, NY</subfield><subfield code="w">(DE-627)ELV011843691</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:612</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:638-654</subfield><subfield code="g">extent:17</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.ins.2022.08.072</subfield><subfield code="3">Volltext</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">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.70</subfield><subfield code="j">Biochemie: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.12</subfield><subfield code="j">Biophysik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.15</subfield><subfield code="j">Zellbiologie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">612</subfield><subfield code="j">2022</subfield><subfield code="h">638-654</subfield><subfield code="g">17</subfield></datafield></record></collection>
|
| author |
Sun, Jing |
| spellingShingle |
Sun, Jing ddc 610 ddc 570 fid BIODIV bkl 35.70 bkl 42.12 bkl 42.15 Elsevier Dynamic multi-objective optimization Elsevier Time series Elsevier Weight vectors Elsevier Self-evolving fuzzy system A self-evolving fuzzy system online prediction-based dynamic multi-objective evolutionary algorithm |
| authorStr |
Sun, Jing |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV011843691 |
| format |
electronic Article |
| dewey-ones |
610 - Medicine & health 570 - Life sciences; biology |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl A self-evolving fuzzy system online prediction-based dynamic multi-objective evolutionary algorithm Dynamic multi-objective optimization Elsevier Time series Elsevier Weight vectors Elsevier Self-evolving fuzzy system Elsevier |
| topic |
ddc 610 ddc 570 fid BIODIV bkl 35.70 bkl 42.12 bkl 42.15 Elsevier Dynamic multi-objective optimization Elsevier Time series Elsevier Weight vectors Elsevier Self-evolving fuzzy system |
| topic_unstemmed |
ddc 610 ddc 570 fid BIODIV bkl 35.70 bkl 42.12 bkl 42.15 Elsevier Dynamic multi-objective optimization Elsevier Time series Elsevier Weight vectors Elsevier Self-evolving fuzzy system |
| topic_browse |
ddc 610 ddc 570 fid BIODIV bkl 35.70 bkl 42.12 bkl 42.15 Elsevier Dynamic multi-objective optimization Elsevier Time series Elsevier Weight vectors Elsevier Self-evolving fuzzy system |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
x g xg d g dg x t xt h d hd z z zz |
| hierarchy_parent_title |
Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study |
| hierarchy_parent_id |
ELV011843691 |
| dewey-tens |
610 - Medicine & health 570 - Life sciences; biology |
| hierarchy_top_title |
Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV011843691 |
| title |
A self-evolving fuzzy system online prediction-based dynamic multi-objective evolutionary algorithm |
| ctrlnum |
(DE-627)ELV059467088 (ELSEVIER)S0020-0255(22)00982-3 |
| title_full |
A self-evolving fuzzy system online prediction-based dynamic multi-objective evolutionary algorithm |
| author_sort |
Sun, Jing |
| journal |
Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study |
| journalStr |
Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology 500 - Science |
| recordtype |
marc |
| publishDateSort |
2022 |
| contenttype_str_mv |
zzz |
| container_start_page |
638 |
| author_browse |
Sun, Jing |
| container_volume |
612 |
| physical |
17 |
| class |
610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Sun, Jing |
| doi_str_mv |
10.1016/j.ins.2022.08.072 |
| dewey-full |
610 570 |
| title_sort |
a self-evolving fuzzy system online prediction-based dynamic multi-objective evolutionary algorithm |
| title_auth |
A self-evolving fuzzy system online prediction-based dynamic multi-objective evolutionary algorithm |
| abstract |
The changes of dynamic multi-objective optimization problems in decision space are usually nonlinear. However, the previous dynamic multi-objective evolutionary algorithms usually use linear prediction models to generate the initial population in the new environment, and some nonlinear prediction models often have high computational cost. Therefore, it is difficult to quickly and accurately respond to nonlinear environmental changes. This paper presents a dynamic multi-objective evolutionary algorithm based on online prediction of self-evolving fuzzy system (SEFS). In this algorithm, the decomposition based multi-objective evolutionary algorithm (MOEA/D) acts as the static optimizer. When the environment changes, individuals are first put into an associate set of their corresponding weight vectors. Then, the time series of each variable is constructed based on the associate set, and the SEFS online prediction model is established. Finally, an environmental response strategy based on SEFS is designed to quickly generate an initial population with high performance in the new environment. The proposed algorithm is compared with seven state-of-the-art dynamic multi-objective evolutionary algorithms on 20 benchmark functions. Experimental results show that the proposed algorithm can quickly and accurately respond to nonlinear environmental changes, and has competitiveness. |
| abstractGer |
The changes of dynamic multi-objective optimization problems in decision space are usually nonlinear. However, the previous dynamic multi-objective evolutionary algorithms usually use linear prediction models to generate the initial population in the new environment, and some nonlinear prediction models often have high computational cost. Therefore, it is difficult to quickly and accurately respond to nonlinear environmental changes. This paper presents a dynamic multi-objective evolutionary algorithm based on online prediction of self-evolving fuzzy system (SEFS). In this algorithm, the decomposition based multi-objective evolutionary algorithm (MOEA/D) acts as the static optimizer. When the environment changes, individuals are first put into an associate set of their corresponding weight vectors. Then, the time series of each variable is constructed based on the associate set, and the SEFS online prediction model is established. Finally, an environmental response strategy based on SEFS is designed to quickly generate an initial population with high performance in the new environment. The proposed algorithm is compared with seven state-of-the-art dynamic multi-objective evolutionary algorithms on 20 benchmark functions. Experimental results show that the proposed algorithm can quickly and accurately respond to nonlinear environmental changes, and has competitiveness. |
| abstract_unstemmed |
The changes of dynamic multi-objective optimization problems in decision space are usually nonlinear. However, the previous dynamic multi-objective evolutionary algorithms usually use linear prediction models to generate the initial population in the new environment, and some nonlinear prediction models often have high computational cost. Therefore, it is difficult to quickly and accurately respond to nonlinear environmental changes. This paper presents a dynamic multi-objective evolutionary algorithm based on online prediction of self-evolving fuzzy system (SEFS). In this algorithm, the decomposition based multi-objective evolutionary algorithm (MOEA/D) acts as the static optimizer. When the environment changes, individuals are first put into an associate set of their corresponding weight vectors. Then, the time series of each variable is constructed based on the associate set, and the SEFS online prediction model is established. Finally, an environmental response strategy based on SEFS is designed to quickly generate an initial population with high performance in the new environment. The proposed algorithm is compared with seven state-of-the-art dynamic multi-objective evolutionary algorithms on 20 benchmark functions. Experimental results show that the proposed algorithm can quickly and accurately respond to nonlinear environmental changes, and has competitiveness. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA |
| title_short |
A self-evolving fuzzy system online prediction-based dynamic multi-objective evolutionary algorithm |
| url |
https://doi.org/10.1016/j.ins.2022.08.072 |
| remote_bool |
true |
| author2 |
Gan, Xingjia Gong, Dunwei Tang, Xiaoke Dai, Hongwei Zhong, Zhaoman |
| author2Str |
Gan, Xingjia Gong, Dunwei Tang, Xiaoke Dai, Hongwei Zhong, Zhaoman |
| ppnlink |
ELV011843691 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth oth oth |
| doi_str |
10.1016/j.ins.2022.08.072 |
| up_date |
2024-07-06T22:05:19.736Z |
| _version_ |
1803868983511744512 |
| 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">ELV059467088</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626052942.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221219s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.ins.2022.08.072</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001960.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV059467088</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0020-0255(22)00982-3</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">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.70</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.12</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.15</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Sun, Jing</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A self-evolving fuzzy system online prediction-based dynamic multi-objective evolutionary algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">17</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">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The changes of dynamic multi-objective optimization problems in decision space are usually nonlinear. However, the previous dynamic multi-objective evolutionary algorithms usually use linear prediction models to generate the initial population in the new environment, and some nonlinear prediction models often have high computational cost. Therefore, it is difficult to quickly and accurately respond to nonlinear environmental changes. This paper presents a dynamic multi-objective evolutionary algorithm based on online prediction of self-evolving fuzzy system (SEFS). In this algorithm, the decomposition based multi-objective evolutionary algorithm (MOEA/D) acts as the static optimizer. When the environment changes, individuals are first put into an associate set of their corresponding weight vectors. Then, the time series of each variable is constructed based on the associate set, and the SEFS online prediction model is established. Finally, an environmental response strategy based on SEFS is designed to quickly generate an initial population with high performance in the new environment. The proposed algorithm is compared with seven state-of-the-art dynamic multi-objective evolutionary algorithms on 20 benchmark functions. Experimental results show that the proposed algorithm can quickly and accurately respond to nonlinear environmental changes, and has competitiveness.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The changes of dynamic multi-objective optimization problems in decision space are usually nonlinear. However, the previous dynamic multi-objective evolutionary algorithms usually use linear prediction models to generate the initial population in the new environment, and some nonlinear prediction models often have high computational cost. Therefore, it is difficult to quickly and accurately respond to nonlinear environmental changes. This paper presents a dynamic multi-objective evolutionary algorithm based on online prediction of self-evolving fuzzy system (SEFS). In this algorithm, the decomposition based multi-objective evolutionary algorithm (MOEA/D) acts as the static optimizer. When the environment changes, individuals are first put into an associate set of their corresponding weight vectors. Then, the time series of each variable is constructed based on the associate set, and the SEFS online prediction model is established. Finally, an environmental response strategy based on SEFS is designed to quickly generate an initial population with high performance in the new environment. The proposed algorithm is compared with seven state-of-the-art dynamic multi-objective evolutionary algorithms on 20 benchmark functions. Experimental results show that the proposed algorithm can quickly and accurately respond to nonlinear environmental changes, and has competitiveness.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Dynamic multi-objective optimization</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Time series</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Weight vectors</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Self-evolving fuzzy system</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gan, Xingjia</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gong, Dunwei</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tang, Xiaoke</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dai, Hongwei</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhong, Zhaoman</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science Inc</subfield><subfield code="a">Petrruzziello, Carmelina ELSEVIER</subfield><subfield code="t">Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study</subfield><subfield code="d">2013</subfield><subfield code="d">an international journal</subfield><subfield code="g">New York, NY</subfield><subfield code="w">(DE-627)ELV011843691</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:612</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:638-654</subfield><subfield code="g">extent:17</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.ins.2022.08.072</subfield><subfield code="3">Volltext</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">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.70</subfield><subfield code="j">Biochemie: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.12</subfield><subfield code="j">Biophysik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.15</subfield><subfield code="j">Zellbiologie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">612</subfield><subfield code="j">2022</subfield><subfield code="h">638-654</subfield><subfield code="g">17</subfield></datafield></record></collection>
|
| score |
7.3997936 |