State-of-the-Art Differential Evolution Algorithms Selection and Modifications for Difficult Functions

Differential evolution (DE) is powerful for global optimization problems and constantly improved. However, satisfactory solutions of some functions can be hardly obtained so far. According to the experimental data of many state-of-the-art DE algorithms from the literature and our pre-experiment, sol...
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Autor*in:	Zhe Chen [verfasserIn] Chengjun Li [verfasserIn] Zukai Tang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Differential evolution function JADE CoBiDE modifications

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 6(2018), Seite 76586-76595
Übergeordnetes Werk:	volume:6 ; year:2018 ; pages:76586-76595

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2018.2882528

Katalog-ID:	DOAJ007142595

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520			\|a Differential evolution (DE) is powerful for global optimization problems and constantly improved. However, satisfactory solutions of some functions can be hardly obtained so far. According to the experimental data of many state-of-the-art DE algorithms from the literature and our pre-experiment, solutions for F12 among the 25 CEC 2005 benchmark functions have an outstanding large mean error to the optimal value, while solutions for F15, F21, and F23-F24 all fall into one or several values. It can be seen that, in the involved state-of-the-art DE algorithms, JADE obtains the best solutions for F15, while EDEV obtains the best solutions for F12. In this paper, we modify the two DE algorithms for the two functions, respectively. Experimental results show that our modifications leads to significant improvement on solutions. As a result, solutions for these two functions are improved to an unprecedented degree.
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allfieldsSound	10.1109/ACCESS.2018.2882528 doi (DE-627)DOAJ007142595 (DE-599)DOAJ359f87a740274848bda03a2bc6f313b3 DE-627 ger DE-627 rakwb eng TK1-9971 Zhe Chen verfasserin aut State-of-the-Art Differential Evolution Algorithms Selection and Modifications for Difficult Functions 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Differential evolution (DE) is powerful for global optimization problems and constantly improved. However, satisfactory solutions of some functions can be hardly obtained so far. According to the experimental data of many state-of-the-art DE algorithms from the literature and our pre-experiment, solutions for F12 among the 25 CEC 2005 benchmark functions have an outstanding large mean error to the optimal value, while solutions for F15, F21, and F23-F24 all fall into one or several values. It can be seen that, in the involved state-of-the-art DE algorithms, JADE obtains the best solutions for F15, while EDEV obtains the best solutions for F12. In this paper, we modify the two DE algorithms for the two functions, respectively. Experimental results show that our modifications leads to significant improvement on solutions. As a result, solutions for these two functions are improved to an unprecedented degree. Differential evolution function JADE CoBiDE modifications Electrical engineering. Electronics. Nuclear engineering Chengjun Li verfasserin aut Zukai Tang verfasserin aut In IEEE Access IEEE, 2014 6(2018), Seite 76586-76595 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:6 year:2018 pages:76586-76595 https://doi.org/10.1109/ACCESS.2018.2882528 kostenfrei https://doaj.org/article/359f87a740274848bda03a2bc6f313b3 kostenfrei https://ieeexplore.ieee.org/document/8542738/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 76586-76595
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source	In IEEE Access 6(2018), Seite 76586-76595 volume:6 year:2018 pages:76586-76595
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container_title	IEEE Access
authorswithroles_txt_mv	Zhe Chen @@aut@@ Chengjun Li @@aut@@ Zukai Tang @@aut@@
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callnumber-first	T - Technology
author	Zhe Chen
spellingShingle	Zhe Chen misc TK1-9971 misc Differential evolution misc function misc JADE misc CoBiDE misc modifications misc Electrical engineering. Electronics. Nuclear engineering State-of-the-Art Differential Evolution Algorithms Selection and Modifications for Difficult Functions
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topic_title	TK1-9971 State-of-the-Art Differential Evolution Algorithms Selection and Modifications for Difficult Functions Differential evolution function JADE CoBiDE modifications
topic	misc TK1-9971 misc Differential evolution misc function misc JADE misc CoBiDE misc modifications misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Differential evolution misc function misc JADE misc CoBiDE misc modifications misc Electrical engineering. Electronics. Nuclear engineering
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title	State-of-the-Art Differential Evolution Algorithms Selection and Modifications for Difficult Functions
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title_sort	state-of-the-art differential evolution algorithms selection and modifications for difficult functions
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title_auth	State-of-the-Art Differential Evolution Algorithms Selection and Modifications for Difficult Functions
abstract	Differential evolution (DE) is powerful for global optimization problems and constantly improved. However, satisfactory solutions of some functions can be hardly obtained so far. According to the experimental data of many state-of-the-art DE algorithms from the literature and our pre-experiment, solutions for F12 among the 25 CEC 2005 benchmark functions have an outstanding large mean error to the optimal value, while solutions for F15, F21, and F23-F24 all fall into one or several values. It can be seen that, in the involved state-of-the-art DE algorithms, JADE obtains the best solutions for F15, while EDEV obtains the best solutions for F12. In this paper, we modify the two DE algorithms for the two functions, respectively. Experimental results show that our modifications leads to significant improvement on solutions. As a result, solutions for these two functions are improved to an unprecedented degree.
abstractGer	Differential evolution (DE) is powerful for global optimization problems and constantly improved. However, satisfactory solutions of some functions can be hardly obtained so far. According to the experimental data of many state-of-the-art DE algorithms from the literature and our pre-experiment, solutions for F12 among the 25 CEC 2005 benchmark functions have an outstanding large mean error to the optimal value, while solutions for F15, F21, and F23-F24 all fall into one or several values. It can be seen that, in the involved state-of-the-art DE algorithms, JADE obtains the best solutions for F15, while EDEV obtains the best solutions for F12. In this paper, we modify the two DE algorithms for the two functions, respectively. Experimental results show that our modifications leads to significant improvement on solutions. As a result, solutions for these two functions are improved to an unprecedented degree.
abstract_unstemmed	Differential evolution (DE) is powerful for global optimization problems and constantly improved. However, satisfactory solutions of some functions can be hardly obtained so far. According to the experimental data of many state-of-the-art DE algorithms from the literature and our pre-experiment, solutions for F12 among the 25 CEC 2005 benchmark functions have an outstanding large mean error to the optimal value, while solutions for F15, F21, and F23-F24 all fall into one or several values. It can be seen that, in the involved state-of-the-art DE algorithms, JADE obtains the best solutions for F15, while EDEV obtains the best solutions for F12. In this paper, we modify the two DE algorithms for the two functions, respectively. Experimental results show that our modifications leads to significant improvement on solutions. As a result, solutions for these two functions are improved to an unprecedented degree.
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title_short	State-of-the-Art Differential Evolution Algorithms Selection and Modifications for Difficult Functions
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State-of-the-Art Differential Evolution Algorithms Selection and Modifications for Difficult Functions

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Zugang & Verfügbarkeit

Vorhandene Bände

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