Multi-objective differential evolution algorithm with data-driven selection strategy; [数据驱动选择策略的多目标差分进化算法] - Details

Author：

Hou, Y. (Hou, Y..) | Wu, Y.-L. (Wu, Y.-L..) | Bai, X. (Bai, X..) | Han, H.-G. (Han, H.-G..)

Indexed by：

EI Scopus

Abstract：

The　multi-objective　differential　evolution　(MODE)　algorithm　has　high　computational　complexity　of　the　selection　strategy　in　solving　complex　multi-objective　optimization　problems.　To　address　this　issue,　a　multi-objective　differential　evolution　with　data-driven　selection　strategy　(MODE-DDSS)　is　proposed.　First,　the　ranking　evaluation　criteria　of　optimization　solutions　is　designed,　and　the　ranking　evaluation　database　of　optimization　solutions　based　on　evaluation　criteria　is　established.　Then,　a　data-driven　selection　strategy,　based　on　a　two-way　search　mechanism　and　a　non-repeated　comparison　mechanism,　is　designed　to　search　and　compare　the　optimal　solutions　efficiently,　and　select　the　optimal　solutions.　Finally,　a　multi-objective　differential　evolution　algorithm　with　the　data-driven　selection　strategy　is　constructed,　which　reduces　the　complexity　of　optimal　solution　selection　operation　and　improves　the　optimization　efficiency　of　the　algorithm.　Experimental　results　show　that　the　proposed　MODE-DDSS　algorithm　can　effectively　reduce　the　number　of　comparison　operations　in　the　selection　strategy,　and　improve　the　efficiency　of　the　multi-objective　differential　evolution　algorithm　in　solving　complex　multi-objective　optimization　problems.　©　2023　Northeast　University.　All　rights　reserved.

Keyword：

data-driven optimization efficiency non-dominated sorting multi-objective optimization selection strategy differential evolution algorithm

Author Community：

[ 1 ] [Hou Y.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 2 ] [Hou Y.]Engineering Research Center of Digital Community of Ministry of Education, Beijing University of Technology, Beijing, 100124, China
[ 3 ] [Wu Y.-L.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 4 ] [Wu Y.-L.]Engineering Research Center of Digital Community of Ministry of Education, Beijing University of Technology, Beijing, 100124, China
[ 5 ] [Bai X.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 6 ] [Bai X.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
[ 7 ] [Han H.-G.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 8 ] [Han H.-G.]Engineering Research Center of Digital Community of Ministry of Education, Beijing University of Technology, Beijing, 100124, China
[ 9 ] [Han H.-G.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China

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Source ：

Control and Decision

ISSN： 1001-0920

Year： 2023

Issue： 7

Volume： 38

Page： 1816-1824

Cited Count：

WoS CC Cited Count： 0

SCOPUS Cited Count：

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 13

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