A Coupling Approach With GSO-BFOA for Many-Objective Optimization

被引：5

作者：

Zhang, Jiangjiang ^{[1
]}

Cui, Zhihua ^{[1
]}

Wang, Yechuang ^{[1
]}

Wang, Hui ^{[2
]}

Cai, Xingjuan ^{[1
]}

Chen, Jinjun ^{[3
]}

Li, Wuzhao ^{[4
]}

机构：

[1] Taiyuan Univ Sci & Technol, Complex Syst & Computat Intelligence Lab, Taiyuan 030024, Shanxi, Peoples R China

[2] Nanchang Inst Technol, Sch Informat Engn, Nanchang 330099, Jiangxi, Peoples R China

[3] Swinburne Univ Technol, Dept Comp Sci & Software Engn, Melbourne, Vic 3000, Australia

[4] Jiaxing Vocat Tech Coll, Jiaxing 314001, Peoples R China

来源：

IEEE ACCESS | 2019年 / 7卷

基金：

中国国家自然科学基金;

关键词：

Swarm intelligence; glowworm swarm optimization; bacterial foraging optimization algorithm; external archive; many-objective optimization; GLOWWORM SWARM OPTIMIZATION; CUCKOO SEARCH ALGORITHM; EVOLUTIONARY ALGORITHM; BAT ALGORITHM; PERFORMANCE; SYSTEM; SELECTION;

D O I：

10.1109/ACCESS.2019.2937538

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Glowworm swarm optimization (GSO) and bacterial foraging optimization algorithm (BFOA) are two popular swarm intelligence optimization algorithms (SIOAs). However, both GSO and BFOA show some difficulties when solving many-objective optimization problems (MaOPs). To challenge MaOPs, a coupling approach based on GSO and BFOA is proposed in this paper. To implement the coupling method, an external archive is established to save the best solutions found so far. The internal populations in GSO and BFOA can exchange the search information with the external archive in the evolutionary process. Simulation experiments are verified on two benchmark sets (DTLZ and WFG) with 3 to 15 objectives. The performance of our approach is compared with five other famous algorithms including NSGA-III, KnEA, MOEA/D-DE, GrEA and HypE. Results prove the effectiveness of our approach.

引用

页码：120248 / 120261

页数：14

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