Multi-Hive Artificial Bee Colony Algorithm for Constrained Multi-Objective Optimization

被引：0

作者：

Zhang, Hao ^{[1
]}

Zhu, Yunlong ^{[1
]}

Yan, Xiaohui ^{[1
]}

机构：

[1] Chinese Acad Sci, Key Lab Ind Informat, Shenyang Inst Automat, Shenyang 110016, Peoples R China

来源：

2012 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2012年

关键词：

Multi-Hive; ABC algorithm; Constraint; Multi-objective Optimization; symbiosis theory; PERFORMANCE;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper presents a general cooperative coevolution model inspired by the concept and main ideas of the coevolution of symbiotic species in natural ecosystems. A novel approach called "multi-hive artificial bee colony" for constrained multi-objective optimization (MHABC-CMO) is proposed based on this model. A novel information transfer strategy among multiple swarms and division operator are proposed in MHABC-CMO to tie it closer to natural evolution, as well as improve the robustness of the algorithm. Simulation experiment of MHABC-CMO on a set of benchmark test functions are compared with other nature inspired techniques which includes multi-objective artificial bee colony (MOABC), nondominated sorting genetic algorithm II (NSGA II) and multi-objective particle swarm optimization (MOPSO). The numerical results demonstrate MHABC-CMO approach is a powerful search and optimization technique for constrained multi-objective optimization.

引用

页数：8

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