文献类型：期刊文献

英文题名：Handling Multiple Objectives with Biogeography-based Optimization

作者：Ma, Hai-Ping[1];Ruan, Xie-Yong[1];Pan, Zhang-Xin[1]

机构：[1]Shaoxing Univ, Dept Phys & Elect Engn, Shaoxing 312000, Peoples R China

年份：2012

卷号：9

期号：1

起止页码：30

外文期刊名：INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING

收录：ESCI(收录号：WOS:000442366000005)、EI(收录号：20120914823307)、Scopus(收录号：2-s2.0-84857462719)、WOS

基金：This work was supported by Zhejiang Provincial Natural Science Foundation of China (No. Y1090866).

语种：英文

外文关键词：Multi-objective optimization; biogeography-based optimization (BBO); evolutionary algorithms; Pareto optimal; non-dominated sorting

外文摘要：Biogeography-based optimization (BBO) is a new evolutionary optimization method inspired by biogeography. In this paper, BBO is extended to a multi-objective optimization, and a biogeography-based multi-objective optimization (BBMO) is introduced, which uses the cluster attribute of islands to naturally decompose the problem. The proposed algorithm makes use of nondominated sorting approach to improve the convergence ability efficiently. It also combines the crowding distance to guarantee the diversity of Pareto optimal solutions. We compare the BBMO with two representative state-of-the-art evolutionary multi-objective optimization methods, non-dominated sorting genetic algorithm-II (NSGA-II) and archive-based micro genetic algorithm (AMGA) in terms of three metrics. Simulation results indicate that in most cases, the proposed BBMO is able to find much better spread of solutions and converge faster to true Pareto optimal fronts than NSGA-II and AMGA do.