文献类型：期刊文献

英文题名：Self-adaptive differential evolution algorithm with α-constrained-domination principle for constrained multi-objective optimization

作者：Qian, Feng[1];Xu, Bin[1];Qi, Rongbin[1];Tianfield, Huaglory[2]

机构：[1]E China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Glasgow Caledonian Univ, Sch Engn & Built Environm, Dept Comp Commun & Interact Syst, Glasgow G4 0BA, Lanark, Scotland

年份：2012

卷号：16

期号：8

起止页码：1353

外文期刊名：SOFT COMPUTING

收录：;EI(收录号：20123115300804);WOS:【SCI-EXPANDED(收录号:WOS:000306354600005)】；

基金：Authors would like to express their sincere thanks to the reviewers for their valuable suggestions and comments, and Dr. P. N. Suganthan for providing the source codes of CMODE. This work was supported by Major State Basic Research Development Program of China (973 Program: 2012CB720500), National Natural Science Foundation of China (Key Program: 61134007), Major State Basic Research Development Program of Shanghai (10JC1403500), New Teacher Fund Program of Specialized Research Fund for the Doctoral Program of Higher Education (No. 200802511011), Shanghai Leading Academic Discipline Project (No. B504).

语种：英文

外文关键词：Constrained optimization; Differential evolution; Self-adaptive strategy; Multi-objective optimization; alpha-constrained-domination

摘要：Real-world problems are inherently constrained optimization problems often with multiple conflicting objectives. To solve such constrained multi-objective problems effectively, in this paper, we put forward a new approach which integrates self-adaptive differential evolution algorithm with alpha-constrained-domination principle, named SADE-alpha CD. In SADE-alpha CD, the trial vector generation strategies and the DE parameters are gradually self-adjusted adaptively based on the knowledge learnt from the previous searches in generating improved solutions. Furthermore, by incorporating domination principle into alpha-constrained method, alpha-constrained-domination principle is proposed to handle constraints in multi-objective problems. The advantageous performance of SADE-alpha CD is validated by comparisons with non-dominated sorting genetic algorithm-II, a representative of state-of-the-art in multi-objective evolutionary algorithms, and constrained multi-objective differential evolution, over fourteen test problems and four well-known constrained multi-objective engineering design problems. The performance indicators show that SADE-alpha CD is an effective approach to solving constrained multi-objective problems, which is basically enabled by the integration of self-adaptive strategies and alpha-constrained-domination principle.