文献类型：期刊文献

英文题名：Objective reduction particle swarm optimizer based on maximal information coefficient for many-objective problems

作者：Liang, Yi[1];He, Wangli[1];Zhong, Weimin[1];Qian, Feng[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai, Peoples R China

年份：2018

卷号：281

起止页码：1

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20175004545262);WOS:【SCI-EXPANDED(收录号:WOS:000424891600001)】；

基金：This work was supported by the National Key R&D Program of China (2016YFB0303403), National Natural Science Foundation of China (61773163), Young Elite Sciientists Sponsorship Program by CAST (2016QNRC001), Natural Science Foundation of Shanghai (17ZR1444600).

语种：英文

外文关键词：Maximal information coefficient; Particle swarm optimization; Objective reduction; Many-objective problems

摘要：It is challenging to solve reducible many-objective problems due to difficulties caused by the unknown number of non-conflicting objectives. Objective reduction method is one of promising and efficient solutions in which two fundamental problems should be addressed: how to find the redundant objectives and which objectives should be selected or omitted. A novel objective reduction algorithm is proposed in this paper, named Maximal Information Coefficient based Multi-Objective Particle Swarm Optimizer (MIC-MOPSO). By a powerful MIC indicator, the algorithm could find hidden linear or nonlinear relationships between two objectives. Another indicator, the change rate of non-dominated population, is used to judge whether there exist non-conflicting objectives or not. An effective way to rapidly select the retained objectives is also developed based on these two indicators. Tested by a series of benchmark experiments and a real industrial optimization problem, the results show that our approach significantly improve the performance on both reducible and irreducible many-objective problems. (c) 2017 Published by Elsevier B.V.