文献类型：期刊文献

英文题名：Multi-objective differential evolution with ranking-based mutation operator and its application in chemical process optimization

作者：Chen, Xu[1];Du, Wenli[1];Qian, Feng[1]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2014

卷号：136

起止页码：85

外文期刊名：CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

收录：;EI(收录号：20242616528653);WOS:【SCI-EXPANDED(收录号:WOS:000339534500009)】；

基金：Authors would like to express their sincere thanks to the reviewers for their valuable suggestions and comments. This work is supported by the Major State Basic Research Development Program of China (2012CB720500), National Natural Science Foundation of China (61333010, 21276078), National Science Fund for Outstanding Young Scholars (61222303), Fundamental Research Funds for the Central Universities, Shanghai Rising-Star Program (13QH1401200), Program for New Century Excellent Talents in University (NCET-10-0885) and Shanghai R&D Platform Construction Program (13DZ2295300).

语种：英文

外文关键词：Dynamic optimization; Multi-objective optimization; Differential evolution; Ranking-based mutation operator

摘要：Dynamic optimization problems in chemical processes are often quite challenging because these problems often involve multiple and conflicting objectives. To solve the multi-objective dynamic optimization problems (MDOPs), in this paper, we propose a new multi-objective differential evolution (MODE) variant, named MODE-RMO for short, inspired by the phenomenon that good individuals which contain good information often have more chance to be utilized to guide other individuals. In MODE-RMO, the ranking-based mutation operator is integrated into the MODE algorithm to accelerate the convergence speed, and thus enhance the performance. The performance of our proposed algorithm is firstly evaluated in ten test functions and compared with other MOEAs. The results demonstrate that MODE-RMO can generate Pareto optimal fronts with satisfactory convergence and diversity. Finally, MODE-RMO is applied to solve three MDOPs taken from literature using control vector parameterization. The obtained results indicate that MODE-RMO is an effective and efficient approach for MDOPs. (C) 2014 Elsevier B.V. All rights reserved.