Constrained multi-objective optimization using constrained non-dominated sorting combined with an improved hybrid multi-objective evolutionary algorithm. Issue 10 (3rd October 2017)

• Record Type: Journal Article
• Authors: Ning, Weikang; Guo, Baolong; Yan, Yunyi; Wu, Xianxiang; Wu, Jinfu; Zhao, Dan
• Is Part Of: Engineering optimization. Volume 49:Issue 10(2017)
• Page Start: 1645
• Page End: 1664
• Publication Date: 2017-10-03
• Subjects: Multi-objective optimization -- constraint handling -- hybrid algorithm -- dynamic resource allocation; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2016.1271661
• Languages: English
• ISSNs: 0305-215X

Abstract

ABSTRACT: Constrained multi-objective optimization problems (cMOPs) are complex because the optimizer should balance not only between exploration and exploitation, but also between feasibility and optimality. This article suggests a parameter-free constraint handling approach called constrained non-dominated sorting (CNS). In CNS, each solution in a population is assigned a constrained non-dominated rank based on its constraint violation degree and Pareto rank. An improved hybrid multi-objective optimization algorithm called cMOEA/H for solving cMOPs is proposed. Additionally, a dynamic resource allocation mechanism is adopted by cMOEA/H to spare more computational efforts for those relatively hard sub-problems. cMOEA/H is first compared with the baseline algorithm using an existing constraint handling mechanism, verifying the advantages of the proposed constraint handling mechanism. Then cMOEA/H is compared with some classic constrained multi-objective optimizers, experimental results indicating that cMOEA/H could be a competitive alternative for solving cMOPs. Finally, the characteristics of cMOEA/H are studied.