A Survey on Evolutionary Constrained Multiobjective Optimization

Jing Liang,Boyang Qu,Xuanxuan Ban,Ke Chen,Kunjie Yu,Kangjia Qiao,Caitong Yue,Kay Chen Tan

doi:10.1109/tevc.2022.3155533

Abstract

Handling constrained multi-objective optimization problems (CMOPs) is extremely challenging, since multiple conflicting objectives subject to various constraints require to be simultaneously optimized. To deal with CMOPs, numerous constrained multi-objective evolutionary algorithms (CMOEAs) have been proposed in recent years, and they have achieved promising performance. However, there has been few literature on the systematic review of the related studies currently. This article provides a comprehensive survey for evolutionary constrained multi-objective optimization. We first review a large number of CMOEAs through categorization, and analyze their advantages and drawbacks in each category. Then, we summarize the benchmark test problems and investigate the performance of different constraint handling techniques and different algorithms, followed by some emerging and representative applications of CMOEAs. Finally, we discuss some new challenges and point out some directions of the future research in the field of evolutionary constrained multi-objective optimization.

Highlights

A variety of optimization problems in real-world applications contain several conflicting objectives and multiple complicate constraints, such as the robot gripper optimization problem [1], the combined economic emission dispatch problem [2], the urban bus scheduling problem [3], and energy saving optimization problem [4]
Methods based on the separation of objectives and constraints
It is worth noting that constraint handling technique (CHT) are indispensable when combining multi-objective evolutionary algorithms (MOEAs) to form constrained multi-objective evolutionary algorithms (CMOEAs)

Summary

Introduction

A variety of optimization problems in real-world applications contain several conflicting objectives and multiple complicate constraints, such as the robot gripper optimization problem [1], the combined economic emission dispatch problem [2], the urban bus scheduling problem [3], and energy saving optimization problem [4]. This kind of problems is denoted as constrained multi-objective optimization problems. For this kind of problems, the UPF completely overlap with CPF, so the information of objectives and constraints on the UPF is very desirable. The SP method has an advantage on solving Type I CMOPs

Objectives

Methods

Findings

Conclusion