Evolutionary Dynamic Constrained Multiobjective Optimization: Test Suite and Algorithm

Dynamic constrained multiobjective optimization problems (DCMOPs) abound in real-world applications and gain increasing attention in the evolutionary computation community. To evaluate the capability of an algorithm in solving dynamic constrained multiobjective optimization problems (DCMOPs), artifi...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 28; no. 5; pp. 1381 - 1395
Main Authors: Chen, Guoyu, Guo, Yinan, Wang, Yong, Liang, Jing, Gong, Dunwei, Yang, Shengxiang
Format: Journal Article
Language:English
Published: IEEE 01.10.2024
Subjects:
Benchmark testing
Diversity
Diversity reception
dynamic constrained multiobjective optimization
evolutionary algorithm
Heuristic algorithms
Linear programming
Optimization
Sociology
Statistics
test suite
ISSN:1089-778X, 1941-0026
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Dynamic constrained multiobjective optimization problems (DCMOPs) abound in real-world applications and gain increasing attention in the evolutionary computation community. To evaluate the capability of an algorithm in solving dynamic constrained multiobjective optimization problems (DCMOPs), artificial test problems play a fundamental role. Nevertheless, some characteristics of real-world scenarios are not fully considered in the previous test suites, such as time-varying size, location, and shape of feasible regions, the controllable change severity, as well as small feasible regions. Therefore, we develop the generators of objective functions and constraints to facilitate the systematic design of DCMOPs, and then a novel test suite consisting of nine benchmarks, termed as DCP, is put forward. To solve these problems, a dynamic constrained multiobjective evolutionary algorithm (DCMOEA) with a two-stage diversity compensation strategy (TDCEA) is proposed. Some initial individuals are randomly generated to replace historical ones in the first stage, improving the global diversity. In the second stage, the increment between center points of Pareto sets in the past two environments is calculated and employed to adaptively disturb solutions, forming an initial population with good diversity for the new environment. Intensive experiments show that the proposed test problems enable a good understanding of strengths and weaknesses of algorithms, and TDCEA outperforms other state-of-the-art comparative ones, achieving promising performance in tackling DCMOPs.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2023.3313689