Evolutionary dynamic constrained multiobjective optimization: Test suite and algorithm

Date

2023-09-11

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

IEEE Press

Type

Article

Peer reviewed

Yes

Abstract

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 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 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.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

Dynamic constrained multiobjective optimization, Evolutionary algorithm, Test suite, Diversity

Citation

G. Chen, Y. Guo, Y. Wang, J. Liang, D. Gong and S. Yang. (2023) Evolutionary Dynamic Constrained Multiobjective Optimization: Test Suite and Algorithm. IEEE Transactions on Evolutionary Computation,

Rights

Research Institute

Institute of Artificial Intelligence (IAI)