A dynamic multi-objective particle swarm optimization algorithm based on adversarial decomposition and neighborhood evolution

Date

2021-09-22

Advisors

Journal Title

Journal ISSN

ISSN

2210-6502

Volume Title

Publisher

Elsevier

Type

Article

Peer reviewed

Yes

Abstract

Many multi-objective optimization problems in the real world are dynamic, with objectives that conflict and change over time. These problems put higher demands on the algorithm’s convergence performance and the ability to respond to environmental changes. Confronting these two points, this paper proposes a dynamic multi-objective particle swarm optimization algorithm based on adversarial decomposition and neighborhood evolution (ADNEPSO). To overcome the instability of the traditional decomposition method for the changing Pareto optimal front (POF) shape, the proposed algorithm utilizes the complementary characteristics in the search area of the adversarial vector, and the two populations are alternately updated and co-evolved by adversarial search directions. Additionally, a novel particle update strategy is proposed to select promising neighborhood information to guide evolution and enhance diversity. To improve the ability to cope with environmental changes, an effective dynamic response mechanism is proposed, including three parts: archive set prediction, exploration of global optimal information, and retention of excellent particles to accelerate convergence to the Pareto optimal set (POS) in the new environment. The proposed algorithm is tested on a series of benchmark problems and compared to several state-of-the-art algorithms. The results show that ADNEPSO performed excellently in both convergence and diversity, and is highly competitive in dealing with dynamic problems.

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 multi-objective optimization, Particle swarm optimization, Adversarial decomposition

Citation

Zheng, J., Zhang, Z., Zou, J., Yang, S., Ou, J., and Hu, Y. (2021) A dynamic multi-objective particle swarm optimization algorithm based on adversarial decomposition and neighborhood evolution. Swarm and Evolutionary Computation, 100987.

Rights

Research Institute