Title: An adaptive disturbance multi-objective evolutionary algorithm based on decomposition

Authors: Yanfang Shi; Jianguo Shi

Addresses: Hebei Software Institute, Baoding, Hebei, 071000, China ' Hebei Software Institute, Baoding, Hebei, 071000, China

Abstract: In solving multi-objective optimisation problems, the uniformly distributed weight vector of decomposition based multi-objective evolutionary algorithm (MOEA/D) is not completely suitable for the non-uniformly distributed Pareto front (PF). In order to solve the situation above, this paper proposes an adaptive disturbance multi-objective evolutionary algorithm based on decomposition (AD-MOEA/D), which introduces the disturbance individuals and disturbance weight vectors during the evolution. The disturbance individuals maintain the population diversity and improve convergence accuracy. The disturbance weight vectors assist the weight vectors to adjust adaptively and improve the distribution of PF. Besides, both disturbance individuals and disturbance weight vectors are produced according to the actual evolution, which will not participate in evolution when it is not necessary. The experimental results on multi-objective test functions show that the PF optimised by AD-MOEA/D has better convergence and distribution.

Keywords: multi-objective evolutionary algorithm; disturbance individuals; disturbance weight vectors; decomposition.

DOI: 10.1504/IJMIC.2022.128314

International Journal of Modelling, Identification and Control, 2022 Vol.41 No.4, pp.306 - 315

Received: 17 Nov 2021
Accepted: 15 Jan 2022
Published online: 17 Jan 2023 *

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