An adaptive disturbance multi-objective evolutionary algorithm based on decomposition. (11th January 2023)

• Record Type: Journal Article
• Authors: Shi, Yanfang; Shi, Jianguo
• Is Part Of: International journal of modelling, identification and control. Volume 41:Number 4(2022)
• Page Start: 306
• Page End: 315
• Publication Date: 2023-01-11
• Subjects: multi-objective evolutionary algorithm -- disturbance individuals -- disturbance weight vectors -- decomposition; Engineering -- Methodology -- Periodicals; Science -- Methodology -- Periodicals; 001.42
• Languages: English
• ISSNs: 1746-6172

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.