Title:

Multi-operator Differential Evolution with MOEA/D for Solving Multi-objective Optimization Problems, Journal of Telecommunications and Information Technology, 2022, nr3

Creator:

Aggarwal, Sakshi ; Mishra, Krishn K

Subject and Keywords:

multi-objective optimization ; weighted-aggregation ; mutationoperators ; erential evolution

Description:

In this paper, we propose a multi-operator differentia evolution variant that incorporates three diverse mutation strategies in MOEA/D. Instead of exploiting the local region, the proposed approach continues to search for optimal solutions in the entire objective space. It explicitly maintains diversity of the population by relying on the benefit of clustering. To promowe convergence, the solutions close to the ideal position, in the objective space are given preference in the evolutionary process. The core idea is to ensure diversity of the population by applying multiple mutation schemes and a faster convergence rate, giving preference to solutions based on their proximity to the ideal position in the MOEA/D paradigm. The performance of the proposed algorithm is evaluated by two popular test suites. The experimental results demonstrate that the proposed approach outperforms other MOEA/D algorithms.

Publisher:

Instytut Łączności - Państwowy Instytut Badawczy, Warszawa

Date:

2022, nr 3

Resource Type:

artykuł

Format:

application/pdf

Resource Identifier:

ISSN 1509-4553, on-line: ISSN 1899-8852

Source:

Journal of Telecommunications and Information Technology

Language:

ang

Rights Management:

Biblioteka Naukowa Instytutu Łączności