Title: An adaptive multi-objective particle swarm optimisation algorithm based on fitness distance to streamline repository

Authors: Suyu Wang; Dengcheng Ma; Ze Ren; Yuanyuan Qu; Miao Wu

Addresses: School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), D11 Xueyuan Road, HaiDian District, 100083, Beijing, China ' School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), D11 Xueyuan Road, HaiDian District, 100083, Beijing, China ' School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), D11 Xueyuan Road, HaiDian District, 100083, Beijing, China ' School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), D11 Xueyuan Road, HaiDian District, 100083, Beijing, China ' School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), D11 Xueyuan Road, HaiDian District, 100083, Beijing, China

Abstract: In recent years, multi-objective particle swarm optimisation (MOPSO) algorithm has been paid more attention. One of its indispensable structures is the maintenance and update mechanism of the repository. The existing mechanisms are relatively simple, and most of them are based on the crowding distance sorting strategy, and not conducive to the distribution and accuracy of the algorithms. The paper innovated this mechanism and proposed an adaptive multi-objective particle swarm optimisation algorithm to streamline repository based on fitness distance (FDMOPSO). Both the concept of fitness distance and the corresponding improve methods of mutation mechanism and adaptive mechanism was proposed. The algorithm itself was tested using benchmarks. The results show that the proposed application of fitness distance had a better improvement on the convergence and distribution. Compared with other algorithms, the FDMOPSO algorithm had the best overall performance.

Keywords: MOPSO; fitness distance; streamline repository; multi-objective optimisation; MOO; adaptive.

DOI: 10.1504/IJBIC.2022.128089

International Journal of Bio-Inspired Computation, 2022 Vol.20 No.4, pp.209 - 219

Received: 13 Mar 2020
Accepted: 27 Oct 2020
Published online: 05 Jan 2023 *

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