Title: A new multi-objective artificial bee colony algorithm based on reference point and opposition

Authors: Songyi Xiao; Wenjun Wang; Hui Wang; Zhikai Huang

Addresses: School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China ' School of Business Administration, Nanchang Institute of Technology, Nanchang 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China

Abstract: A new multi-objective artificial bee colony (ABC) algorithm based on reference point and opposition (called ROMOABC) is proposed in this paper. Firstly, the original framework of ABC is modified to improve the efficiency of population renewal and accelerate the convergence rate. On the basis of this framework, two new strategies are proposed. In the scout bee search, opposition-based learning and elite solutions are used to reduce the waste of computing resources. Distribution of solutions is improved by using reference points' associated external archive. Experiments are conducted on 16 multi-objective benchmark functions including ZDT, DTLZ and WFG multi-objective benchmark functions. The comparison of ROMOABC with five other multi-objective algorithms shows that it has competitive convergence and diversity.

Keywords: artificial bee colony; ABC; multi-objective optimisation; external archive; opposition; elite learning.

DOI: 10.1504/IJBIC.2022.120732

International Journal of Bio-Inspired Computation, 2022 Vol.19 No.1, pp.18 - 28

Received: 18 Oct 2020
Accepted: 19 Nov 2020
Published online: 07 Feb 2022 *

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