Title: Dynamic multi-swarm pigeon-inspired optimisation

Authors: Yichao Tang; Bo Wei; Yinglong Zhang; Xiong Li; Xuewen Xia; Ling Gui

Addresses: Intelligent Optimisation and Information Processing Lab, School of Software, East China Jiaotong University, Jiangxi, China ' Intelligent Optimisation and Information Processing Lab, School of Software, East China Jiaotong University, Jiangxi, China ' Intelligent Optimisation and Information Processing Lab, School of Software, East China Jiaotong University, Jiangxi, China ' Intelligent Optimisation and Information Processing Lab, School of Software, East China Jiaotong University, Jiangxi, China ' Intelligent Optimisation and Information Processing Lab, School of Software, East China Jiaotong University, Jiangxi, China ' School of Economics and Management, East China Jiaotong University, Jiangxi, China

Abstract: Pigeon-inspired optimisation (PIO) has shown favourable performance on global optimisation problems. However, it lacks the part of individual experience, which makes it prone to premature convergence when solving multimodal problems. Moreover, the landmark operator model in PIO may cause the population size to decrease too quickly, which is harmful for exploration. To overcome the shortcomings, a dynamic multi-swarm pigeon-inspired optimisation (DMS-PIO) is proposed in this research. In PIO, the entire population is divided into multiple swarms. During the evolutionary process, the size of each swarm can be dynamically adjusted, and the multiple swarms can be randomly regrouped. Relying on the dynamic adjustment of swarms' sized, exploration and exploitation are balanced in the initial evolutionary stage and last stage. Furthermore, the randomly regrouping schedule is used to keep the population diversity. To enhance the comprehensive performance of PIO, the map and compass operator and the landmark operator in it are conducted alternately in each generation. Experimental results between DMS-PIO and other five PIO algorithms demonstrate that our proposed DMS-PIO can avoid the premature convergence problem when solving multimodal problems, and yields more effective performance in complex continuous optimisation problems.

Keywords: pigeon-inspired optimisation; PIO; dynamical swarm sized; randomly regrouping schedule; continuous optimisation problems.

DOI: 10.1504/IJCSM.2021.10039884

International Journal of Computing Science and Mathematics, 2021 Vol.13 No.3, pp.267 - 282

Received: 16 Aug 2018
Accepted: 28 Sep 2018

Published online: 02 Aug 2021 *

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