Title: Many-objective particle swarm optimisation algorithm based on multi-elite opposition mutation mechanism in the internet of things environment

Authors: Lanlan Kang; Naiwei Liu; Wenliang Cao; Yeh-Cheng Chen

Addresses: College of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China; Department of Information Engineering, Gannan University of Science and Technology, Ganzhou 341000, China ' College of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China ' School of Electronic Information, Dongguan Polytechnic, Dongguan, Guangdong 523808, China ' Department of Computer Science, University of California, Davis, CA, 95616, USA

Abstract: Multi-objective optimisation problem in Internet of Things technology has been widely concerned by researchers. The family of multi-objective particle swarm optimisation is among the most representative ones. However, there still exist the shortcomings of overspending and premature convergence. This paper proposes a many-objective particle swarm optimisation algorithm based on opposition-based mutation for elite mechanism. The new algorithm mainly includes three strategies: (1) Opposition-based learning population initialisation strategy, which is designed to avoid the blindness and uncertainty of initial population, and improves the distribution of population and accelerates speed of exploration. (2) Multi-elite opposition mutation mechanism, which is proposed to help particles get away from local optimal positions via a targeted exploration in the search space. (3) Singularity archive technique, which is established to disturb the global evolution trend and further balance the contradiction of convergence and diversity of the population. The effectiveness of the proposed algorithm is verified by comparing 11 algorithms in the simulation experiments.

Keywords: IoTs; many-objective optimisation; particle swarm optimisation; opposition-based mutation; singularity archive technique.

DOI: 10.1504/IJGUC.2023.131010

International Journal of Grid and Utility Computing, 2023 Vol.14 No.2/3, pp.107 - 121

Received: 16 Jun 2022
Received in revised form: 11 Sep 2022
Accepted: 25 Sep 2022
Published online: 18 May 2023 *

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