Title: Improved adaptive multi-objective particle swarm algorithm under big data

Authors: Tao Wang

Addresses: School of Science, Hubei University for Nationalities, Enshi 445000, China

Abstract: Boundary handling and the global best guiders' selection are of significance to the performance of the multi-objective particle swarm algorithm. Considering the different characteristics of methods of operation, an improved multi-objective adaptive particle swarm optimisation (IMAPSO) was proposed in this paper. When the algorithm falls into local optimum, start crossover and mutation; when the algorithm's convergence stagnates, switch the boundary handling operator between the truncation and the exponential distribution truncation methods; when the diversity of algorithm has not improved in a given duration, switch the two operations of trim boundary handling and exponential distribution and the simulation results of the standard test functions demonstrate the effectiveness of the algorithm proposed in this paper.

Keywords: multi-objective optimisation; particle swarm optimisation; PSO; Pareto optimality; constrained domination; bound handling; global best selection; adaptive algorithm.

DOI: 10.1504/IJAMC.2017.085936

International Journal of Advanced Media and Communication, 2017 Vol.7 No.2, pp.124 - 137

Received: 08 Jun 2016
Accepted: 13 Sep 2016
Published online: 18 Aug 2017 *

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