Barebones particle swarm for multi-objective optimisation problems. (25th February 2010)

• Record Type: Journal Article
• Authors: Zhang, Yong; Gong, Dun-wei; Jiang, Ya-nan
• Is Part Of: International journal of innovative computing and applications. Volume 2:Number 2(2009)
• Page Start: 86
• Page End: 99
• Publication Date: 2010-02-25
• Subjects: multi-objective optimisation -- particle swarm optimisation -- barebones PSO -- mutation operators -- control parameters -- inertia weight -- acceleration coefficients; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

Control parameters, inertia weight and acceleration coefficients influence strongly performance of multi-objective particle swarm optimisation (MOPSO) algorithms. To eliminate the need for tuning of these parameters for different optimisation problems, this paper presents an almost parameter-free MOPSO algorithm, in which the concept of barebones particle swarm is incorporated into MOPSO. A special mutation operator that enriches the exploratory capabilities of our algorithm is also introduced. The proposed algorithm is validated using several benchmark test problems and four standard metrics. Results indicate that the proposed algorithm is highly competitive, and that can be considered a viable alternative to solving multi-objective optimisation problems.