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Barebones particle swarm for multi-objective optimisation problems
by Yong Zhang, Dun-wei Gong, Ya-nan Jiang
International Journal of Innovative Computing and Applications (IJICA), Vol. 2, No. 2, 2009

 

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.

Online publication date: Wed, 24-Feb-2010

 

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