Particle swarm optimization for minimax problems

This paper investigates the ability of the Particle Swarm Optimization (PSO) method to cope with minimax problems through experiments on well-known test functions. Experimental results indicate that PSO tackles minimax problems effectively. Moreover, PSO alleviates difficulties that might be encount...

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Bibliographic Details
Published inProceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) Vol. 2; pp. 1576 - 1581 vol.2
Main Authors Laskari, E.C., Parsopoulos, K.E., Vrahatis, M.N.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2002
Subjects
Artificial intelligence
Chebyshev approximation
Design engineering
Eigenvalues and eigenfunctions
Game theory
Mathematics
Minimax techniques
Particle swarm optimization
Quadratic programming
Testing
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Summary:This paper investigates the ability of the Particle Swarm Optimization (PSO) method to cope with minimax problems through experiments on well-known test functions. Experimental results indicate that PSO tackles minimax problems effectively. Moreover, PSO alleviates difficulties that might be encountered by gradient-based methods, due to the nature of the minimax: objective function, and potentially lead to failure. The performance of PSO is compared with that of other established approaches, such as the sequential quadratic programming (SQP) method and a recently proposed smoothing technique.
ISBN:0780372824
9780780372825
DOI:10.1109/CEC.2002.1004477