Abstract
Depending on whether or not satisfying the symmetric condition, minimax problems could be considered either symmetric or asymmetric. In this paper, firstly we propose a new method for efficiently solving symmetric minimax problem using coevolutionary particle swarm optimization, in which a two-way alpha-beta pruning evaluation method and a simulated annealing based replacement strategy are employed. Secondly, in order to address asymmetric situations, a relaxation-based PSO is also introduced. Numerical simulations based on a series of symmetric and asymmetric minimax test functions are performed. Results of the proposed approaches and other state-of-the-art methods demonstrate that our methods could achieve considerably higher accuracy with less or equal number of function evaluations and CPU time.
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Xiong, S., Gao, R. (2015). New Approaches to the Problems of Symmetric and Asymmetric Continuous Minimax Optimizations. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_4
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DOI: https://doi.org/10.1007/978-3-319-22186-1_4
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