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Memetic particle swarm optimization

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Abstract

We propose a new Memetic Particle Swarm Optimization scheme that incorporates local search techniques in the standard Particle Swarm Optimization algorithm, resulting in an efficient and effective optimization method, which is analyzed theoretically. The proposed algorithm is applied to different unconstrained, constrained, minimax and integer programming problems and the obtained results are compared to that of the global and local variants of Particle Swarm Optimization, justifying the superiority of the memetic approach.

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Authors and Affiliations

  1. Computational Intelligence Laboratory (CI Lab), Department of Mathematics, University of Patras Artificial Intelligence Research Center (UPAIRC), University of Patras, 26110, Patras, Greece

    Y. G. Petalas, K. E. Parsopoulos & M. N. Vrahatis

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  1. Y. G. Petalas
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  2. K. E. Parsopoulos
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  3. M. N. Vrahatis
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Correspondence to M. N. Vrahatis.

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Petalas, Y.G., Parsopoulos, K.E. & Vrahatis, M.N. Memetic particle swarm optimization. Ann Oper Res 156, 99–127 (2007). https://doi.org/10.1007/s10479-007-0224-y

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