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Memetic particle swarm optimization

Author

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  • Y. Petalas
  • K. Parsopoulos
  • M. Vrahatis

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. Copyright Springer Science+Business Media, LLC 2007

Suggested Citation

  • Y. Petalas & K. Parsopoulos & M. Vrahatis, 2007. "Memetic particle swarm optimization," Annals of Operations Research, Springer, vol. 156(1), pages 99-127, December.
    • Handle: RePEc:spr:annopr:v:156:y:2007:i:1:p:99-127:10.1007/s10479-007-0224-y
    DOI: 10.1007/s10479-007-0224-y
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    Citations

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    Cited by:

    1. Umberto Bartoccini & Arturo Carpi & Valentina Poggioni & Valentino Santucci, 2019. "Memes Evolution in a Memetic Variant of Particle Swarm Optimization," Mathematics, MDPI, vol. 7(5), pages 1-13, May.
    2. Zhang, Zijun & Kusiak, Andrew & Song, Zhe, 2013. "Scheduling electric power production at a wind farm," European Journal of Operational Research, Elsevier, vol. 224(1), pages 227-238.
    3. Marco Locatelli & Fabio Schoen, 2016. "Global optimization based on local searches," Annals of Operations Research, Springer, vol. 240(1), pages 251-270, May.
    4. M. Joseane F. G. Macêdo & Elizabeth W. Karas & M. Fernanda P. Costa & Ana Maria A. C. Rocha, 2020. "Filter-based stochastic algorithm for global optimization," Journal of Global Optimization, Springer, vol. 77(4), pages 777-805, August.
    5. Ivona Brajević, 2021. "A Shuffle-Based Artificial Bee Colony Algorithm for Solving Integer Programming and Minimax Problems," Mathematics, MDPI, vol. 9(11), pages 1-20, May.
    6. Khalid Abdulaziz Alnowibet & Salem Mahdi & Mahmoud El-Alem & Mohamed Abdelawwad & Ali Wagdy Mohamed, 2022. "Guided Hybrid Modified Simulated Annealing Algorithm for Solving Constrained Global Optimization Problems," Mathematics, MDPI, vol. 10(8), pages 1-25, April.
    7. Ana Maria A. C. Rocha & M. Fernanda P. Costa & Edite M. G. P. Fernandes, 2017. "On a smoothed penalty-based algorithm for global optimization," Journal of Global Optimization, Springer, vol. 69(3), pages 561-585, November.
    8. Sacchelli, S. & Fabbrizzi, S., 2015. "Minimisation of uncertainty in decision-making processes using optimised probabilistic Fuzzy Cognitive Maps: A case study for a rural sector," Socio-Economic Planning Sciences, Elsevier, vol. 52(C), pages 31-40.
    9. Mohamed A. Tawhid & Ahmed F. Ali, 2016. "Simplex particle swarm optimization with arithmetical crossover for solving global optimization problems," OPSEARCH, Springer;Operational Research Society of India, vol. 53(4), pages 705-740, December.
    10. Gao, Fei & Gao, Hongrui & Li, Zhuoqiu & Tong, Hengqing & Lee, Ju-Jang, 2009. "Detecting unstable periodic orbits of nonlinear mappings by a novel quantum-behaved particle swarm optimization non-Lyapunov way," Chaos, Solitons & Fractals, Elsevier, vol. 42(4), pages 2450-2463.
    11. Mohamed A. Tawhid & Ahmed F. Ali, 2017. "Multi-directional bat algorithm for solving unconstrained optimization problems," OPSEARCH, Springer;Operational Research Society of India, vol. 54(4), pages 684-705, December.
    12. Ana Rocha & M. Costa & Edite Fernandes, 2014. "A filter-based artificial fish swarm algorithm for constrained global optimization: theoretical and practical issues," Journal of Global Optimization, Springer, vol. 60(2), pages 239-263, October.

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