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Memes Evolution in a Memetic Variant of Particle Swarm Optimization

Author

Listed:
  • Umberto Bartoccini

    (Department of Humanities and Social Sciences, University for Foreigners of Perugia, 06123 Perugia, Italy)

  • Arturo Carpi

    (Department of Mathematics and Computer Science, University of Perugia, 1-06121 Perugia, Italy)

  • Valentina Poggioni

    (Department of Mathematics and Computer Science, University of Perugia, 1-06121 Perugia, Italy)

  • Valentino Santucci

    (Department of Humanities and Social Sciences, University for Foreigners of Perugia, 06123 Perugia, Italy)

Abstract

In this work, a coevolving memetic particle swarm optimization (CoMPSO) algorithm is presented. CoMPSO introduces the memetic evolution of local search operators in particle swarm optimization (PSO) continuous/discrete hybrid search spaces. The proposed solution allows one to overcome the rigidity of uniform local search strategies when applied to PSO. The key contribution is that memes provides each particle of a PSO scheme with the ability to adapt its exploration dynamics to the local characteristics of the search space landscape. The objective is obtained by an original hybrid continuous/discrete meme representation and a probabilistic co-evolving PSO scheme for discrete, continuous, or hybrid spaces. The coevolving memetic PSO evolves both the solutions and their associated memes, i.e. the local search operators. The proposed CoMPSO approach has been experimented on a standard suite of numerical optimization benchmark problems. Preliminary experimental results show that CoMPSO is competitive with respect to standard PSO and other memetic PSO schemes in literature, and its a promising starting point for further research in adaptive PSO local search operators.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:5:p:423-:d:230221
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    References listed on IDEAS

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    1. Y. Petalas & K. Parsopoulos & M. Vrahatis, 2007. "Memetic particle swarm optimization," Annals of Operations Research, Springer, vol. 156(1), pages 99-127, December.
    2. Hongfeng Wang & Shengxiang Yang & W.H. Ip & Dingwei Wang, 2012. "A memetic particle swarm optimisation algorithm for dynamic multi-modal optimisation problems," International Journal of Systems Science, Taylor & Francis Journals, vol. 43(7), pages 1268-1283.
    3. Shagun Akarsh & Avadh Kishor & Rajdeep Niyogi & Alfredo Milani & Paolo Mengoni, 2017. "Social Cooperation in Autonomous Agents to Avoid the Tragedy of the Commons," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 8(2), pages 1-19, April.
    4. Wu, Xueqi & Che, Ada, 2019. "A memetic differential evolution algorithm for energy-efficient parallel machine scheduling," Omega, Elsevier, vol. 82(C), pages 155-165.
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    Cited by:

    1. Tae-Hyoung Kim & Jung-In Byun, 2020. "Truss Sizing Optimization with a Diversity-Enhanced Cyclic Neighborhood Network Topology Particle Swarm Optimizer," Mathematics, MDPI, vol. 8(7), pages 1-21, July.
    2. Shiyuan Yang & Hongtao Wang & Yihe Xu & Yongqiang Guo & Lidong Pan & Jiaming Zhang & Xinkai Guo & Debiao Meng & Jiapeng Wang, 2023. "A Coupled Simulated Annealing and Particle Swarm Optimization Reliability-Based Design Optimization Strategy under Hybrid Uncertainties," Mathematics, MDPI, vol. 11(23), pages 1-26, November.

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