IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v7y2019i5p423-d230221.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/7/5/423/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/7/5/423/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yamashiro, Hirochika & Nonaka, Hirofumi, 2021. "Estimation of processing time using machine learning and real factory data for optimization of parallel machine scheduling problem," Operations Research Perspectives, Elsevier, vol. 8(C).
    2. 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.
    3. 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.
    4. 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.
    5. Rujapa Nanthapodej & Cheng-Hsiang Liu & Krisanarach Nitisiri & Sirorat Pattanapairoj, 2021. "Hybrid Differential Evolution Algorithm and Adaptive Large Neighborhood Search to Solve Parallel Machine Scheduling to Minimize Energy Consumption in Consideration of Machine-Load Balance Problems," Sustainability, MDPI, vol. 13(10), pages 1-25, May.
    6. 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.
    7. Zhang, Zhe & Gong, Xue & Song, Xiaoling & Yin, Yong & Lev, Benjamin & Chen, Jie, 2022. "A column generation-based exact solution method for seru scheduling problems," Omega, Elsevier, vol. 108(C).
    8. Wu, Xueqi & Che, Ada, 2020. "Energy-efficient no-wait permutation flow shop scheduling by adaptive multi-objective variable neighborhood search," Omega, Elsevier, vol. 94(C).
    9. 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.
    10. Roman Buil & Jesica de Armas & Daniel Riera & Sandra Orozco, 2021. "Optimization of the Real-Time Response to Roadside Incidents through Heuristic and Linear Programming," Mathematics, MDPI, vol. 9(16), pages 1-20, August.
    11. 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.
    12. He, Xuan & Pan, Quan-Ke & Gao, Liang & Neufeld, Janis S. & Gupta, Jatinder N.D., 2024. "Historical information based iterated greedy algorithm for distributed flowshop group scheduling problem with sequence-dependent setup times," Omega, Elsevier, vol. 123(C).
    13. Lotfi Hidri & Ali Alqahtani & Achraf Gazdar & Belgacem Ben Youssef, 2021. "Green Scheduling of Identical Parallel Machines with Release Date, Delivery Time and No-Idle Machine Constraints," Sustainability, MDPI, vol. 13(16), pages 1-30, August.
    14. Hongliang Zhang & Yujuan Wu & Ruilin Pan & Gongjie Xu, 2021. "Two-stage parallel speed-scaling machine scheduling under time-of-use tariffs," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 91-112, January.
    15. Jin, Xiaolong & Wu, Qiuwei & Jia, Hongjie, 2020. "Local flexibility markets: Literature review on concepts, models and clearing methods," Applied Energy, Elsevier, vol. 261(C).
    16. Hamza Jouhari & Deming Lei & Mohammed A. A. Al-qaness & Mohamed Abd Elaziz & Ahmed A. Ewees & Osama Farouk, 2019. "Sine-Cosine Algorithm to Enhance Simulated Annealing for Unrelated Parallel Machine Scheduling with Setup Times," Mathematics, MDPI, vol. 7(11), pages 1-18, November.
    17. 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.
    18. 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.
    19. Tadumadze, Giorgi & Boysen, Nils & Emde, Simon & Weidinger, Felix, 2019. "Integrated truck and workforce scheduling to accelerate the unloading of trucks," European Journal of Operational Research, Elsevier, vol. 278(1), pages 343-362.
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:7:y:2019:i:5:p:423-:d:230221. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.