A BIO-inspired optimization algorithm named deflective Memetic algorithm for detecting multi-minima of multimodal functions is proposed. The Nash Equilibria detecting in game theory is used to test the proposed algorithm. The basic Memetic Algorithm is constructed of particle swarm optimization as global search and Tabu search as local search. Two improvements are incorporated to enhance PSO. Tabu search iterates through the neighborhood to get a local optimum. Deflective technique is incorporated to tune the search for multi minima. The performance is evaluated on a series of Nash Equilibria detecting examples. The results show that the deflective MA has a notable ability to detect multi minima while yielding high accuracy and performance.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Nash Equilibrium Computing Based on Deflective Memetic Algorithm


    Contributors:
    Jiao-jiao, Gu (author) / Da-peng, Sui (author) / Peng-fei, Fu (author)


    Publication date :

    2018-08-01


    Size :

    669803 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Essays on Nash equilibrium refinements

    De Stefano, Federico | BASE | 2015

    Free access

    An Asynchronous, Forward-Backward, Distributed Generalized Nash Equilibrium Seeking Algorithm (I)

    Cenedese, Carlo / Belgioioso, Giuseppe / Grammatico, Sergio et al. | BASE | 2019

    Free access


    Probably approximately correct Nash equilibrium learning

    Fele, F / Margellos, K | BASE | 2020

    Free access