Glowworm Swarm Optimization algorithm is applied for the simultaneous capture of multipleoptima of multimodal functions. The algorithm uses an ensemble of agents, which scan thesearch space and exchange information concerning a fitness of their current position. Thefitness is represented by a level of a luminescent quantity called luciferin. An agent movesin direction of randomly chosen neighbour, which broadcasts higher value of the luciferin.Unfortunately, in the absence of neighbours, the agent does not move at all. This is anunwelcome feature, because it diminishes the performance of the algorithm. Additionally,in the case of parallel processing, this feature can lead to unbalanced loads. This paperpresents simple modifications of the original algorithm, which improve performance of thealgorithm by limiting situations, in which the agent cannot move. The paper provides resultsof comparison of an original and modified algorithms calculated for several multimodal testfunctions.


    Access

    Download


    Export, share and cite



    Title :

    IMPROVEMENTS TO GLOWWORM SWARM OPTIMIZATION ALGORITHM


    Contributors:

    Publication date :

    2013-03-15


    Remarks:

    doi:10.7494/csci.2010.11.0.7
    Computer Science; Vol. 11; 7 ; 2300-7036 ; 1508-2806



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629




    Quantum-inspired glowworm swarm optimisation and its application

    Gao, Hongyuan / Du, Yanan / Diao, Ming | British Library Online Contents | 2017



    Quantum Glowworm Swarm Algorithm and Its Application to No-wait Flowshop Scheduling

    Qi, Xuemei / Wang, Hongtao / Yang, Jie et al. | British Library Online Contents | 2016