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Multipopulation artificial bee colony algorithm based on a modified probability selection model
Author(s) -
Xu Minyang,
Wang Wenjun,
Wang Hui,
Xiao Songyi,
Huang Zhikai
Publication year - 2021
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.6216
Subject(s) - benchmark (surveying) , selection (genetic algorithm) , mathematical optimization , artificial bee colony algorithm , convergence (economics) , computer science , population , artificial intelligence , algorithm , mathematics , demography , geodesy , sociology , economic growth , economics , geography
Artificial bee colony (ABC) performs excellently over many problems, but it has some shortcomings, such as weak exploitation as well as slow convergence. For the sake of dealing with these issues, a modified ABC known as MPABC is presented. Firstly, the entire population is partitioned into two different subpopulations at the stage of employed bees, and they use different search strategies. Then, a new probability selection strategy is designed on the basis of the principle of Soft Maximum function. Finally, a novel search method is constructed for improving the intensity of exploitation by gradually increasing the ratio of the current optimal solutions. In order to comprehensively validate the capability of MPABC, 12 benchmark problems are employed. Computational results clearly demonstrate MPABC surpasses the basic ABC and some other famous ABCs.