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A dynamic fuzzy-based dance mechanism for the bee colony optimization algorithm
journal contributionposted on 2018-11-01, 00:00 authored by S S Choong, L P Wong, Chee Peng LimChee Peng Lim
The bee colony optimization (BCO) algorithm with a linear dance function (denoted as the BCO-Linear algorithm) is inspired by the bees' foraging behaviors, in which waggle dances are modeled as a communication medium among bees. Through these informative waggle dances, more bees are recruited toward exploring more profitable search regions. In the BCO-Linear algorithm, a fitter bee is allowed to dance longer, and the dance duration is determined by a linear function with a scaling parameter that requires manual tuning. This article presents a dynamic fuzzy-based dance mechanism, ie, the BCO-Fuzzy algorithm, to solve the manual tuning problem. A fuzzy-based approach is applied to regulate the duration of waggle dances instead of regulating the dance duration using a linear function. The proposed BCO-Fuzzy algorithm comprises parameters that are dynamically controlled based on the feedback of the search process, therefore overcoming the limitation of manual parameter tuning of the BCO-Linear algorithm. The BCO-Fuzzy algorithm is evaluated comprehensively using a set of benchmark traveling salesman problems. The experimental results show that the performance of the BCO-Fuzzy algorithm is comparable with that of the BCO-Linear algorithm. Specifically, the dynamic fuzzy-based dance mechanism improves the BCO algorithm in terms of rewarding dance instances near the inflection point. Performance comparison with other nature-inspired algorithms proves the effectiveness of the proposed BCO-Fuzzy algorithm.
Pagination999 - 1024
PublisherJohn Wiley & Sons
Publication classificationC1 Refereed article in a scholarly journal
Copyright notice2018, Wiley Periodicals, Inc.
bee colony optimizationdynamic parameter controlfuzzy logictraveling salesman problemwaggle danceScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer SciencePARTICLE SWARM OPTIMIZATIONPARAMETER ADAPTATIONLOGICDESIGNINTELLIGENCEInformation SystemsArtificial Intelligence and Image ProcessingComputation Theory and Mathematics