A dynamic fuzzy‐based dance mechanism for the bee colony optimization algorithm. (5th January 2018)

• Record Type: Journal Article
• Authors: Choong, Shin Siang; Wong, Li‐Pei; Lim, Chee Peng
• Is Part Of: Computational intelligence. Volume 34:Number 4(2018)
• Page Start: 999
• Page End: 1024
• Publication Date: 2018-01-05
• Subjects: bee colony optimization -- dynamic parameter control -- fuzzy logic -- traveling salesman problem -- waggle dance; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12159
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: 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.