Simplified bacterial foraging optimization with quorum sensing for global optimization

Abstract

Bacterial foraging optimization (BFO) has been exploited for function optimization, owing to its innovative ideas gleaned from the microbiological system. This paper first discusses its three crucial limitations: high computational cost, difficulty in parameter settings, and premature convergence. To alleviate the above problems, simplified BFO with quorum sensing (QS) is proposed. First, a novel computational framework is provided to reduce the computational complexity, leading to a simplified version. Second, the concept of “QS,” bacterial reciprocal behavior, is integrated into the simplified version by utilizing a new position updating equation coupled with a dynamic communication topology. Each bacterium adjusts its search trajectory based on both biased random walk and promising search directions provided by its communicatees. The communicatees are selected via a dynamic communication topology, where a rank-based communication strategy and two information mutation schemes are used for global exploration of the search space. Finally, a parameter automation strategy is introduced to promote the exploitation of promising regions. Further, the effectiveness and efficiency of the proposed algorithm are empirically confirmed on 30 benchmark functions, by comparing it with the four variants of BFO and four other advanced algorithms.