Chaos Enhanced Bacterial Foraging Optimization for Global Optimization

The recently developed Bacterial Foraging Optimization algorithm (BFO) is a nature-inspired optimization algorithm based on the foraging behavior of Escherichia coli. Due to its simplicity and effectiveness, BFO has been applied widely in many engineering and scientific fields. However, when dealing with more complex optimization problems, especially high dimensional and multimodal problems, BFO performs poorly in convergence compared to other nature-inspired optimization techniques. In this paper, we therefore propose an improved BFO, termed ChaoticBFO, which combines two chaotic strategies to achieve a more suitable balance between exploitation and exploration. Specifically, a chaotic initialization strategy is incorporated into BFO for bacterial population initialization to achieve acceleration throughout early steps of the proposed algorithm. Then, a chaotic local search with a `shrinking' strategy is introduced into the chemotaxis step to escape from local optimum. The performance of ChaoticBFO was validated on 23 numerical well-known benchmark functions by comparing with 10 other competitive metaheuristic algorithms. Moreover, it was applied to two real-world benchmarks from IEEE CEC 2011. The experimental results demonstrate that ChaoticBFO is superior to its counterparts in both convergence speed and solution quality in most of the cases. This paper is of great significance for promoting the research, improvement and application of the BFO algorithm.