Improved Bacterial Foraging Optimization Algorithm with Comprehensive Swarm Learning Strategies

Bacterial foraging optimization (BFO), a novel bio-inspired heuristic optimization algorithm, has been attracted widespread attention and widely applied to various practical optimization problems. However, the standard BFO algorithm exists some potential deficiencies, such as the weakness of convergence accuracy and a lack of swarm communication. Owing to the improvement of these issues, an improved BFO algorithm with comprehensive swarm learning strategies (LPCBFO) is proposed. As for the LPCBFO algorithm, each bacterium keeps on moving with stochastic run lengths based on linear-decreasing Lévy flight strategy. Moreover, illuminated by the social learning mechanism of PSO and CSO algorithm, the paper incorporates cooperative communication with the current global best individual and competitive learning into the original BFO algorithm. To examine the optimization capability of the proposed algorithm, six benchmark functions with 30 dimensions are chosen. Finally, experimental results demonstrate that the performance of the LPCBFO algorithm is superior to the other five algorithms.