Enhanced Bacterial Foraging Optimization Based on Progressive Exploitation Toward Local Optimum and Adaptive Raid

Since the bacterial foraging optimization algorithm (BFO) was proposed, many variants about it have been designed in order to improve the performance and applied in different fields. Even so, people are constantly probing new methods designed to enhance the performance of BFO, so as to form new variants with superior performance. As a new variant of original BFO, bacterial foraging optimization using strategies of progressive exploitation approximating local optimum and adaptive raid (BFO-DX) was proposed. On the one hand, the strategy of progressive exploration approximating local optimum (PELO) was introduced into BFO to enhance its ability of exploitation in a local space, which enables the algorithm to find the global optima better possibly. On the other hand, the strategy of the adaptive raid for the leader (ARL) was adopted to boost the speed of convergence by strengthening its exploration capacity. The numerical experiments indicates that the BFO-DX possesses better ability of finding global optima, better stability and other acceptable terms such as iteration and running time compared with classical genetic algorithm (GA), particle swarm algorithm (PSO), and conventional bacterial foraging optimization (BFO).