Adaptive comprehensive learning bacterial foraging optimization and its application on vehicle routing problem with time windows

Abstract

This paper proposes a variant of the bacterial foraging optimization (BFO) algorithm with time-varying chemotaxis step length and comprehensive learning strategy which we call adaptive comprehensive learning bacterial foraging optimization (ALCBFO). An adaptive non-linearly decreasing modulation model is used to keep a well balance between the exploration and exploitation of the proposed algorithm. The comprehensive learning mechanism maintains the diversity of the bacterial population and thus alleviates the premature convergence. Compared with the classical GA, PSO, the original BFO and two improved BFO (BFO-LDC and BFO-NDC) algorithm, the proposed ACLBFO shows significantly better performance in solving multimodal problems. We also assess the performance of the ACLBFO method on vehicle routing problem with time windows (VRPTW). Compared with three other BFO algorithms, the proposed algorithm is superior and confirms its potential to solve vehicle routing problem with time windows (VRPTW).