A multi-resolution grid-based bacterial foraging optimization algorithm for multi-objective optimization problems

In recent years, bacterial foraging optimization (BFO) has been used to solve multiobjective optimization problems (MOPs). However, BFO has not fully developed its potentials on MOPs for the reason of lacking of in-depth research on the optimization mechanisms and the diversity maintenance strategies. To solve it, this paper develops a multi-resolution grid-based BFO algorithm (called as MRBFO). MRBFO redesigns four tailored optimization mechanisms for MOPs including chemotaxis, conjugation, reproduction, and elimination and dispersal to search optimal nondominated solutions. Moreover, MRBFO defines a multi-resolution grid strategy to produce well-distributed diverse nondominated solutions. The performance of MRBFO is comprehensively evaluated by comparing it with several state-of-the-art algorithms on many benchmark test problems. The empirical results have sufficiently verified the advantages of MRBFO.

Automated summary

This paper presents a multi-resolution grid-based bacterial foraging optimization algorithm (MRBFO) to solve multiobjective optimization problems (MOPs). MRBFO redesigns four tailored optimization mechanisms and introduces a multi-resolution grid strategy to search for optimal nondominated solutions. The empirical results demonstrate the advantages of MRBFO.