A modified single and multi-objective bacteria foraging optimisation for the solution of quadratic assignment problem

Abstract

Non-polynomial hard (NP-hard) problems are challenging due to time-constraint. The bacteria foraging optimisation (BFO) algorithm is a metaheuristics algorithm that is used for NP-hard problems. BFO is inspired by the behaviour of the bacteria foraging such as E. coli. The aim of BFO is to eliminate weak foraging properties bacteria and maintain breakthrough foraging properties bacteria toward the optimum. However, reaching to optimal solutions are time-demanding. In this paper, we modified single objective and multi-objective BFO (MOBFO) by adding mutation and crossover from genetic algorithm operators to update the solutions in each generation, and local tabu search algorithm to reach the local optimum solution. Additionally, we used fast non-dominated sort algorithm in MOBFO to find the best non-dominated solutions. We evaluated the performance of the proposed algorithms with quadratic assignment problem instances. The experimental results show that our approaches outperform some previous optimisation algorithms in both convergent and divergent aspects.