A novel multi-objective optimisation algorithm: artificial bee colony in conjunction with bacterial foraging

A novel multi-objective optimisation algorithm is proposed in the present study in order to gain the advantages of two well-known optimisation algorithms, artificial bee colony (ABC) in conjunction with bacterial foraging (BF). The novel multi-objective optimisation algorithm is compared with three multi-objective optimisation algorithms (i.e., NSGA II, SPEA 2, and Sigma MOPSO). The unique features of ABC involve fast convergence, strong robustness, high flexibility, and fewer setting parameters in solving real-parameter, non-convex, and non-smooth optimisation problems; however, the outstanding qualities of BF include excellent global searching and the self-adaptability of individuals in the group searching activities. More precisely, innovative approaches, such as the Sigma method and the neighbourhood radius approach to confinement of the archive are employed in the proposed algorithm to introduce this hybrid optimisation algorithm. To evaluate the ability of the proposed algorithm, the Pareto solutions obtained from this algorithm are compared with three well-known multi-objective optimisation algorithms. The results prove that the proposed hybrid algorithm achieves non-dominated Pareto solutions closer to the true optimal Pareto front and outperforms three prominent multi-objective optimisation algorithms.