MOGSABAT: a metaheuristic hybrid algorithm for solving multi-objective optimisation problems

This study proposes a novel strength of multi-objective gravitational search algorithm and bat algorithm MOGSABAT to solve multi-objective optimisation problem. The proposed MOGSABAT algorithm is divided into three stages. In the first stage (moving space), a switch in a solution from single function to multiple functions that contain more than one objective to use the gravitational search algorithm GSA is determined. We established a new equation to calculate the masses of individuals in the population using the theoretical work found in the strength Pareto evolutionary algorithm. In the second stage (moving in space), how to handle the bat algorithm BAT to solve multiple functions is established. We applied the theoretical work of multi-objective particle swarm optimisation into the BAT algorithm to solve multiple functions. In the third stage, multi-objective GSA and multi-objective BAT are integrated to obtain the hybrid MOGSABAT algorithm. MOGSABAT is tested by adopting a three-part evaluation methodology that (1) describes the benchmarking of the optimisation problem (bi-objective and tri-objective) to evaluate the performance of the algorithm; (2) compares the performance of the algorithm with that of other intelligent computation techniques and parameter settings; and (3) evaluates the algorithm based on mean, standard deviation and Wilcoxon signed-rank test statistic of the function values. The optimisation results and discussion confirm that the MOGSABAT algorithm competes well with advanced metaheuristic algorithms and conventional methods.