A novel metaheuristic for multi-objective optimization problems: The multi-objective vortex search algorithm

This study investigates a multi-objective Vortex Search algorithm (MOVS) by modifying the single-objective Vortex Search algorithm or VS. The VS is a metaheuristic-based algorithm that uses a new adaptive step-size adjustment strategy to improve the performance of the search process. Search mechanism of the VS is inspired by the vortex pattern, so it is called a “Vortex Search” algorithm. The original VS is an improved way of solving single-objective continuous problems. To improve the MOVS algorithm, the VS algorithm is enhanced with added calculation approaches, such as fast-nondominated-sorting and crowding-distance, in order to identify the degree of non-dominance of the solutions and the densities of their occurrence. In addition, a crossover operation is added to the MOVS algorithm in order to enhance the Pareto front convergence capacity of the solutions. Finally, to spread the solutions more successfully over the Pareto front, it has been randomly produced using the inverse incomplete gamma function using a parameter between 0 and 1. The proposed MOVS algorithm is tested against 36 different benchmark problems together with NSGAII, MOCell, IBEA and MOEA/D algorithms. The test results indicate that the MOVS algorithm achieves a better performance on accuracy and convergence speed than any other algorithms when comparisons are made against several test problems, and they also show that it is a competitive algorithm.

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