A Multi-Objective Salp Swarm Algorithm Based on Strength Pareto

In this study, a multi-objective Salp Swarm Algorithm based on strength Pareto (SPMSSA) is proposed. Firstly, an external archive is established to store the non-dominated solutions and the concept of strength Pareto is introduced to select some optimal individuals. Secondly, an external archive maintenance mechanism based on fitness value and truncation operator is incorporated to ensure the distribution of the solutions. Finally, a Levy flight strategy is adopted to increase population diversity and promote global search ability. Simulation experiments are carried out on six benchmark functions, and the results show that compared with the classical algorithms MSSA, SPEAII and NSGAII, the proposed algorithm performs better for convergence character, distribution property, and population diversity.