An Integrated External Archive Local Disturbance Mechanism for Multi-Objective Snake Optimizer

It is an interesting research direction to develop new multi-objective optimization algorithms based on meta-heuristics. Both the convergence accuracy and population diversity of existing methods are not satisfactory. This paper proposes an integrated external archive local disturbance mechanism for multi-objective snake optimizer (IMOSO) to overcome the above shortcomings. There are two improved strategies. The adaptive mating between subpopulations strategy introduces the special mating behavior of snakes with multiple husbands and wives into the original snake optimizer. Some positions are updated according to the dominated relationships between the newly created individuals and the original individuals. The external archive local disturbance mechanism is used to re-search partial non-inferior solutions with poor diversities. The perturbed solutions are non-dominated sorting with the generated solutions by the next iteration to update the next external archive. The main purpose of this mechanism is to make full use of the non-inferior solution information to better guide the population evolution. The comparison results of the IMOSO and 7 state-of-the-art algorithms on WFG benchmark functions show that IMOSO has better convergence and population diversity.