An Improved Leader Guidance in Multi Objective Particle Swarm Optimization

Generally, Particle Swarm Optimization based Multi-Objective Optimization algorithm use only one leader to guide the particles flight in the velocity update. Thus, this paper introduces a Multi Leaders Multi Objective Optimization algorithm which is an initial implementation of multiple leaders in guiding the particles flight to search for optimum solutions. The multiple leaders´ method is implemented by summing up all the distance between a particle and all of its leaders during velocity update The algorithm is tested on several benchmark test problems to measure its convergence and diversity ability in finding the best Pareto Front. The results show a promising and competitive performance when compared to the other algorithms.