Many-objective optimization problems are problems that have more than three objective functions. Traditional multi-objective evolutionary algorithms scale poorly when the number of objective functions increases. Recently, different approaches have been proposed for improving the performance of these algorithms on many-objective optimization problems. One of these approaches is the use of multiple populations on multi-objective particle swarm optimization, called multi-swarm. Multi-swarm techniques explore parallel populations to decompose the problem and optimize them in a collaborative manner. This paper presents a new multi-swarm algorithm, called Multi-Swarm Algorithm Based on Archiving and Topologies (MSAT). MSAT combines different archiving methods and communication topologies aiming to obtain good convergence and diversity in many-objective optimization problems. An experimental set is performed seeking to find the best combination of archiving and topology methods. Furthermore, MSAT is confronted to NSGA-III algorithm in different scenarios. MSAT outperformed NSGA-III both in terms of convergence and diversity, for concave and convex problems.
