Archive Based Multi-swarm Algorithm for Many-Objective Problems

Multi-Objective Optimization Problems (MOPS) present several challenges. In particular, when the number of objectives is greater than three, they are actually called Many-Objective Optimization Problems (MaOPs). To overcome this limitation, researches are investigating multi-swarm approaches. Multi-swarm is a very interesting approach that allows the decomposition of different aspects of the problem and each swarm could specialize on a dedicate part of the problem. This study explores this idea to create a novel multi-swarm algorithm, called A-Multi, which tackles the main challenge of MaOPs: to convergence towards the true Pareto front and to diversify the obtained solutions covering the entire Pareto front. A-Multi project is based on different swarms use different archiving methods, ones specialized on diversity and others specialized on convergence. The algorithm is evaluated with several MaOPs in terms of both convergence and diversity and the results shows the validity of archive based multi-swarm approach.