A new hierarchical approach for MOPSO based on dynamic subdivision of the population using Pareto fronts

This paper introduces a new hierarchical architecture for multi-objective optimization. Based on the concept of Pareto dominance, the process of implementation of the algorithm consists of two stages. First, when executing a multiobjective Particle S warm Optimization (MOPSO), a ranking operator is applied to the population in a predefined iteration to build an initial archive Using ε-dominance. Second, several runs will be based on a dynamic number of sub-populations. Those populations, having a fixed size, are generated from the Pareto fronts witch are resulted from ranking operator. A comparative study with other algorithms existing in the literature has shown a better performance of our algorithm referring to some most used benchmarks.