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Multi-Objective Problems (MOPs) presents two or more objective functions to be simultaneously optimized. MOPs presenting more than three objective functions are called Many-Objective Problems (MaOPs) and pose challenges to optimization algorithms. Multi-objective Particle Swarm Optimization (MOPSO) is a promising meta-heuristic to solve MaOPs. Previous works have proposed different leader selection...
A well-known problem faced by Multi-Objective Particle Swarm Optimization Algorithms (MOPSO) is the deterioration of its search ability when the number of objectives scales up. In the literature some techniques were proposed to overcome these limitations, however, most of them focuses on alternatives to the non-domination relation. In this work, a different direction is explored, and some specific...
Multi-Objective Particle Swarm Optimization (MOPSO) is a population based multi-objective meta-heuristic inspired on animal swarm intelligence. It is used to solve several Multi-Objective Optimization Problems (MOPs), problems with more than one objective function. However, Multi-Objective Evolutionary Algorithms (MOEAs), including MOPSO, have some limitations when the number of objective grows. Many-Objective...
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