摘要：This paper proposes a hybrid particle swarmapproach called Simple Multi-Ob jective ParticleSwarm Optimizer (SMOPSO) which incorporatesPareto dominance, an elitist policy, and two tech-niques to maintain diversity: a mutation operatorand a grid which is used as a geographical lo ca-tion over ob jective function space.In order to validate our approach we use threewell-known test functions proposed in the spe-cialized literature.Preliminary simulations results are presented andcompared with those obtained with the ParetoArchived Evolution Strategy (PAES) and theMulti-Ob jective Genetic Algorithm 2 (MOGA2).These results also show that the SMOPSO algo-rithm is a promising alternative to tackle multi-ob jective optimization problems
