Recently more research works are focused on multi-objective particle swarm optimization algorithm (MOPSO) due to its ability of global and local search for solving multi-objective optimization problems (MOOPs) however, most of existing MOPSOs cannot achieve satisfactory results in solution quality. This paper proposes an efficient hybrid multi-objective particle swarm optimization with a multi-objective dichotomy line search (MOLS), named MOLS-MOPSO, to deal with such problem. MOLS-MOPSO combines an effective particle updating strategy with the local search of MOLS. The effective particle updating strategy is used for global search to deal with premature convergence and diversity maintenance within the swarm

1-s2.0-S0377042714005354-main.pdf
1-s2.0-S0925231215002210-main.pdf

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