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-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