An efficient hybrid multi-objective particle swarm optimization with a multi-objective dichotomy line search

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; the MOLS is periodically activated for fast local search to converge toward the Pareto front. The exploratory capabilities are enhanced more efficiently by keeping a desirable balance between global search and local search, so as to ensure sufficient diversity and well distribution amongst the solutions of the non-dominated fronts, while retaining at the same time the convergence to the Pareto-optimal front. Comparing MOLS-MOPSO with various state-of-the-art multi-objective optimization algorithms developed recently, the comparative study shows the effectiveness of MOLS-MOPSO, which not only assures a better convergence to the Pareto frontier but also illustrates a good diversity and distribution of solutions.