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The main difference between an original PSO (single-objective) with a multi-objective PSO (MOPSO) is the local guide (global best solution) distribution must be redefined in order to obtain a set of non-dominated solutions (Pareto front). In MOPSO, the selection of local guide for particles will direct affect the performance of finding Pareto optimum. This paper presents a local guide assignment strategy for MOPSO called cross-searching strategy (CSS) which will distribute suitable local guides for particles to lead them toward to Pareto front and also keeping diversity of solutions. Experiments were conducted on several test functions and metrics from the standard literature on evolutionary multi-objective optimization. The results demonstrate good performance of the CSS for MOPSO in solving multi-objective problems when compare with recent approaches of multi-objective optimizer.

Multi-objective optimization (MO) has been an active area of research in the last two decades. In multi-objective genetic algorithm (MOGA), the quality of newly generated offspring of the population will directly affect the performance of finding the Pareto optimum. In this paper, an improved MOGA, named SMGA, is proposed for solving multi-objective optimization problems. To increase efficiency during solution searching, an effective mutation named sharing mutation is adopted to generate potential offspring. Experiments were conducted on CEC-09 MOP test problems. The results show that the proposed method exhibits better performance when solving these benchmark problems compared to related multi-objective evolutionary algorithms (MOEA).

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