Abstract
Particle swarm optimization(PSO) algorithm has been widely applied in solving multi-objective optimization problems(MOPs) since it was proposed. However, PSO algorithms updated the velocity of each particle using a single search strategy, which may be difficult to obtain approximate Pareto front for complex MOPs. In this paper, inspired by the theory of P system, a multi-objective particle swarm optimization (PSO) algorithm based on the framework of membrane system(PMOPSO) is proposed to solve MOPs. According to the hierarchical structure, objects and rules of P system, the PSO approach is used in elementary membranes to execute multiple search strategy. And non-dominated sorting and crowding distance is used in skin membrane for improving speed of convergence and maintaining population diversity by evolutionary rules. Compared with other multi-objective optimization algorithm including MOPSO, dMOPSO, SMPSO, MMOPSO, MOEA/D, SPEA2, PESA2, NSGAII on a benchmark series function, the experimental results indicate that the proposed algorithm is not only feasible and effective but also have a better convergence to true Pareto front.
Highlights
P systems[8], has drawn many researcher attention and was used as a framework to construct hybrid particle swarm optimization (PSO) because it provides an evolutionary procedure by hierarchical membrane structure, objects and rules
Motivated by the successful hybrid PSO and membrane system, this paper proposes a multiobjective particle swarm optimization (PSO) algorithm based on the framework of membrane system(PMOPSO)
In order to solve the complex MOPS in reality, a PMOPSO algorithm based on membrane system is proposed
Summary
P systems[8], has drawn many researcher attention and was used as a framework to construct hybrid PSO because it provides an evolutionary procedure by hierarchical membrane structure, objects and rules. Previous researches in hybrid PSOs for MOPs such as non-dominated sorting PSO(NSPSO)[11] and PS-EA[12] is very competitive with existing multi-objective evolutionary algorithms(MOEAs) These algorithms based on membrane system mainly focused on SOPs though they can achieve better result and improve the performance of PSO. Motivated by the successful hybrid PSO and membrane system, this paper proposes a multiobjective particle swarm optimization (PSO) algorithm based on the framework of membrane system(PMOPSO). 1)According to the hierarchical structure of membrane system, the solutions are updated parallel in the elementary membranes by using the concepts of personal best and global best of PSO This strategy can maintain the search diversity at the same time.
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