Particle Swarm Optimization with Novel Processing Strategy and its Application

The loss of population diversity is one of main reasons which lead standard particle swarm optimization (SPSO) to suffer from the premature convergence when solving complex multimodal problems. In SPSO, the personal experience and sharing experience are processed with a completely random strategy. It is still an unsolved problem whether the completely random processing strategy is good for maintaining the population diversity. To study this problem, this paper presents a correlation PSO model in which a novel correlative strategy is used to process the personal experience and sharing experience. The relational expression between the correlation coefficient and population diversity is developed through theoretical analysis. It is found that the processing strategy with positive linear correlation is helpful to maintain the population diversity. Then a positive linear correlation PSO, PLCPSO, is proposed, where particles adopt the positive linear correlation strategy to process the personal experience and sharing experience. Finally, PLCPSO has been applied to solve single-objective and multi-objective optimization problems. The experimental results show that PLCPSO is a robust effective optimization method for complex optimization problems