Parallel conjugate gradient-particle swarm optimization and the parameters design based on the polygonal fuzzy neural network

Simple binary coded genetic algorithm (GA) and particle swarm optimization (PSO) fall easily into local minimums and fail to find the global optimal solution to the algorithm. Thus, the development of a hybrid algorithm between GA and PSO is urgently demanded. In this paper, a three-layer polygonal fuzzy neural network (PFNN) model and its error function are first given by the arithmetic operations of the polygonal fuzzy numbers. Second, the random sequences are constructed by a chaos random generator, these random sequences are used as the initial population of chaos GA and the optimal individuals for sub-populations gained by chaos search are used as the initial population of PSO, and then an new parallel conjugate gradient-particle swarm optimization (PCG-PSO) is designed. Finally, a case study shows the proposed parallel CG-PS algorithm not only avoids dependence of traditional GA on initial values and overcomes the poor global optimization capability of traditional PSO, but also possesses advantages of rapid convergence and high stability.