Hybrid optimization method through complete Logistic chaoticparticle swarm optimization and genetic algorithm
一种采用完全Logistic混沌的PSO-GA优化方法

In order to improve the optimization performance of particle swarm optimization, this paper proposed a new algorithm called complete Logistic chaotic particle swarm optimization combined with genetic algorithm. Logistic chaos search, which had the property of pseudo-randomness and ergodicity, was applied to the initialization of position and velocity of initial swarm, the optimization of inertia weight, the generation of random constant and the generation of the local optimum neighborhood point. After the particle velocity and position were updated, it embedded genetic algorithm in the complete Logistic chao-tic particle swarm optimization, to perform the operation of selection and crossover. Experimental results with three typical Benchmark functions show that the proposed algorithm is effective, and has better search property and convergence speed.