An Enhanced Hybrid Quadratic Particle Swarm Optimization

Particle swarm optimization (PSO) is swarm-based stochastic optimization originating from artificial life and evolutionary computation. The algorithm completes the optimization through following the personal best solution of each particle and the global best value of the whole swarm. This paper improved the standard PSO´s evolution equation on the foundation of analyzing standard PSO´s model and its mechanisms, then presents a quadratic PSO and gives a strategy of self-adapting quadratic PSO parameters through comparing quadratic PSO with PSO and analyzing the impact that the parameters have on the performance of algorithm. Further, this paper presents a hybrid particle optimization combined their advantages on the basis of comparing quadratic PSO with PSO. The simulation illustrates the quadratic PSO improves the performance of the PSO and the self-adapting parameters strategy is better than the fixed parameters, and the hybrid PSO outperformed both standard PSO and quadratic PSO, the experimental results show that the methods are correct and efficient