Solving for complex functions with high dimensions based on hybrid particle swarm optimization

A new hybrid particle swarm optimization combined with genetic algorithm was proposed in order to solve complex questions with high dimensions and overcome pre-maturity and the weak ability of local search.The global solution can not be found because of the bad results of some dimensions,and it is difficult to find all the best value in each dimension using the usual optimization algorithm.Enlightened by genetic algorithm,the improved algorithm can find the best position through evaluating each dimension and picking out the bad ones,adopting mutation and improving it during the process of evolution.Experimental results on several benchmark complex functions with high dimensions show that the algorithm can rapidly converge at high quality solutions.