A Hybrid Optimization Algorithm based on Particle Swarm Optimization Algorithm and Artificial Bee Colony Algorithm
YE Yi-ma · Journal of Guangxi University for Nationalities · 2013
Concerning the problems of premature and low convergence accuracy for the particle swarm algorithm in search of optimization,update strategies and formulas of the particle algorithm and bee colony algorithm have been made improvements in this paper,respectively.And a new mixed optimization algorithm was proposed based on updated arithmetic method to a particle position using improved particle swarm optimization and bee colony algorithm.Through global optimization tests in 12multiple maximum benchmark functions,the experimental results show the convergence accuracy of the mixed optimization algorithm has improved greatly and the rate of convergence become faster,and the performance is better than improved Comprehensive Learning Particle Swarm Optimization(CLPSO)Algorithm and artificial bee colony algorithm,which can be applied in the optimization for high and low dimensional complicated functions.