The Improved Intelligent Optimal Algorithm Based on the Artificial Bee Colony Algorithm and the Differential Evolution Algorithm

This paper proposes an improved ABC algorithm to avoid the phenomenon of premature convergence of the basic artificial bee colony (ABC) algorithm. By combining the mutation operators of differential evolution (DE) algorithm, the new search equations of employed bees and onlookers are designed. Besides, a selective probability is leaded into the former one and a development coefficient is added in the latter one. In order to demonstrate the availability of the proposed ABC algorithm, simulations are conducted on a set of benchmark functions. The simulation results show that the improved ABC algorithm performs better than the basic ABC algorithm, DE algorithm and particle swarm optimization (PSO) algorithm.