A study on the performance of the hybrid optimization method based on artificial bee colony and differential evolution algorithms

Nowadays, the attraction of the optimization techniques based on artificial intelligence has increased the due to obtaining the its successful results on the difficult optimization problems in various areas. The artificial bee colony (ABC) algorithm based on swarm intelligence and the differential evolution (DE) algorithm improved by inspiring the natural biological evolution mechanism is the most popular of the artificial intelligence optimization methods. The exploration ability of these optimization techniques is generally very good. However, there is a disadvantage of these algorithms for the difficult optimization problem. The exploitation capability of the algorithms is usually not sufficient. In this study, the investigation results of the performance for an improved hybrid optimization method (HOM) to overcome their disadvantages occurred in optimization process of the difficult problems by combining ABC with DE algorithms are presented. While the improved algorithm keeps the exploration ability by retaining their standard updating strategy in the employed bees step, it enhances the exploitation skill by using powerful mutation and crossover strategies of the DE algorithm into onlooker bees step. The HOM having very high convergence speed and performance is a novel and robust optimization method based on meta-heuristic approach. In order to appraise the performance of the HOM in the testing step, a sequence of the classical benchmark function has been utilized and the performance results of the proposed method are compared with the performance of the standard DE, ABC algorithms. The reached numerical results in this study illustrated that the search performance of the presented HOM is better than the standard ABC, DE algorithms.