یک الگوریتم ترکیبی بر اساس ازدحام ذرات و کلونی زنبور عسل مصنوعی برای مسائل بهینه سازی با ابعاد بالا

Particle swarm optimization (PSO) and artificial bee colony (ABC) are new optimization methods that have attracted increasing research interests because of its simplicity and efficiency. However, when being applied to high-dimensional optimization problems, PSO algorithm may be trapped in the local optimal because of its low global exploration efficiency; ABC algorithm has slower convergence speed in some cases because of the lack of powerful local exploitation capacity. In this paper, we propose a hybrid algorithm called PS–ABC, which combines the local search phase in PSO with two global search phases in ABC for the global optimum. In the iteration process, the algorithm examines the aging degree of pbest for each individual to decide which type of search phase (PSO phase, onlooker bee phase, and modified scout bee phase) to adopt. The proposed PS–ABC algorithm is validated on 13 high-dimensional benchmark functions from the IEEE-CEC 2014 competition problems, and it is compared with ABC, PSO, HPA, ABC–PS and OXDE algorithms. Results show that the PS–ABC algorithm is an efficient, fast converging and robust optimization method for solving high-dimensional optimization problems.