A hybrid artificial bee colony optimization and quantum evolutionary algorithm for continuous optimization problems.
In this paper, a novel hybrid Artificial Bee Colony (ABC) and Quantum Evolutionary Algorithm (QEA) is proposed for solving continuous optimization problems. ABC is adopted to increase the local search capacity as well as the randomness of the populations. In this way, the improved QEA can jump out of the premature convergence and find the optimal value. To show the performance of our proposed hybrid QEA with ABC, a number of experiments are carried out on a set of well-known Benchmark continuous optimization problems and the related results are compared with two other QEAs: the QEA with classical crossover operation, and the QEA with 2-crossover strategy. The experimental comparison results demonstrate that the proposed hybrid ABC and QEA approach is feasible and effective in solving complex continuous optimization problems.
Duan
Hai-Bin
HB
National Key Laboratory of Science and Technology on Holistic Control, School of Automation Science and Electrical Engineering, Beihang University, No. 37, Xueyuan Road, Haidian District, Beijing, 100191, PR China. hbduan@buaa.edu.cn
Xu
Chun-Fang
CF
Xing
Zhi-Hui
ZH
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001516:Bees / Q000502:physiology*; D001522:Behavior, Animal / Q000502:physiology; D005075:Biological Evolution; D002678:Chimera; D003198:Computer Simulation; D003661:Decision Support Techniques; D008954:Models, Biological; D017711:Nonlinear Dynamics