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.
Int J Neural Syst. 2010 Feb;20(1):39-50. doi: 10.1142/S012906571000222X.
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.
在本文中，提出了一种新颖的混合人工蜂群（ABC）和量子进化算法（QEA），用于解决连续优化问题。ABC 被用来提高种群的局部搜索能力和随机性。通过这种方式，改进的 QEA 可以跳出过早收敛并找到最优值。为了展示我们提出的混合 QEA 与 ABC 的性能，在一组著名的基准连续优化问题上进行了大量实验，并将相关结果与另外两种 QEA 进行了比较：具有经典交叉操作的 QEA 和具有 2-交叉策略的 QEA。实验比较结果表明，所提出的混合 ABC 和 QEA 方法在解决复杂连续优化问题时是可行和有效的。
