Abstract
Quantum evolutionary algorithm (QEA) is proposed on the basis of the concept and principles of quantum computing, which is a classical meta-heuristic algorithm for the approximate solution of combinatorial optimization problems that has been inspired by the principles of evolution of living organisms in nature. QEA has strong robustness and is easy to combine with other methods in optimization, but it has the shortcomings of stagnation that limits the wide application to the various areas. In this paper, a hybrid QEA with Artificial Bee Colony (ABC) optimization was proposed to overcome the above-mentioned limitations. ABC is adopted to increase the local search capacity as well as the randomness of the populations, which can help QEA jump out of the premature convergence and find the optimal value. The proposed algorithm is tested with the Benchmark optimization problem, and the experimental results demonstrate that the proposed QEA is feasible and effective in solving complex optimization problems.
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