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 evaluation of living organisms in nature. QEA has strong robustness and 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 2-crossovers was proposed to overcome the above-mentioned limitations. Considering the importance of randomization, 2-crossovers were applied to improve the convergence quality in the basic QEA model. In this way, the new-born individual after each updating can to help the population jump out of premature convergence. The proposed algorithm is tested with the Benchmark optimization problem, and the experimental results demonstrate that the proposed QEA is a feasible and effective in solving complex optimization problems.
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Xing, Z., Duan, H., Xu, C. (2009). An Improved Quantum Evolutionary Algorithm with 2-Crossovers. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5551. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01507-6_83
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DOI: https://doi.org/10.1007/978-3-642-01507-6_83
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