Abstract
As for the problems of premature convergence, slow convergence and long computing time in solving complex continuous function optimization by traditional quantum evolutionary algorithm, a dynamic parallel quantum evolutionary algorithm for solving complex continuous function optimization problem is proposed in this paper. Multi population co-evolution is adopted, and each sub-population evolves in different search areas according to their own evolution objectives to form a parallel search mode, which can speed up the algorithm convergence and avoid premature convergence; Quantum computation is introduced into the differential evolution algorithm. In this method, the probability amplitude representation of qubits is applied to the real number coding of chromosomes, the chromosome position is updated by quantum mutation, quantum crossover and quantum selecting operations, the two probability amplitudes of qubits are exchanged by quantum non-gate, and an adaptive operator is introduced to improve the population diversity, It can not only prevent the premature convergence of the algorithm, but also make the algorithm converge faster and improve the problem-solving ability of the optimization algorithm. Taking the function extreme value problem as an example, the effectiveness of the algorithm is verified by this algorithm.
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This research was supported by general scientific research project of education department of Hunan Province in China (21C0338).
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Sun, Y. (2022). Parallel Improved Quantum Evolutionary Algorithm for Complex Optimization Problems. In: Qiu, M., Gai, K., Qiu, H. (eds) Smart Computing and Communication. SmartCom 2021. Lecture Notes in Computer Science, vol 13202. Springer, Cham. https://doi.org/10.1007/978-3-030-97774-0_23
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