Abstract
This paper presents a modified quantum-inspired particle swarm optimization algorithm (MQPSO) which uses particle swarm optimization algorithm to update quantum coding. The introduction of quantum coding can improve the diversity of algorithm, but may mislead the global search simultaneously. To remedy this drawback, a novel repair operator is developed to improve the search accuracy and efficiency of algorithm. The performance of MQPSO is evaluated and compared with quantum-inspired evolutionary algorithm (QEA), QEA with NOT gate (QEAN) and quantum swarm evolutionary algorithm (QSE) on 0-1knapsack problem and multidimensional knapsack problem. The experimental results demonstrate that the presented repair operator can effectively improve the global search ability of algorithm and MQPSO outperforms QEA, QEAN and QSE on all test benchmark problems in terms of search accuracy and convergence speed.
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Wang, L., Zhang, M., Niu, Q., Yao, J. (2011). A Modified Quantum-Inspired Particle Swarm Optimization Algorithm. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23896-3_51
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DOI: https://doi.org/10.1007/978-3-642-23896-3_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23895-6
Online ISBN: 978-3-642-23896-3
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