An Improved LS-SVM Based on Quantum PSO Algorithm and Its Application
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 02
A new ant colony optimization algorithm for the multidimensional Knapsack problem
Computers and Operations Research
Quantum-Inspired Swarm Evolution Algorithm
CISW '07 Proceedings of the 2007 International Conference on Computational Intelligence and Security Workshops
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Improved Quantum Evolutionary Algorithm Combined with Chaos and Its Application
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
A novel quantum ant colony optimization algorithm
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
Quantum-inspired evolutionary algorithms: a survey and empirical study
Journal of Heuristics
A novel quantum swarm evolutionary algorithm for solving 0-1 knapsack problem
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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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.