Quantum-inspired evolutionary clustering algorithm based on manifold distance
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Quantum and biogeography based optimization for a class of combinatorial optimization
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
An Improved Harmony Search Algorithm with Differential Mutation Operator
Fundamenta Informaticae - Swarm Intelligence
Baldwinian learning in clonal selection algorithm for optimization
Information Sciences: an International Journal
Quantum-inspired evolutionary multicast algorithm
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Quantum-inspired immune clone algorithm and multiscale Bandelet based image representation
Pattern Recognition Letters
Quantum-inspired evolutionary algorithms: a survey and empirical study
Journal of Heuristics
Robust multi-user detection based on quantum bee colony optimisation
International Journal of Innovative Computing and Applications
An Improved Harmony Search Algorithm with Differential Mutation Operator
Fundamenta Informaticae - Swarm Intelligence
A region-based quantum evolutionary algorithm (RQEA) for global numerical optimization
Journal of Computational and Applied Mathematics
Multi-elitist immune clonal quantum clustering algorithm
Neurocomputing
A simple quantum-inspired bee colony algorithm for discrete optimisation problems
International Journal of Computer Applications in Technology
Sub-pixel mapping of remote-sensing imagery based on chaotic quantum bee colony algorithm
International Journal of Computing Science and Mathematics
SAR image segmentation based on quantum-inspired multiobjective evolutionary clustering algorithm
Information Processing Letters
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Based on the concepts and principles of quantum computing, a novel immune clonal algorithm, called a quantum-inspired immune clonal algorithm (QICA), is proposed to deal with the problem of global optimization. In QICA, the antibody is proliferated and divided into a set of subpopulation groups. The antibodies in a subpopulation group are represented by multistate gene quantum bits. In the antibody's updating, the general quantum rotation gate strategy and the dynamic adjusting angle mechanism are applied to accelerate convergence. The quantum NOT gate is used to realize quantum mutation to avoid premature convergences. The proposed quantum recombination realizes the information communication between subpopulation groups to improve the search efficiency. Theoretical analysis proves that QICA converges to the global optimum. In the first part of the experiments, 10 unconstrained and 13 constrained benchmark functions are used to test the performance of QICA. The results show that QICA performs much better than the other improved genetic algorithms in terms of the quality of solution and computational cost. In the second part of the experiments, QICA is applied to a practical problem (i.e., multiuser detection in direct-sequence code-division multiple-access systems) with a satisfying result.