Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Swarm intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Real-Coded Quantum Inspired Evolution Algorithm Applied to Engineering Optimization Problems
ISOLA '06 Proceedings of the Second International Symposium on Leveraging Applications of Formal Methods, Verification and Validation
A novel hybrid constraint handling technique for evolutionary optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Adaptive-type servo controller utilizing a quantum neural network with qubit neurons
International Journal of Hybrid Intelligent Systems
Binarization based edge detection using universal law of gravity and ant colony optimization
International Journal of Hybrid Intelligent Systems
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Evolutionary Algorithms (EA) have been successfully employed for solving difficult constrained engineering optimization problems. However, EA implementations often suffer from premature convergence due to the lack of proper balance between exploration and exploitation in the search process. This paper proposes a Hybrid Quantum inspired EA, which balances the exploration and exploitation in the search process by adaptively evolving the populations. It employs an adaptive quantum rotation based crossover operator designed by hybridizing a conventional crossover operator with the principles of Quantum Mechanics. The degree of rotation in this operator is determined adaptively. The proposed algorithm does not require either a mutation operator, to avoid premature convergence, or a local heuristic to improve convergence rate. Further, a parameter-tuning free hybrid technique is employed for handling constraints, which overcomes some limitations in the traditional techniques like penalty factor methods, by hybridizing Feasibility Rules method with Adaptive Penalty Factor method. It is implemented by using two populations, each evolving by applying one of the constraints handling techniques and swapping a part of the populations. A standard set of six diverse benchmark engineering design optimization problems have been used for testing the proposed algorithm. The algorithm exhibits superior performance than the existing state-of-the-art approaches.