A new real-coded quantum-inspired evolutionary algorithm for continuous optimization |
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| Affiliation: | 1. The Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China;2. The School of Statistics, University of Minnesota, Minneapolis, MN 55455, USA;3. The Center for Cybersecurity, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China;4. The College of Information Science and Engineering, Hunan University, Changsha 410082, PR China;1. University Institute of Technology, Burdwan, WB, India;2. Techno India College of Technology, Kolkata, WB, India;3. Tanta University, Tanta, Egypt;4. Aurel Vlaicu University of Arad, Arad, Romania |
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| Abstract: | This paper presents a recursive deepening hybrid strategy to solve real-parameter optimization problems. It couples a local search technique with a quantum-inspired evolutionary algorithm. In order to adapt the quantum-inspired evolutionary algorithm for continuous optimization without losing the states superposition property, a suitable sampling of the search space that tightens recursively and an integration of a uniformly generated random part after measurement have been utilized. The use of local search provides, for each search window, a good exploitation of the quantum inspired generated solution's neighbourhood. The proposed approach has been tested through the reference black-box optimization benchmarking framework. The comparison of the obtained results with those of some state-of-the-art algorithms has shown its actual effectiveness. |
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| Keywords: | Quantum-inspired evolutionary algorithms Local search Continuous optimization Exploration Exploitation |
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