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A new real-coded quantum-inspired evolutionary algorithm for continuous optimization
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DOI：10.1016/j.asoc.2017.07.046.png)
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Abstract
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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 super-position 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. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Quantum-inspired evolutionary algorithms
Local search
Continuous optimization
Exploration
Exploitation
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