A Comprehensive Learning Quantum-Inspired Evolutionary Algorithm

This paper proposes a Comprehensive Learning Quantum-Inspired Evolutionary Algorithm (CLQEA) by introducing the philosophy of comprehensive learning into quantum-inspired evolutionary algorithms. In CLQEA, each individual in a population learns not only from its own best historical solution searched, but also from the best solutions that other individuals found. This idea is very helpful to enhance population diversity through applying a group of elite individuals to perform Q-gates to produce offspring. Extensive experiments carried out on knapsack problems with various items show that CLQEA outperforms several QIEAs recently reported in the literature.

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