Abstract
This study proposes a novel quantum evolutionary algorithm called four-chain quantum-inspired evolutionary algorithm (FCQIEA) based on the four gene chains encoding method. In FCQIEA, a chromosome comprises four gene chains to expand the search space effectively and promote the evolutionary rate. Different parameters, including rotational angle and mutation probability, have been analyzed for better optimization. Performance comparison with other quantum-inspired evolutionary algorithms (QIEAs), evolutionary algorithms, and different chains of QIEA demonstrates the effectiveness and efficiency of FCQIEA.
Highlights
Quantum computation is based on the principal concepts of the quantum theory [1, 2]
Li [74] further proposed a convenient method to determine the direction of the rotational angle; in particular, a real qubits encoding method can be used to avoid binary coding and the double-chains quantum evolutionary algorithm, which is characterized by each qubit that is regarded as two coordinate genes
Our results revealed that common quantum-inspired evolutionary algorithm (CQIEA) is the most inefficient of the three algorithms
Summary
Quantum computation is based on the principal concepts of the quantum theory [1, 2]. Numerous researchers have devoted increasing interests to quantum computation, a novel interdisciplinary field that covers quantum mechanics and information science. The binary observation QIEA (bQIEA), based on a probability optimization algorithm according to quantum computation concept and theory, was initially proposed in 2002 by Han and Kim to solve combinatorial optimization problems [4]. Li [74] further proposed a convenient method to determine the direction of the rotational angle; in particular, a real qubits encoding method can be used to avoid binary coding and the double-chains quantum evolutionary algorithm, which is characterized by each qubit that is regarded as two coordinate genes. Li [75] studied the appropriate rotational angle size between two rotational angles to obtain the best optimization effects and proposed the three-chain quantum evolutionary algorithm, which is characterized by bloch coordinates as the gene chains, such that each chromosome has three gene chains. We compared the performance of the proposed FCQIEA with other QIEAs and other EAs
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