An Improved Quantum Evolutionary Algorithm with 2-Crossovers

Zhang, Xing; Duan, Haibin; Xu, Chunfang

doi:10.1007/978-3-642-01507-6_83

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…In addition, the rotation angle and direction are solved through multi-objective optimization, then these parameters are used in QEA optimization process in [22]. Quantum rotation gates are improved using twopoint crossover operator in [23] to ensure current solutions converge to chromosomes with higher fitness. A new rotation angle is defined and adaptively adjusted in [24] according to the evolution generations.…”

Section: I) Introducing New Operatorsmentioning

confidence: 99%

Quantum Optimization and Quantum Learning: A Survey

Tian

Liu

et al. 2020

IEEE Access

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Quantum mechanism, which has received widespread attention, is in continuous evolution rapidly. The powerful computing power and high parallel ability of quantum mechanism equip the quantum field with broad application scenarios and brand-new vitality. Inspired by nature, intelligent algorithm has always been one of the research hotspots. It is a frontier interdisciplinary subject with a perfect integration of biology, mathematics and other disciplines. Naturally, the idea of combining quantum mechanism with intelligent algorithms will inject new vitality into artificial intelligence system. This paper lists major breakthroughs in the development of quantum domain firstly, then summarizes the existing quantum algorithms from two aspects: quantum optimization and quantum learning. After that, related concepts, main contents and research progresses of quantum optimization and quantum learning are introduced respectively. At last, experiments are conducted to prove that quantum intelligent algorithms have strong competitiveness compared with traditional intelligent algorithms and possess great potential by simulating quantum computing. INDEX TERMS Quantum optimization, quantum learning, quantum evolutionary algorithm (QEA), quantum particle swarm algorithm (QPSO), quantum immune clonal algorithm (QICA), quantum neural network (QNN), quantum clustering (QC).

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Section: I) Introducing New Operatorsmentioning

confidence: 99%

Quantum Optimization and Quantum Learning: A Survey

Tian

Liu

et al. 2020

IEEE Access

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“…𝐸(1) 𝐷( 2) 𝐶( 3) 𝐵( 4) 𝐴( 5) 𝐸( 6) 𝐷 (7) The whole interference crossover operation can make full use of the information in the chromosome, [10] improve the unilateralism of classical crossover and avoid premature convergence and stagnation problems.…”

mentioning

confidence: 99%

Improved Quantum Evolutionary Computation Based on Particle Swarm Optimization and Two-Crossovers

Duan¹,

Zhi-Hui²

2009

Chinese Phys. Lett.

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A quantum evolutionary computation (QEC) algorithm with particle swarm optimization (PSO) and twocrossovers is proposed to overcome identified limitations. PSO is adopted to update the Q-bit automatically, and two-crossovers are applied to improve the convergence quality in the basic QEC model. This hybrid strategy can effectively employ both the ability to jump out of the local minima and the capacity of searching the global optimum. The performance of the proposed approach is compared with basic QEC on the standard unconstrained scalable benchmark problem that numerous hard combinatorial optimization problems can be formulated. The experimental results show that the proposed method outperforms the basic QEC quite significantly.

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Improved Quantum Particle Swarm Optimization by Bloch Sphere

Duan

Liao

et al. 2010

Lecture Notes in Computer Science

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An Improved Quantum Evolutionary Algorithm with 2-Crossovers

Cited by 5 publications

References 3 publications

Quantum Optimization and Quantum Learning: A Survey

Quantum Optimization and Quantum Learning: A Survey

Improved Quantum Evolutionary Computation Based on Particle Swarm Optimization and Two-Crossovers

Improved Quantum Particle Swarm Optimization by Bloch Sphere

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