Studying the Performance of Quantum Evolutionary Algorithm Based on Immune Theory

You, Xiaoming; Liu, Sheng; Shuai, Dianxun

doi:10.1007/978-3-540-72590-9_161

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…The bQIEAi was studied in Li et al (2004aLi et al ( , 2004cLi et al ( , 2005b, Li and Jiao (2005, 2007, 2008, You et al (2006aYou et al ( , 2006bYou et al ( , 2006cYou et al ( , 2007, Du et al (2007), , Jiao and Li (2005), Bi and Jin (2007) (Li et al 2004c). Based on prior knowledge about the problem, a vaccination is used to modify certain genes of some genotype individuals, and then the immune selection is implemented using the following two processes.…”

Section: (A) Bqieaimentioning

confidence: 96%

“…To improve bQIEA performance, other optimization techniques have been introduced (Li et al 2004a(Li et al , 2004c(Li et al , 2005bWang et al 2005aWang et al , 2005bWang et al , 2005dWang et al , 2007aWang et al , 2007cYou et al 2006aYou et al , 2006bYou et al , 2006cYou et al , 2007Li and Jiao 2005, 2007, 2008Jiao and Li 2005;Bi and Jin 2007;Huang et al 2007;Malossini et al 2008;Pan et al 2007;Wang 2006, 2007;Shu and He 2007;Qin et al 2007;Yu et al 2006;Su et al 2010;Wu et al 2009;Wang and Li 2010;Jiao et al 2008;Niu et al 2009;Zhang et al 2008;Du et al 2007). The class bQIEAh concentrates on the interactions between bQIEA and CGAs, immune algorithms and particle swarm optimization (PSO).…”

Section: Bqieahmentioning

confidence: 98%

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Quantum-inspired evolutionary algorithms: a survey and empirical study

2010

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Quantum-inspired evolutionary algorithms, one of the three main research areas related to the complex interaction between quantum computing and evolutionary algorithms, are receiving renewed attention. A quantum-inspired evolutionary algorithm is a new evolutionary algorithm for a classical computer rather than for quantum mechanical hardware. This paper provides a unified framework and a comprehensive survey of recent work in this rapidly growing field. After introducing of the main concepts behind quantum-inspired evolutionary algorithms, we present the key ideas related to the multitude of quantum-inspired evolutionary algorithms, sketch the differences between them, survey theoretical developments and applications that range from combinatorial optimizations to numerical optimizations, and compare the advantages and limitations of these various methods. Finally, a small comparative study is conducted to evaluate the performances of different types of quantum-inspired evolutionary algorithms and conclusions are drawn about some of the most promising future research developments in this area.

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Section: (A) Bqieaimentioning

confidence: 96%

Section: Bqieahmentioning

confidence: 98%

Quantum-inspired evolutionary algorithms: a survey and empirical study

2010

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“…Optimal solution is obtained through the mechanism in which cross-mutation is accomplished by immune cells. Memory cells are produced while similar antibodies are suppressed in [89], [90]. The memory strategy in [91] realizes the information transfer during the courses of evolution.…”

Section: ) Research Progress A: Improvement On 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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“…Quantum gate (Q-gate) U (t) is a variable operator of QEA, it can be chosen according to the problem. A common rotation gate used in QEA is as follows 3 :…”

Section: Quantum Computing and Related Backgroundmentioning

confidence: 99%

Quantum Dynamic Mechanism-based Parallel Ant Colony Optimization Algorithm

You¹,

Liu²,

Wang³

2010

IJCIS

Self Cite

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A novel Parallel Ant Colony Optimization Algorithm based on Quantum dynamic mechanism for traveling salesman problem (PQACO) is proposed. The use of the improved 3-opt operator provides this methodology with superior local search ability; several antibody diversification schemes were incorporated into the PQACO in order to improve the balance between exploitation and exploration. We describe the quantum dynamic mechanism and analysis the technology of improving performance, the efficiency of the approach has been illustrated by applying to TSP benchmark instances Chn144.

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Studying the Performance of Quantum Evolutionary Algorithm Based on Immune Theory

Cited by 4 publications

References 9 publications

Quantum-inspired evolutionary algorithms: a survey and empirical study

Quantum-inspired evolutionary algorithms: a survey and empirical study

Quantum Optimization and Quantum Learning: A Survey

Quantum Dynamic Mechanism-based Parallel Ant Colony Optimization Algorithm

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