A Novel Immune Quantum-Inspired Genetic Algorithm

Li, Ying; Zhang, Yanning; Cheng, Yinglei; Jiang, Xiaoyue; Zhao, Ran

doi:10.1007/11539902_25

Cited by 14 publications

(6 citation statements)

References 2 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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“…where ( , ) and ( , ) are the probability amplitudes of the th qubit in chromosome before and after the quantum rotating gates updating, respectively; is the rotating angle, and the value and the sign of are determined by the adjustment strategy [17,18]. is static in conventional quantum genetic algorithm.…”

Section: ⋅⋅⋅ ⋅⋅⋅mentioning

confidence: 99%

Improved Quantum Genetic Algorithm in Application of Scheduling Engineering Personnel

Wang

Liu

et al. 2014

Abstract and Applied Analysis

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To verify the availability of the improved quantum genetic algorithm in solving the scheduling engineering personnel problem, the following work has been carried out: the characteristics of the scheduling engineering personnel problem are analyzed, the quantum encoding method is proposed, and an improved quantum genetic algorithm is applied to address the issue. Taking the low efficiency and the bad performance of the conventional quantum genetic algorithm into account, a universal improved quantum genetic algorithm is introduced to solve the scheduling engineering personnel problem. Finally, the examples are applied to verify the effectiveness and superiority of the improved quantum genetic algorithm and the rationality of the encoding method.

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“…In this paper, a rotating strategy with self-adaptive angle is adopted. It can dynamically adjust the value of the rotating angle of the Quantum rotating gates based on the evolutionary process [26]. A large rotating angle is set early in the evolutionary process to quickly facilitate the entire interval and find the region with the optimal values.…”

Section: Self-adaptive Rotating Angle Strategymentioning

confidence: 99%

The Improvement of Quantum Genetic Algorithm and Its Application on Function Optimization

Wang

Liu

Jun

et al. 2013

Mathematical Problems in Engineering

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To accelerate the evolutionary process and increase the probability to find the optimal solution, the following methods are proposed to improve the conventional quantum genetic algorithm: an improved method to determine the rotating angle, the self-adaptive rotating angle strategy, adding the quantum mutation operation and quantum disaster operation. The efficiency and accuracy to search the optimal solution of the algorithm are greatly improved. Simulation test shows that the improved quantum genetic algorithm is more effective than the conventional quantum genetic algorithm to solve some optimization problems.

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A Novel Immune Quantum-Inspired Genetic Algorithm

Cited by 14 publications

References 2 publications

Quantum-inspired evolutionary algorithms: a survey and empirical study

Quantum-inspired evolutionary algorithms: a survey and empirical study

Improved Quantum Genetic Algorithm in Application of Scheduling Engineering Personnel

The Improvement of Quantum Genetic Algorithm and Its Application on Function Optimization

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