A novel quantum swarm evolutionary algorithm and its applications

Wang, Yan; Feng, Xiaoyue; Huang, Yanxin; Pu, Dongbing; Zhou, Wengang; Liang, Yanchun; Zhou, Chunguang

doi:10.1016/j.neucom.2006.10.001

Cited by 144 publications

(81 citation statements)

References 6 publications

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“…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: 99%

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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: Bqieahmentioning

confidence: 99%

“…(b) QIEApso bQIEApso is the result of the interplay between bQIEA and PSO, studied in Pan et al (2007), Wang et al (2005dWang et al ( , 2007c, Yu et al (2006), Huang et al (2007). The pseudocode algorithm for bQIEApso is given in Fig.…”

Section: (A) Bqieaimentioning

confidence: 99%

Quantum-inspired evolutionary algorithms: a survey and empirical study

2010

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“…[11] takes the concepts from both QEA [93] and PSO [4]. Similar to PSO's swarm intelligent concept, quantum swarms are represented using Q-bits.…”

Section: Quantum-mechanics-based Algorithmsmentioning

confidence: 99%

“…After half a decade later, in 2002, Han and Kim proposed Quantum-Inspired Evolutionary Algorithm (QEA). In 2004 Quantum-Inspired Particle Swarm Optimization (QPSO) was proposed by Sun et al [10] and in 2007 another swarm-based Quantum Swarm Evolutionary Algorithm (QSE) was proposed by Wang et al [11]. Apart from the quantum mechanics, other principles and theorems of physics also begun to draw the attention of researchers.…”

Section: Introductionmentioning

confidence: 99%

Physics-Inspired Optimization Algorithms: A Survey

Biswas

Mishra

Tiwari

et al. 2013

Journal of Optimization

109

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Natural phenomenon can be used to solve complex optimization problems with its excellent facts, functions, and phenomenon. In this paper, a survey on physics-based algorithm is done to show how these inspirations led to the solution of well-known optimization problem. The survey is focused on inspirations that are originated from physics, their formulation into solutions, and their evolution with time. Comparative studies of these noble algorithms along with their variety of applications have been done throughout this paper.

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“…But ordinary evolutionary and swarm intelligence algorithms can be inspired by quantum computing concepts and some merging and hybrid approaches have been proposed during last decades [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Designing And Comparing Classic Versus Quantum Artificial Bee Colony Algorithm

Manochehri¹,

Alizadegan²

2015

J. Math. Computer Sci.

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Artificial Bee Colony (ABC) algorithm is based on natural behavior of honey bees and has earned good success in optimization area. In this paper a new quantum inspired algorithm that is called Quantum Artificial Bee Colony (QABC) is presented. QABC is a general method and in this work it is adapted to be applied on Knapsack 0-1 problem. In the experiments QABC is compared with classic ABC and the results present robustness of QABC.

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A novel quantum swarm evolutionary algorithm and its applications

Cited by 144 publications

References 6 publications

Quantum-inspired evolutionary algorithms: a survey and empirical study

Quantum-inspired evolutionary algorithms: a survey and empirical study

Physics-Inspired Optimization Algorithms: A Survey

Designing And Comparing Classic Versus Quantum Artificial Bee Colony Algorithm

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