Quantum-inspired evolutionary algorithms: a survey and empirical study

Zhang, Gexiang

doi:10.1007/s10732-010-9136-0

Cited by 232 publications

(177 citation statements)

References 150 publications

(226 reference statements)

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“…Evolutionary computing (EC) and quantum computing are two research areas involving the use of three algorithms categories [48]. First, the quantum evolutionary algorithms are focused on the application of EC algorithms in a quantum-computing environment.…”

Section: Binarization Methodsmentioning

confidence: 99%

A Binary Cuckoo Search Big Data Algorithm Applied to Large‐Scale Crew Scheduling Problems

et al. 2018

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The progress of metaheuristic techniques, big data, and the Internet of things generates opportunities to performance improvements in complex industrial systems. This article explores the application of Big Data techniques in the implementation of metaheuristic algorithms with the purpose of applying it to decision-making in industrial processes. This exploration intends to evaluate the quality of the results and convergence times of the algorithm under different conditions in the number of solutions and the processing capacity. Under what conditions can we obtain acceptable results in an adequate number of iterations? In this article, we propose a cuckoo search binary algorithm using the MapReduce programming paradigm implemented in the Apache Spark tool. The algorithm is applied to different instances of the crew scheduling problem. The experiments show that the conditions for obtaining suitable results and iterations are specific to each problem and are not always satisfactory.

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Section: Binarization Methodsmentioning

confidence: 99%

A Binary Cuckoo Search Big Data Algorithm Applied to Large‐Scale Crew Scheduling Problems

et al. 2018

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“…The majority of the quantum algorithms use similar primary ideas and approaches, which are based on the qubit representation of the population [14]. Therefore the proposed algorithm is compared to the traditional quantum genetic algorithm: it corresponds to the case with the dimension of quantum system = 2.…”

Section: Qga N Effectiveness Evaluationmentioning

confidence: 99%

Quantum Genetic Algorithm on Multilevel Quantum Systems

Tkachuk

2018

Mathematical Problems in Engineering

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We present a new evolutionary algorithm on the basis of quantum computations technology for solving optimization problems. The algorithm is built using many-valued quantum logic concept, which is more prospective from the computing power's point of view. We compare the suggested algorithm to the traditional quantum genetic algorithm to demonstrate its high effectiveness on the example of test function global optimization problems. The advantages can be observed in the running time, the convergence speed, and the solution precision. The proposed implementation for the algorithm of quantum gate operator has an adaptive nature and does not require a lookup table. The role and the influence mechanism of the quantum disaster operator on the proposed algorithm effectiveness are also analyzed.

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“…Quantum-inspired evolutionary algorithm (QEA) is one of the well-known stochastic meta-heuristics, integrating quantum computation concept into genetic algorithm (GA) [11]. Without explicit population, QEA records the estimated distribution of promising individuals in the search space using quantum-bit representation.…”

Section: Introductionmentioning

confidence: 99%

A quantum inspired evolutionary algorithm for dynamic multicast routing with network coding

Xing

et al. 2016

2016 16th International Symposium on Communications and Information Technologies (ISCIT)

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. Abstract-this paper studies and models the multicast routing problem with network coding in dynamic network environment, where computational and bandwidth resources are to be jointly optimized. A quantum inspired evolutionary algorithm (QEA) is developed to address the problem above, where a restart scheme is devised for well adapting QEA for tracing the ever-changing optima in dynamic environment. Experimental results show that the proposed QEA outperforms a number of existing evolutionary algorithms in terms of the best solution obtained.

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Quantum-inspired evolutionary algorithms: a survey and empirical study

Cited by 232 publications

References 150 publications

A Binary Cuckoo Search Big Data Algorithm Applied to Large‐Scale Crew Scheduling Problems

A Binary Cuckoo Search Big Data Algorithm Applied to Large‐Scale Crew Scheduling Problems

Quantum Genetic Algorithm on Multilevel Quantum Systems

A quantum inspired evolutionary algorithm for dynamic multicast routing with network coding

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