Adaptive double chain quantum genetic algorithm for constrained optimization problems

Kong, Haipeng; Li, Ni; Shen, Yuzhong

doi:10.1016/j.cja.2014.12.010

Cited by 19 publications

(10 citation statements)

References 43 publications

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“…• New solutions are obtained and sent to the objective function by using the Euclidian distance of the coefficient and distance between the crickets. a) Convergence graph of present study b) Convergence graph of [39] c) Convergence graph of [45] …”

Section: The Algorithm Of the Cricketsmentioning

confidence: 99%

Cricket behaviour-based evolutionary computation technique in solving engineering optimization problems

Canayaz

Karcı

2015

Appl Intell

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Meta-heuristic algorithms are widely used in various areas such as engineering, statistics, industrial, image processing, artificial intelligence etc. In this study, the Cricket algorithm which is a novel nature-inspired metaheuristic algorithm approach which can be used for the solution of some global engineering optimization problems was introduced. This novel approach is a meta-heuristic method that arose from the inspiration of the behaviour of crickets in the nature. It has a structure for the use in the solution of various problems. In the development stage of the algorithm, the good aspects of the Bat, Particle Swarm Optimization and Firefly were experimented for being applied to this algorithm. In addition to this, because of the fact that these insects intercommunicate through sound, the physical principles of sound propagation in the nature were practiced in the algorithm. Thanks to this, the compliance of the algorithm to real life tried to be provided. This new developed approach was applied on the familiar global engineering problems and the obtained results were compared with the results of the algorithm applied to these problems.

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Section: The Algorithm Of the Cricketsmentioning

confidence: 99%

Cricket behaviour-based evolutionary computation technique in solving engineering optimization problems

Canayaz

Karcı

2015

Appl Intell

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“…Quantum Genetic Algorithm (QGA) is popular among scholars because of its convenience and efficiency. Therefore, many improved algorithms such as real-coded QGA [16], adaptive double-chain QGA [17], and Bloch Quantum Genetic Algorithm (BQGA) [18] have emerged.…”

Section: Introductionmentioning

confidence: 99%

Application of Improved BQGA in Robot Kinematics Inverse Solution

Zhao

2019

Journal of Robotics

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In view of the problem that Bloch Quantum Genetic Algorithm (BQGA) is easy to fall into local optimum, an improved BQGA is proposed. The algorithm can control the step size and the mutation probability in real time in the iterative process, avoiding over the optimal solution and guaranteeing search efficiency. In addition, the improved algorithm further completes the anti-degradation mechanism, which maintains the diversity of the population while preserving the dominant gene to the maximum extent, so that the algorithm is not easy to fall into the local extremum and finally approaches the global optimal solution. The application in the inverse solution of robot kinematics shows that the improved BQGA effectively avoids the premature problem and accelerates the convergence of understanding and the search result is close to the complete solution, which provides a new idea for solving complex nonlinear and multivariate functional equations.

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“…Ye Zhang et al [12] used it to solve the observing and downloading integrated scheduling problem of earth observation satellite. Kong Haipeng et al [13] presented an adaptive double chain quantum genetic algorithm (ADCQGA) for solving constrained optimization problems.…”

Section: Introductionmentioning

confidence: 99%

A Cooperative Evolution of Multiple Operators Based Adaptive Quantum Genetic Algorithm for Network Coding Resources Optimization

Xu¹,

Wang²,

Qu³

2019

JCC

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In order to optimize the network coding resources in a multicast network, an improved adaptive quantum genetic algorithm (AM-QEA) was proposed. Firstly, the optimization problem was translated into a graph decomposition problem. Then the graph decomposition problem was represented by the binary coding, which can be processed by quantum genetic algorithm. At last, a multiple-operators based adaptive quantum genetic algorithm was proposed to optimize the network coding resources. In the algorithm, the individual fitness evaluation operator and population mutation adjustment operator were employed to solve the shortcomings of common quantum genetic algorithm, such as high convergence rate, easy to fall into local optimal solution and low diversity of the population in later stage. The experimental results under various topologies show that the proposed algorithm has the advantages of high multicast success rate, fast convergence speed and strong global search ability in resolving the network coding resource optimization problems.Network coding is an effective technique for improving network throughput, and it also can bring many benefits for the multicast transmission. It allows intermediate network nodes to perform arbitrary mathematical calculations to combine (encode) the input packets, and then output the encoded packets to the downstream nodes [2]. The key problem network coding based multicast technology is how to quickly and efficiently determine the coding nodes of each route in the network. Therefore, the point-to-multipoint multicast rate can reach the upper limit specified by Shannon's "maximum stream-minimum cut" theorem [3]. As an emerging technology, network coding technology has received much attention. However, this problem belongs to the NP-hard problem, and there is currently no clear and efficient solution.

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Adaptive double chain quantum genetic algorithm for constrained optimization problems

Cited by 19 publications

References 43 publications

Cricket behaviour-based evolutionary computation technique in solving engineering optimization problems

Cricket behaviour-based evolutionary computation technique in solving engineering optimization problems

Application of Improved BQGA in Robot Kinematics Inverse Solution

A Cooperative Evolution of Multiple Operators Based Adaptive Quantum Genetic Algorithm for Network Coding Resources Optimization

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