Chaotic quantum behaved particle swarm optimization algorithm for solving nonlinear system of equations

Turgut, Oğuz Emrah; Turgut, Mert Sinan; Çoban, Mustafa Turhan

doi:10.1016/j.camwa.2014.06.013

Cited by 69 publications

(53 citation statements)

References 72 publications

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“…Because of the uncertainty principle of quantum mechanics, the position and velocity of a particle cannot be determined synchronously in quantum world. New state of each particle is determined by wave function ψ(x,t) [14]. In literature [15], Clerc and Kennedy analyze the trajectory of each particle in PSO and assume that each particle can converge to its local attractor which can guarantee the global convergence.…”

Section: Quantum Behaved Particle Swarm Optimizationmentioning

confidence: 99%

A Chaotic Quantum Behaved Particle Swarm Optimization Algorithm for Short-term Hydrothermal Scheduling

Chen¹,

Huang²,

Zhi³

2017

TOEEJ

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This study proposes a novel chaotic quantum-behaved particle swarm optimization (CQPSO) algorithm for solving shortterm hydrothermal scheduling problem with a set of equality and inequality constraints. In the proposed method, chaotic local search technique is employed to enhance the local search capability and convergence rate of the algorithm. In addition, a novel constraint handling strategy is presented to deal with the complicated equality constrains and then ensures the feasibility and effectiveness of solution. A system including four hydro plants coupled hydraulically and three thermal plants has been tested by the proposed algorithm. The results are compared with particle swarm optimization (PSO), quantum-behaved particle swarm optimization (QPSO) and other population-based artificial intelligence algorithms considered. Comparison results reveal that the proposed method can cope with short-term hydrothermal scheduling problem and outperforms other evolutionary methods in the literature.

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Section: Quantum Behaved Particle Swarm Optimizationmentioning

confidence: 99%

A Chaotic Quantum Behaved Particle Swarm Optimization Algorithm for Short-term Hydrothermal Scheduling

Chen¹,

Huang²,

Zhi³

2017

TOEEJ

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“…It combines PSO algorithm with the quantum mechanic and has better global searching ability than that of the PSO algorithm [21,22]. Since the QPSO algorithm was proposed, many researchers devoted to apply it for practical applications [23][24][25][26] or improve the algorithm itself [27][28][29][30][31]. For example, Mariani et al [29] proposed a novel chaotic QPSO algorithm for the image matching, but this algorithm only introduces chaos variables into the particle position initialization.…”

Section: Introductionmentioning

confidence: 99%

“…Mariani et al [29] proposed a QPSO algorithm combined with the Zaslavskii chaotic map and applied it to the optimization of shell and tube heat exchangers. en, Turgut et al [30] proposed another chaotic QPSO algorithm for solving nonlinear system of equations, and the benchmark function experiments are proved that the algorithm using Logistic chaotic map has the best performance. On the basis of Ref.…”

Section: Introductionmentioning

confidence: 99%

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Improved Chaotic Quantum-Behaved Particle Swarm Optimization Algorithm for Fuzzy Neural Network and Its Application

Peng

Lei

Yang

et al. 2020

Mathematical Problems in Engineering

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Traditional fuzzy neural network has certain drawbacks such as long computation time, slow convergence rate, and premature convergence. To overcome these disadvantages, an improved quantum-behaved particle swarm optimization algorithm is proposed as the learning algorithm. In this algorithm, a new chaotic search is introduced, and benchmark function experiments prove it outperforms the other five existing algorithms. Finally, the proposed algorithm is presented as the learning algorithm for Takagi-Sugeno fuzzy neural network to form a new neural network, and it is utilized in the water quality evaluation of Dongjiang Lake of Hunan province. Simulation results demonstrated the effectiveness of the new neural network.

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“…Meanwhile, experiments prove that QPSO outperforms the standard PSO and can be a very promising algorithm with its strong global convergence performance. Hence, QPSO has been applied to all kinds of¯elds, such as global optimization of electromagnetic devices, 9 design of IIR digital¯lters, 37 optimization of benchmark functions, 38 route planning, 11,12 load°ow problems, 8 nonlinear system of equations, 43 Financial forecasting, 2 medical image segmentation, 18 adaptive hybrid rule network. 21 Based on in-depth studies of the corresponding features of IWO and QPSO, this paper integrates both of their ideas, and proposes the quantum-behaved invasive weed optimization (QIWO) in order to overcome the defects, such as the strong likelihood of falling into local optimum easily and lower accuracy.…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Effective Algorithm for Large Scale Global Optimization Problems

Kan-chao

Peng

Ouyang

2015

Int. J. Patt. Recogn. Artif. Intell.

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Invasive weed optimization (IWO) algorithm and quantum-behaved particle swarm optimization (QPSO) algorithm are inclined to fall into local optimum with lower convergence accuracy when separately used to deal with large scale global optimization (LSGO) problems. In order to fully utilize the advantages of these two intelligent algorithms and complement each other, following the idea of portfolio optimization, this paper correspondingly adjusts and improves the quantum models of IWO and QPSO, organically integrates the two algorithms, and proposes the quantum-behaved invasive weed optimization (QIWO) algorithm. This mixed algorithm can achieve the purpose of information exchange and cooperative search through alternate search enables the make algorithm converge to the optimal solution quickly, properly overcoming the defects of falling into local optimum and premature convergence. Test results of 20 LSGO functions show that compared with other algorithms, QIWO has stronger global optimization capability, faster convergence speed and higher convergence accuracy.

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Chaotic quantum behaved particle swarm optimization algorithm for solving nonlinear system of equations

Cited by 69 publications

References 72 publications

A Chaotic Quantum Behaved Particle Swarm Optimization Algorithm for Short-term Hydrothermal Scheduling

A Chaotic Quantum Behaved Particle Swarm Optimization Algorithm for Short-term Hydrothermal Scheduling

Improved Chaotic Quantum-Behaved Particle Swarm Optimization Algorithm for Fuzzy Neural Network and Its Application

An Efficient and Effective Algorithm for Large Scale Global Optimization Problems

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