A quantum behaved particle swarm optimization for flexible job shop scheduling

Singh, Manas Ranjan; Mahapatra, Siba Sankar

doi:10.1016/j.cie.2015.12.004

Cited by 125 publications

(48 citation statements)

References 52 publications

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“…The objective of this development is to enhance the global search in the early part of the optimization and to encourage the particles to converge toward the global optima at the end of the search. Then, the position of particle i at iteration t is updated as shown (13) in our proposed QPSO with weighted mean personal best position and Adaptive Local Attractor (ALA-QPSO).…”

Section: Adaptive Local Attractormentioning

confidence: 99%

“…In (12), σ 2 (t) is the sum of squares of deviations of the particles' fitness values at iteration t, S is the swarm size, and ϕ is a random number uniformly distributed on (0, 1). In (13), α and u have the same meaning as the corresponding parameters in (5), mpbest t d is the weighted mean personal best position for the d−th dimension at iteration t, and its calculation equation is shown as (11); Ala t id is the adaptive local attractor of particle i for the d−th dimension at iteration t.…”

Section: Adaptive Local Attractormentioning

confidence: 99%

“…Unlike PSO, QPSO needs no velocity vectors for particles, and also has fewer parameters to adjust, making it easier to implement. Since QPSO was proposed, it has attracted much attention and different variants of QPSO have been proposed to enhance the performance from different aspects and successfully applied to solve a wide range of continuous optimization problems [9][10][11][12][13][14]. In general, most current QPSO variants can be classified into three categories [15]: the improvement based on operators from other evolutionary algorithms, hybrid search methods, and cooperative methods.…”

Section: Introductionmentioning

confidence: 99%

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Quantum-Behaved Particle Swarm Optimization with Weighted Mean Personal Best Position and Adaptive Local Attractor

Chen¹

2019

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Motivated by concepts in quantum mechanics and particle swarm optimization (PSO), quantum-behaved particle swarm optimization (QPSO) was proposed as a variant of PSO with better global search ability. In this paper, a QPSO with weighted mean personal best position and adaptive local attractor (ALA-QPSO) is proposed to simultaneously enhance the search performance of QPSO and acquire good global optimal ability. In ALA-QPSO, the weighted mean personal best position is obtained by distinguishing the difference of the effect of the particles with different fitness, and the adaptive local attractor is calculated using the sum of squares of deviations of the particles’ fitness values as the coefficient of the linear combination of the particle best known position and the entire swarm’s best known position. The proposed ALA-QPSO algorithm is tested on twelve benchmark functions, and compared with the basic Artificial Bee Colony and the other four QPSO variants. Experimental results show that ALA-QPSO performs better than those compared method in all of the benchmark functions in terms of better global search capability and faster convergence rate.

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Section: Adaptive Local Attractormentioning

confidence: 99%

Section: Adaptive Local Attractormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantum-Behaved Particle Swarm Optimization with Weighted Mean Personal Best Position and Adaptive Local Attractor

Chen¹

2019

Information

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“…The critical operation is the operation which its total slack equal to 0. In the example, the critical operation is O 21 , O 11 • The fuzzy machine selection is applied to the target solution in the second mode. This mechanism is use to search for the machine assignment part of the new solution.…”

Section: Recombination Processmentioning

confidence: 99%

Discrete Differential Evolution Algorithm with the Fuzzy Machine Selection for Solving the Flexible Job Shop Scheduling Problem

Phu-ang¹

2018

IJNDC

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“…en, a new weighted fractional GM(1,1) (WFGM(1,1)) model, which involves the classical GM(1,1), the NIPGM(1,1), and the FGM(1,1) prediction models as special cases, is constructed. In addition, by defining a nonlinear constrained optimization problem for fitted values, the quantum particle swarm optimization (QPSO) method is adopted to find the best parameters [42][43][44].…”

Section: Introductionmentioning

confidence: 99%

A Novel Weighted Fractional GM(1,1) Model and Its Applications

et al. 2020

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Based on the ideas of the new information priority principle and the fractional-accumulation generating operator, in this paper we propose a novel weighted fractional GM(1,1) (WFGM(1,1)) prediction model. In the new model, the original sequence is first transformed by using the weighted fractional-accumulation generating operator, which involves two parameters. With special choices of these parameters, the proposed WFGM(1,1) model reduces to the classical GM(1,1) model and the fractional GM(1,1) (FGM(1,1)) model, as well as the new information priority GM(1,1) (NIPGM(1,1)) model studied recently. Stability property of the WFGM(1,1) model is studied in detail. In practice, the quantum particle swarm optimization algorithm is adopted to choose the quasi-optimal parameters for the new model so as to get the best fitting accuracy. Finally, four numerical examples from different practical applications are present. Numerical results show that the new proposed prediction model is very efficient and has both the best fitting accuracy and the best prediction accuracy compared with the GM(1,1) and the FGM(1,1) as well as the NIPGM(1,1) prediction models.

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A quantum behaved particle swarm optimization for flexible job shop scheduling

Cited by 125 publications

References 52 publications

Quantum-Behaved Particle Swarm Optimization with Weighted Mean Personal Best Position and Adaptive Local Attractor

Quantum-Behaved Particle Swarm Optimization with Weighted Mean Personal Best Position and Adaptive Local Attractor

Discrete Differential Evolution Algorithm with the Fuzzy Machine Selection for Solving the Flexible Job Shop Scheduling Problem

A Novel Weighted Fractional GM(1,1) Model and Its Applications

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