Entropy-Enhanced Genetic Algorithm with Tabu Search for Job Shop Scheduling Problems

Huang, Yuehui; Yao, Xiang; Ge, Dong Yuan; Li, Yong Xiang

doi:10.4028/www.scientific.net/amr.590.557

Cited by 3 publications

(2 citation statements)

References 4 publications

(5 reference statements)

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“…Given the same maximum iteration generation and particle swarm size, IPSOA algorithm, standard genetic algorithms (SGA) [37], and traditional particle swarm optimization algorithm (PSO) [35] are used to solve the same service composition optimization problem, respectively. As shown in Figure 7, IPSOA converges to the optimal solution in the 49-th generation, SGA converges in the 82-nd generation, and PSO converges to the optimal solution in the 54-th generation.…”

Section: Application Examplementioning

confidence: 99%

Multiobjective Optimization of Cloud Manufacturing Service Composition with Improved Particle Swarm Optimization Algorithm

Yao

Liu

2020

Mathematical Problems in Engineering

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Aiming at the problems of low search efficiency and inaccurate optimization of existing service composition optimization methods, a new multiobjective optimization model of cloud manufacturing service composition was constructed, which took service matching degree, composition synergy degree, cloud entropy, execution time, and execution cost as optimization objectives, and an improved particle swarm optimization algorithm (IPSOA) was proposed. In the IPSOA, the integer encoding method was used for particle encoding. The inertia coefficient and two acceleration coefficients were improved by introducing the normal cloud model, sine function, and cosine function. The global search ability of IPSOA in the early stage was improved, and its prematurity was restrained to form a more comprehensive solution space. In the later stage, IPSOA focused on the local fine search and improved the optimization precision. Taking automatic guided forklift manufacturing task as an example, the correctness of the proposed multiobjective optimization model of cloud manufacturing service composition and the effectiveness of its solution algorithm were verified. The performance of IPSOA was analyzed and compared with standard genetic algorithm (SGA) and traditional particle swarm optimization (PSO). Under the same conditions, IPSOA had a faster convergence speed than PSO and SGA and had better performance than PSO.

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Section: Application Examplementioning

confidence: 99%

Multiobjective Optimization of Cloud Manufacturing Service Composition with Improved Particle Swarm Optimization Algorithm

Yao

Liu

2020

Mathematical Problems in Engineering

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“…The average run time of the algorithm is 13.66s, and the evolution curves are shown in Figure 7. Given the same maximum evolutionary generation and population size, IGABE algorithm, traditional genetic algorithm (SGA), hybrid genetic algorithm (HGA) [45], and cloud-entropy enhanced genetic algorithm (CEGA) [34] are used to solve the same problem. As shown in Figure 8, the IGABE has converged to the optimal solution of the manufacturing task when it evolves to the fifty-seventh generation, the SGA has not converged until the ninety-fourth generation, the HGA converges in the seventy-first generation, and the CEGA converges in the fifty-fourth generation.…”

Section: Application Examplementioning

confidence: 99%

Cloud Manufacturing Service Composition Optimization with Improved Genetic Algorithm

Yao

Liu

2019

Mathematical Problems in Engineering

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Aiming at the problems in which there exists collocation between services and manufacturing tasks, multiobjective cloud manufacturing service composition optimization seldom considers the synergy degree of composite cloud services and the complexity of service composition, so a novel service composition optimization approach, called improved genetic algorithm based on entropy (IGABE), is put forward. First, the mathematical expressions of service collocation degree, composition synergy degree, composition entropy, and their related influence factors of the service composition are analyzed, and their definitions and calculation methods are given. Then, a multiobjective cloud manufacturing service composition optimization mathematical model is established. Moreover, crossover and mutation operators are improved by introducing normal cloud model theory and piecewise function, and improved roulette selection method is used to perform the selection operation. And the fitness function of the proposed IGABE is designed by combining Euclidean deviation with angular deviation. Finally, the manufacturing task of a wheeled cleaning robot is exemplified to verify the correctness of the proposed multiobjective optimization model for cloud manufacturing service composition and the effectiveness of the proposed algorithm, compared with Standard Genetic Algorithm (SGA), Hybrid Genetic Algorithm (HGA), and Cloud-entropy Enhanced Genetic Algorithm (CEGA). The studied results show that IGABE converges faster than SGA and HGA and can analyze and reflect the content difference expressed by the objective functions of service composition scheme and its approximation degree to the corresponding dimensions of the ideal point vector more comprehensively than CEGA. As such, the optimal service composition obtained by IGABE algorithm can better meet the complex needs of users.

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Comparison of performance the neighborhood operators and the ratio of search local-global in minimizing makespan using an GA-VNS collaboration to solve reactive production scheduling

Tuma

Morandin

Caridá

2016

IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society

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Entropy-Enhanced Genetic Algorithm with Tabu Search for Job Shop Scheduling Problems

Cited by 3 publications

References 4 publications

Multiobjective Optimization of Cloud Manufacturing Service Composition with Improved Particle Swarm Optimization Algorithm

Multiobjective Optimization of Cloud Manufacturing Service Composition with Improved Particle Swarm Optimization Algorithm

Cloud Manufacturing Service Composition Optimization with Improved Genetic Algorithm

Comparison of performance the neighborhood operators and the ratio of search local-global in minimizing makespan using an GA-VNS collaboration to solve reactive production scheduling

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