A Hybrid Evolutionary Algorithm Using Two Solution Representations for Hybrid Flow-Shop Scheduling Problem

Fan, Jiaxin; Li, Yingli; Xie, Jin; Zhang, Chunjiang; Shen, Weiming; Gao, Liang

doi:10.1109/tcyb.2021.3120875

Cited by 38 publications

(13 citation statements)

References 52 publications

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“…Intelligent optimization algorithms can obtain the optimal solution with high probability in a reasonable time, and several common intelligent algorithms such as the evolutionary algorithm [9,15,16], artificial bee colony algorithm [17,18], rider optimization algorithm [19], GWO [20,21] and GA [22][23][24] have been widely applied. Pang, X. L [25] et al optimized the fireworks algorithm by introducing strategies such as non-linear radius and detecting the minimum explosion magnitude, Cauchy-Gaussian mixture operator and elite population selection.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, by minimizing the maximum completion time as the objective function of the algorithm, the genetic coding naturally defines the fitness function of the problem as the completion time according to the order production array  . The fitness function is set as shown in Equation (9).…”

Section: Fitness Functionmentioning

confidence: 99%

“…Under this mechanism, chromosomes that are more adapted to the scheme model are retained to survive, and chromosomes that are poorly adapted perish. In this paper, chromosomes are selected in the algorithm by using the competition selection method, and two chromosomes are randomly selected from chromosomes based on Equation (9). The chromosome with high adaptation is selected according to the value of the adaptation function, while the contraindication table is updated.…”

Section: Selection Operatormentioning

confidence: 99%

“…However, GA needs to be improved to enhance the algorithm performance for problems with a higher complexity. Moreover, improved genetic algorithms based on local search algorithms are one of the most common forms of improvement [9].…”

Section: Introductionmentioning

confidence: 99%

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Research on Production Scheduling Technology in Knitting Workshop Based on Improved Genetic Algorithm

et al. 2023

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Production scheduling in a knitting workshop is an important method to improve production efficiency, reduce costs and improve service. In order to achieve a reasonable allocation of parallel machines as well as cooperation between different machines within the workshop, thereby ensuring the optimal scheduling of production plans, this paper proposes a scheduling method using an improved genetic algorithm (IGA) based on tabu search. Firstly, the production scheduling model of a knitting workshop is established. Secondly, an IGA based on the minimum processing time rule, the priority idle machine rule and the production order ranking code is used to optimize the solution. Finally, an experiment platform for knitting workshop production is built to verify the proposed scheduling method. The experimental results show that the proposed IGA based on tabu search performs well in terms of preconvergence speed, global search capability and local search capability. The IGA converges faster than the traditional genetic algorithm by about 25%, reduces the redundancy time of scheduling, meets the production requirements of the knitting intelligent workshop and has a good reference value for promoting the intelligent development of knitting production.

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Section: Related Workmentioning

confidence: 99%

Section: Fitness Functionmentioning

confidence: 99%

Section: Selection Operatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Research on Production Scheduling Technology in Knitting Workshop Based on Improved Genetic Algorithm

et al. 2023

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“…To further verify the superiority of SCBBO for solving highdimensional global optimization problems, we compare it with seven state-of-the-art EAs proposed in the past few years: WOA [64], SSA [65], I-ABC [66], RCGA-rdn [67], DOBL [68], HEA [69], SDSA [70]. Among them, WOA and SSA are highly cited advanced algorithms in "Web of Science".…”

Section: Comparison Between Scbbo and Other Easmentioning

confidence: 99%

A Dual Biogeography-Based Optimization Algorithm for Solving High-Dimensional Global Optimization Problems and Engineering Design Problems

2022

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Biogeography-based optimization (BBO) cannot effectively solve high-dimensional global optimization problems due to its single migration mechanism and random mutation operator. To overcome these limitations, this paper propose a dual BBO based on sine cosine algorithm (SCA) and dynamic hybrid mutation, named SCBBO. Firstly, the Latin hypercube sampling method is innovatively used to improve the initial population ergodicity. Secondly, a nonlinear transformation parameter and a inertia weight adjustment factor are designed into the position update formula of SCA to make SCBBO suitable for high dimensional environments. Then, a dynamic hybrid mutation operator is designed by combining Laplacian and Gaussian mutation, which helps the algorithm to escape from local optima and balance the exploration and exploitation. Finally, the dual learning strategy is integrated, so the convergence accuracy is further improved by generating dual individuals. Meanwhile, A sequence convergence model is established to prove the algorithm can converge to the global optimal solution with probability 1. Compared with other state-of-the-art evolutionary algorithms, SCBBO effectively improves the optimization accuracy and convergence speed for high-dimensional optimization problems. To further show the superiority of SCBBO, the performance is compared on 1000, 2000, 5000 and 10000 dimensions, respectively. The comparsions show that SCBBO's optimization results on these dimensions are basically the same. SCBBO also applied to engineering design problems, and the simulation results demonstrate that the proposed method is also effective on constrained optimization problems.

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An operator-inspired framework for metaheuristics and its applications on job-shop scheduling problems

Li,

Gao

2024

Applied Soft Computing

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A Hybrid Evolutionary Algorithm Using Two Solution Representations for Hybrid Flow-Shop Scheduling Problem

Cited by 38 publications

References 52 publications

Research on Production Scheduling Technology in Knitting Workshop Based on Improved Genetic Algorithm

Research on Production Scheduling Technology in Knitting Workshop Based on Improved Genetic Algorithm

A Dual Biogeography-Based Optimization Algorithm for Solving High-Dimensional Global Optimization Problems and Engineering Design Problems

An operator-inspired framework for metaheuristics and its applications on job-shop scheduling problems

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