An Improved Discrete Artificial Bee Colony Algorithm for Flexible Flowshop Scheduling with Step Deteriorating Jobs and Sequence‐Dependent Setup Times

Xuan, Hua; Zhang, Huixian; Li, Bing

doi:10.1155/2019/8520503

Cited by 16 publications

(27 citation statements)

References 26 publications

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“…ABC has been successfully applied to solve shop scheduling problems due to its advantages such as few control parameters and ease of implementation [20]. As there is no research related to ABC for solving FHFGSP, this section reviews the work related to the use of ABC for solving shop scheduling problems.…”

Section: Related Workmentioning

confidence: 99%

“…Lei et al [24] studied the distributed unrelated parallel machine scheduling problem with preventive maintenance (DUPMSP) and proposed an ABC with division to minimize MS. Xie et al [25] proposed an improved ABC considering machining structure evaluation to solve the flexible integrated scheduling problem of networked equipment, which is an extension of job shop scheduling. Xuan et al [20] proposed an improved DABC with the introduction of a genetic algorithm to solve FJSP for uncorrelated parallel machines with progressively deteriorating jobs and timing dependencies.…”

Section: Related Workmentioning

confidence: 99%

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Sorting-Based Discrete Artificial Bee Colony Algorithm for Solving Fuzzy Hybrid Flow Shop Green Scheduling Problem

Wang

2021

Mathematics

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In this era of unprecedented economic and social prosperity, problems such as energy shortages and environmental pollution are gradually coming to the fore, which seriously restrict economic and social development. In order to solve these problems, green shop scheduling, which is a key aspect of the manufacturing industry, has attracted the attention of researchers, and the widely used flow shop scheduling problem (HFSP) has become a hot topic of research. In this paper, we study the fuzzy hybrid green shop scheduling problem (FHFGSP) with fuzzy processing time, with the objective of minimizing makespan and total energy consumption. This is more in line with real-life situations. The non-linear integer programming model of FHFGSP is built by expressing job processing times as triangular fuzzy numbers (TFN) and considering the machine setup times when processing different jobs. To address the FHFGSP, a discrete artificial bee colony (DABC) algorithm based on similarity and non-dominated solution ordering is proposed, which allows individuals to explore their neighbors to different degrees in the employed bee phase according to a sequence of positions, increasing the diversity of the algorithm. During the onlooker bee phase, individuals at the front of the sequence have a higher chance of being tracked, increasing the convergence rate of the colony. In addition, a mutation strategy is proposed to prevent the population from falling into a local optimum. To verify the effectiveness of the algorithm, 400 test cases were generated, comparing the proposed strategy and the overall algorithm with each other and evaluating them using three different metrics. The experimental results show that the proposed algorithm outperforms other algorithms in terms of quantity, quality, convergence and diversity.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Sorting-Based Discrete Artificial Bee Colony Algorithm for Solving Fuzzy Hybrid Flow Shop Green Scheduling Problem

Wang

2021

Mathematics

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“…However, the traditional artificial bee colony algorithm tends to fall into the local extreme point at the later stage, and many scholars improve the local search performance of the bee colony algorithm from different angles. In recent years, some scholars have introduced chaos mapping to optimize the bee colony algorithm [24]- [27], which improves the search performance to a certain extent, and also fully verifies the advantages of the chaos optimization algorithm that is not sensitive to initial values and has strong traversal.…”

Section: Introductionmentioning

confidence: 97%

Improvement SVM Classification Performance of Hyperspectral Image Using Chaotic Sequences in Artificial Bee Colony

Zhao

Wang

et al. 2020

IEEE Access

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Artificial bee colony algorithm is an effective algorithm for parameter optimization, but the traditional artificial bee colony algorithm is liable to fall into local extreme points at a later stage. In this paper, we propose an improved artificial bee colony optimization algorithm, which solves the problems of premature convergence and falling into the local extreme value in the classification of hyperspectral images. First we use an improved chaotic sequence with higher randomness to initialize and update nectar sources to expand the distribution of nectar sources. Secondly, the optimized adaptive step size is introduced into the neighborhood search to speed up the algorithm convergence and improve the search efficiency. Then we build an improved artificial bee colony algorithm support vector machine optimization model to mine the optimal values of penalty factor C and kernel function parameter σ. Next, the model was used to perform classification experiments on two hyperspectral images (University of Pavia, Indian Pine) with different attributes, and compared with the traditional bee colony algorithm, genetic algorithm, and particle swarm algorithm. Experimental results on HSI datasets demonstrate the superiority of the proposed method over several well-known methods in both classification accuracy and convergence speed. INDEX TERMS Artificial bee colony, support vector machine, hyperspectral image, chaotic sequence.

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“…In literature, Meta-heuristic algorithms have been developed to solve PFSSP by several researchers. Most significant meta-heuristics used in literature are Artificial Bee Colony Algorithm (ABC) (Deng, Xu [35]; Han, Gong [36]; Li and Pan [37]; [38]), Differential Evolution Algorithm (DE) (Liu,Yin [39]), Evolutionary Algorithm (EA)(Qian, Wang [40], Yeh and Chiang [41]), Genetic Algorithms (GA) (Caraffa, Ianes [42]; Ruiz, Maroto [43]; Vallada and Ruiz [44]; Akhshabi, Haddadnia [45]; Andrade, Silva [46]), Hybrid Discrete Differential Evolution (HDDE)(Wang, Pan [47]), Hybrid Differential Evolution Algorithm (HDEA) (Liu,Yin [39]), Simulated Annealing (SA) (Laha and Chakraborty [48]; Lin and Ying [49]; Moslehi and Khorasanian [50]) Lin, Cheng [51], Tabu Search (TS)(Taillard [52]; Grabowski and Wodecki [53]; Grabowski and Pempera [54]; Arık [55]), TS and ABC (Li and Pan [37]), Hybrid Whale optimization algorithm(HWO) (Abdel-Basset, Manogaran [56]), Particle swarm optimization (PSO) (Zhao, Qin [57]) Evolution Strategy (ES) (de Siqueira, Souza [58]; Khurshid, Maqsood [59]), among others. These algorithms have found competitive results for different PFSSP's compared to heuristics; however, they require more computational time, as they initiate from a sequence that is constructed by heuristics and is iterated until termination criteria are achieved.…”

Section: Introductionmentioning

confidence: 99%

An Improved Evolution Strategy Hybridization With Simulated Annealing for Permutation Flow Shop Scheduling Problems

et al. 2021

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Flow Shop Scheduling Problem (FSSP) has significant application in the industry, and therefore it has been extensively addressed in the literature using different optimization techniques. Current research investigates Permutation Flow Shop Scheduling Problem (PFSSP) to minimize makespan using Hybrid Evolution Strategy (HESSA). Initially, a global search of the solution space is performed using an Improved Evolution Strategy (IES), then the solution is improved by utilizing local search abilities of Simulated Annealing (SA). IES thoroughly exploits the solution space using the reproduction operator, in which four offsprings are generated from one parent. A double swap mutation is used to guide the search to more promising areas in less computational time. The mutation rate is also varied for the fine-tuning of results. The best solution of the IES acts as a seed for SA, which further improved the results by exploring better neighborhood solutions. In SA, insertion mutation is used, and the cooling parameter and acceptancerejection criteria induce randomness in the algorithm. The proposed HESSA algorithm is tested on wellknown NP-hard benchmark problems of Taillard (120 instances), and the performance of the proposed algorithm is compared with the famous techniques available in the literature. Experimental results indicate that the proposed HESSA algorithm finds 54 Upper bounds for Taillard instances, while 38 results are further improved for the Taillard instances.

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An Improved Discrete Artificial Bee Colony Algorithm for Flexible Flowshop Scheduling with Step Deteriorating Jobs and Sequence‐Dependent Setup Times

Cited by 16 publications

References 26 publications

Sorting-Based Discrete Artificial Bee Colony Algorithm for Solving Fuzzy Hybrid Flow Shop Green Scheduling Problem

Sorting-Based Discrete Artificial Bee Colony Algorithm for Solving Fuzzy Hybrid Flow Shop Green Scheduling Problem

Improvement SVM Classification Performance of Hyperspectral Image Using Chaotic Sequences in Artificial Bee Colony

An Improved Evolution Strategy Hybridization With Simulated Annealing for Permutation Flow Shop Scheduling Problems

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