A Hybrid Crow Search Algorithm for Solving Permutation Flow Shop Scheduling Problems

Huang, Ko-Wei; Girsang, Abba Suganda; Wu, Ze-Xue; Chuang, Yu-Wei

doi:10.3390/app9071353

Cited by 21 publications

(12 citation statements)

References 47 publications

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“…The traditional CSA has been implemented mostly on continuous optimization problems with a small number of research studies done on their application in combinatorial optimization. Huang et al [22] proposed a hybrid HCSA approach to solve the NP hard permutation flow shop scheduling problem (PFSP). They used several techniques to minimize the makespan of the PFSP, such as the smallest position value (SVP) rules to convert continuous to discrete values or job numbers, Nawaz-Enscore-Ham technique to generate a population of quality individuals and simulated annealing and variable neighborhood search algorithms to maintain the diversity of solution throughout the search process.…”

Section: Gcsa Methodologymentioning

confidence: 99%

A Genetic Crow Search Algorithm for Optimization of Operation Sequencing in Process Planning

et al. 2021

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Computer-aided process planning represents the main link between computer-aided design and computer-aided manufacturing. One of the crucial tasks in computer-aided process planning is an operation sequencing problem. In order to find the optimal process plan, operation sequencing problem is formulated as an NP hard combinatorial problem. To solve this problem, a novel genetic crow search approach (GCSA) is proposed in this paper. The traditional CSA is improved by employing genetic strategies such as tournament selection, three-string crossover, shift and resource mutation. Moreover, adaptive crossover and mutation probability coefficients were introduced to improve local and global search abilities of the GCSA. Operation precedence graph is adopted to represent precedence relationships among features and vector representation is used to manipulate the data in the Matlab environment. A new nearest mechanism strategy is added to ensure that elements of machines, tools and tool approach direction (TAD) vectors are integer values. Repair strategy to handle precedence constraints is adopted after initialization and shift mutation steps. Minimization of total production cost is used as the optimization criterion to evaluate process plans. To verify the performance of the GCSA, two case studies with different dimensions are carried out and comparisons with traditional and some modern algorithms from the literature are discussed. The results show that the GCSA performs well for operation sequencing problem in computer-aided process planning.

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Section: Gcsa Methodologymentioning

confidence: 99%

A Genetic Crow Search Algorithm for Optimization of Operation Sequencing in Process Planning

et al. 2021

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“…Likewise, Anter and Ali integrated the CSA with the Fuzzy Cmeans algorithm and chaos theory and applied it to medical problems [126]. Also, Nawaz-Enscore-Ham (NEH) strategy was used to generate CSA population [127]. Also, in [172] the authors developed a hybrid algorithm that combined WOA with CSA called CrowWhale to solve energy trust routing (ETR).…”

Section: B Hybrid Csamentioning

confidence: 99%

“…Another hybrid version between the CSA, lion algorithm, and AFL called crow-FAL was developed by Rodrigues [175] and was applied to intrusion detection. In [179], Huang et al developed a hybrid version of CSA called HCSA in which CSA was integrated with Nawaz-Enscore-Ham (NEH). The novel algorithm has been applied to the flow shop scheduling problem.…”

Section: B Hybrid Csamentioning

confidence: 99%

Crow Search Algorithm: Theory, Recent Advances, and Applications

et al. 2020

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In this paper, a comprehensive overview of the Crow Search Algorithm (CSA) is introduced with detailed discussions, which is intended to keep researchers interested in swarm intelligence algorithms and optimization problems. CSA is a new swarm intelligence algorithm recently developed, which simulates crow behavior in storing excess food and retrieving it when needed. In the optimization theory, the crow is the searcher, the surrounding environment is the search space, and randomly storing the location of food is a feasible solution. Among all food locations, the location where the most food is stored is considered to be the global optimal solution, and the objective function is the amount of food. By simulating the intelligent behavior of crows, CSA tries to find optimal solutions to various optimization problems. It has gained a considerable interest worldwide since its advantages like simple implementation, a few numbers of parameters, flexibility, etc. This survey introduces a comprehensive variant of CSA, including hybrid, modified, and multi-objective versions. Furthermore, based on the analyzed papers published in the literature by some publishers such as IEEE, Elsevier, and Springer, the comprehensive application scenarios of CSA such as power, computer science, machine learning, civil engineering have also been reviewed. Finally, the advantages and disadvantages of CSA have been discussed by conducting some comparative experiments with other similar published peers.

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“…On these bases, this study enhances the performance of the firefly clustering algorithm by incorporating variable neighborhood search (VNS) 26 as a local search method to overcome their limitations in providing solutions to clustering problems, thereby exhibiting a promising performance in different application domains 27,28 . The FA mainly depends on the initial selection, which causes premature convergence when no neighborhood search strategies are employed to improve the quality of the clustering solutions in the neighborhood region 27 and explore the global regions in the search space 29 .…”

Section: Introductionmentioning

confidence: 99%

Improved Firefly Algorithm with Variable Neighborhood Search for Data Clustering

Al-Behadili

2022

Baghdad Sci.J

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Among the metaheuristic algorithms, population-based algorithms are an explorative search algorithm superior to the local search algorithm in terms of exploring the search space to find globally optimal solutions. However, the primary downside of such algorithms is their low exploitative capability, which prevents the expansion of the search space neighborhood for more optimal solutions. The firefly algorithm (FA) is a population-based algorithm that has been widely used in clustering problems. However, FA is limited in terms of its premature convergence when no neighborhood search strategies are employed to improve the quality of clustering solutions in the neighborhood region and exploring the global regions in the search space. On these bases, this work aims to improve FA using variable neighborhood search (VNS) as a local search method, providing VNS the benefit of the trade-off between the exploration and exploitation abilities. The proposed FA-VNS allows fireflies to improve the clustering solutions with the ability to enhance the clustering solutions and maintain the diversity of the clustering solutions during the search process using the perturbation operators of VNS. To evaluate the performance of the algorithm, eight benchmark datasets are utilized with four well-known clustering algorithms. The comparison according to the internal and external evaluation metrics indicates that the proposed FA-VNS can produce more compact clustering solutions than the well-known clustering algorithms.

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A Hybrid Crow Search Algorithm for Solving Permutation Flow Shop Scheduling Problems

Cited by 21 publications

References 47 publications

A Genetic Crow Search Algorithm for Optimization of Operation Sequencing in Process Planning

A Genetic Crow Search Algorithm for Optimization of Operation Sequencing in Process Planning

Crow Search Algorithm: Theory, Recent Advances, and Applications

Improved Firefly Algorithm with Variable Neighborhood Search for Data Clustering

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