A Binary Crow Search Algorithm for Solving Two-dimensional Bin Packing Problem with Fixed Orientation

Laabadi, Soukaina; Naimi, Mohamed; Amri, Hassan El; Achchab, Boujemâa

doi:10.1016/j.procs.2020.03.420

Cited by 30 publications

(19 citation statements)

References 14 publications

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“…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. Laabadi et al [23] used the binary CSA to solve the two-dimensional bin packaging problem, which is also an NP-hard combinatorial problem. This approach utilizes a sigmoid function for mapping real-valued solution into binary ones and specific knowledge repair operator to improve diversity and restore feasibility of infeasible solutions.…”

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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“…Furthermore, two binary version of CSA algorithm are proposed in [64] and [65]. The first one is the BCSA [64] which used a V-shaped transfer function to obtain a binary representation a continuous data with application to feature selection.…”

Section: Existing Crow Search-based Methodsmentioning

confidence: 99%

“…The first one is the BCSA [64] which used a V-shaped transfer function to obtain a binary representation a continuous data with application to feature selection. The second on [65] consists in applying a sigmoid transformation and was applied to solve the 2D bin packing problem. Several modified versions of CSA tended to manage the diversity based on the Gaussian distribution and diversity information of the population such in [66] for electromagnetic optimization, the usability factors hierarchical model for feature extraction and prediction [67], the priority-based technique is used to determine the sufficient flight length amount for each crow to update their position based other crow for economic load dispatch problem [68] and the modification of the CSA parameters like; the awareness probability and the random perturbation of each crow is proposed in [69].…”

Section: Existing Crow Search-based Methodsmentioning

confidence: 99%

A Distributed Bi-behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization

Aboud¹,

Rokbani²,

Alimi³

et al. 2021

Preprint

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<p>Dynamic multi-objective optimization problems (DMOPs) and Many-Objective Optimization Problems (MaOPs) are two classes of the optimization filed which have potential applications in engineering. Modified Multi-Objective Evolutionary Algorithms hybrid approaches seem to be suitable to effectively deal with such problems. However, the Crow Search Algorithm has not yet considered for both DMOP and MaOP. This paper proposes a Distributed <a>Bi-behaviors </a>Crow Search Algorithm (DB-CSA) with two different mechanisms, one corresponding to the search behavior and another to the exploitative behavior with a dynamic switch mechanism. The bi-behaviors CSA chasing profile is defined based on a large Gaussian-like Beta-1 function which ensures diversity enhancement, while the narrow Gaussian Beta-2 function is used to improve the solution tuning and convergence behavior. The DB-CSA approach is developed to solve several types of DMOPs and a set of MaOPs with 2, 3, 5, 7, 8, 10 and 15 objectives. The Inverted General Distance, the Mean Inverted General Distance and the Hypervolume Difference are the main measurement metrics are used to compare the DB-CSA approach to the state-of-the-art MOEAs. All quantitative results are analyzed using the nonparametric Wilcoxon signed rank test with 0.05 significance level which proving the efficiency of the proposed method for solving both 44 DMOPs and MaOPs utilized. </p>

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“…Souza et al [96] proposed a binary version of CSA called BCSA in which a V-shape transfer function was used to convert the continuous values to discrete ones. Laabadi et al [164] did another work. They developed a binary version of CSA to solve the 2D bin packing problem.…”

Section: ) Binary Csamentioning

confidence: 97%

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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A Binary Crow Search Algorithm for Solving Two-dimensional Bin Packing Problem with Fixed Orientation

Cited by 30 publications

References 14 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

A Distributed Bi-behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization

Crow Search Algorithm: Theory, Recent Advances, and Applications

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