Scheduling jobs and tools is a significant problem for manufacturing systems. Inefficient job scheduling and tool loading planning may result in under utilization of capital intensive machines and a high level of mach...
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Scheduling jobs and tools is a significant problem for manufacturing systems. Inefficient job scheduling and tool loading planning may result in under utilization of capital intensive machines and a high level of machine idle time. Therefore, efficient scheduling of jobs and tools enables a manufacturing system to increase machines' utilization and decrease their idle times. This paper addresses machines' and tools' joint scheduling with alternate machines in a multimachine flexible manufacturing system (FMS) to minimize makespan (MSN). Only one copy of each type of tool is made available in FMS where tools are expensive. The tools are stored in the central tool magazine (CTM), which shares and serves them to several machines in order to reduce the cost of duplicating the tools in every machine. The problem is to select machines from alternate machines for job-operations, allocation of tools to the job-operations and job-operations' sequencing on machines for MSN minimization. This paper presents nonlinear mixed integer programming (MIP) formulation to model this simultaneous scheduling problem and crow search algorithm (CSA) built on the crows' intelligent behavior for solving this problem. The results show that CSA is providing better solutions than Jaya algorithm and the usage of alternate machines for the operations can reduce MSN.
This article explores the load frequency control (LFC) problem for manifold areas and sources under conventional situations. An attempt has been made to demonstrate the solicitation of a real-time simulation laborator...
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This article explores the load frequency control (LFC) problem for manifold areas and sources under conventional situations. An attempt has been made to demonstrate the solicitation of a real-time simulation laboratory for the LFC studies of three thermal area systems. Thermal systems are integrated with renewable like wind systems in area-1, 2 respectively. A novel ancillary controller termed by cascade tilt integral-proportional integral derivative (TI-PID) is suggested and is augmented by crow search algorithm. The superiority of TI-PID controller is tested and found to be better with PID and tilt integral derivative (TID) controller. Moreover, the obtained responses with hybrid peak area-integral square error are compared with ISE, and it shows better responses over ISE. Further, the responses with alternating current-high-voltage direct- current system improve system dynamics over wind-thermal and thermal systems. Furthermore, sensitivity analysis suggests that the TID with filter controller considerations attained at nominal circumstances are vigorous.
The purpose of this study is to develop a hybrid algorithm for feature selection and classi-fication of masses in digital mammograms based on the crow search algorithm (CSA) and Harris hawks optimization (HHO). The pr...
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The purpose of this study is to develop a hybrid algorithm for feature selection and classi-fication of masses in digital mammograms based on the crow search algorithm (CSA) and Harris hawks optimization (HHO). The proposed CSAHHO algorithm finds the best features depending on their fitness value, which is determined by an artificial neural network. Using an artificial neural network and support vector machine classifiers, the best features deter-mined by CSAHHO are utilized to classify masses in mammograms as benign or malignant. The performance of the suggested method is assessed using 651 mammograms. Experi-mental findings show that the proposed CSAHHO tends to be the best as compared to the original CSA and HHO algorithms when evaluated using ANN. It achieves an accuracy of 97.85% with a kappa value of 0.9569 and area under curve AZ = 0.982 +/- 0.006. Further-more, benchmark datasets are used to test the feasibility of the suggested approach and then compared with four state-of-the-art algorithms. The findings indicate that CSAHHO achieves high performance with the least amount of features and support to enhance breast cancer diagnosis.(c) 2022 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
This study shows how Meta-heuristic optimization programming may be implemented to solve constrained optimal control problems. With a single-link rigid manipulator (SLM) model as an example, the proposed crowsearch a...
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Power system operation centers require knowledge of the load level where the system may present instability characteristics. It is common practice to calculate the load margin where the system can collapse in voltage ...
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Power system operation centers require knowledge of the load level where the system may present instability characteristics. It is common practice to calculate the load margin where the system can collapse in voltage respecting a direction of load growth. However, the increase in load can also cause low damped oscillation modes to appear and cause small-signal stability problems of power systems. This research proposes a crow search algorithm-based method to calculate the load margin of power systems considering small-signal stability and voltage stability requirements. To solve the problem of good initial conditions for the search problem, the Sine Cosine algorithm was used. The proposed method was evaluated in the IEEE 39-bus system and the results achieved show the effectiveness of the method in determining the load margins quickly. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)
The use of real-time data from Phasor Measurement Units (PMUs) to compose a Wide-Area Damping Controller (WADC) proved to be effective in damping inter-area power system oscillation modes. However, PMU data is vulnera...
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The use of real-time data from Phasor Measurement Units (PMUs) to compose a Wide-Area Damping Controller (WADC) proved to be effective in damping inter-area power system oscillation modes. However, PMU data is vulnerable to cyber-attacks that can compromise the functioning of the WADC communication channels and the small-signal stability of the closed-loop control system. This article proposes a WADC design method based on an optimization problem to ensure the resilience of WADC to permanent loss of communication channels. The optimization problem is solved using the crow search algorithm. Case studies were done for the IEEE 68-bus system. The results of modal analysis and non-linear simulations in the time domain show the effectiveness of the proposed method. Copyright (c) 2022 The Authors. This is an open access article under the CC-BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)
The crow search algorithm (CSA) is a swarm-based metaheuristic algorithm that simulates the intelligent foraging behaviors of crows. While CSA effectively handles global optimization problems, it suffers from certain ...
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The crow search algorithm (CSA) is a swarm-based metaheuristic algorithm that simulates the intelligent foraging behaviors of crows. While CSA effectively handles global optimization problems, it suffers from certain limitations, such as low search accuracy and a tendency to converge to local optima. To address these shortcomings, researchers have proposed modifications and enhancements to CSA's search mechanism. One widely explored approach is the structured population mechanism, which maintains diversity during the search process to mitigate premature convergence. The island model, a common structured population method, divides the population into smaller independent sub-populations called islands, each running in parallel. Migration, the primary technique for promoting population diversity, facilitates the exchange of relevant and useful information between islands during iterations. This paper introduces an enhanced variant of CSA, called Enhanced CSA (ECSA), which incorporates the cooperative island model (iECSA) to improve its search capabilities and avoid premature convergence. The proposed iECSA incorporates two enhancements to CSA. Firstly, an adaptive tournament-based selection mechanism is employed to choose the guided solution. Secondly, the basic random movement in CSA is replaced with a modified operator to enhance exploration. The performance of iECSA is evaluated on 53 real-valued mathematical problems, including 23 classical benchmark functions and 30 IEEE-CEC2014 benchmark functions. A sensitivity analysis of key iECSA parameters is conducted to understand their impact on convergence and diversity. The efficacy of iECSA is validated by conducting an extensive evaluation against a comprehensive set of well-established and recently introduced meta-heuristic algorithms, encompassing a total of seventeen different algorithms. Significant differences among these comparative algorithms are established utilizing statistical tests like Wilcoxon's rank-sum
Feature selection is a universal combinatorial optimization problem that is used to enhance the characteristics of high-dimensional datasets by eliminating redundant data and selecting prominent features to generate a...
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ISBN:
(纸本)9789811922817;9789811922800
Feature selection is a universal combinatorial optimization problem that is used to enhance the characteristics of high-dimensional datasets by eliminating redundant data and selecting prominent features to generate acceptable classification performance. In optimization problems, the objective function can have several local optima, but the ultimate aim is to identify global optima or values close to global optimum. The crow search algorithm (CSA) is a recently suggested metaheuristic algorithm, implemented to feature selection issues systematically. It is witnessed that the CSA solution search equation is suitable in exploration but poor in exploitation. In this paper, a global best-guided solution to CSA (G-CSA) is proposed and applied to pick the optimum feature subset in a wrapper mode to boost exploitation for classification purposes. The efficiency of the proposed methodology is examined on twelve standard UCI datasets. When comparing experimental results, it is clear that the proposed algorithm is superior to its challengers.
In the recent decades, various optimization algorithms have been considered for the optimization of structures. In this research, a new enhanced algorithm is used for the size and topology optimization of truss struct...
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In the recent decades, various optimization algorithms have been considered for the optimization of structures. In this research, a new enhanced algorithm is used for the size and topology optimization of truss structures. This algorithm, which is obtained from the combination of crow search algorithm (CSA) and the Cellular Automata (CA) method, is called CA-CSA method. In the first iteration of the CA-CSA method, some of the best designs of the crow?s memory are first selected and then located in the cells of CA. Then, a random cell is selected from CA, and the best design is chosen from the selected cell and its neighborhood;it is considered as a ?local superior design? (LSD). In the optimization process, the LSD design is used to modify the CSA method. Numerical examples show that the CA-CSA method is more effective than CSA in the size and topology optimization of the truss structures.
In this article, 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 ...
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In this article, 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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