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Computing the Load Margin of Power Systems Using crow search algorithm

IFAC PAPERSONLINE 2022年第1期55卷 271-275页

作者： Bento, Murilo E. C. Univ Fed Rio de Janeiro BR-21941909 Rio De Janeiro RJ Brazil

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/)

关键词： Power System Stability Small-Signal Stability Voltage Stability Smart Grid Dynamic Security Assessment crow search algorithm Sine Cosine algorithm

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Resilient Wide-Area Damping Controller Design Using crow search algorithm 7th

Resilient Wide-Area Damping Controller Design Using Crow Sea...

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7th International Conference on Advances in Control and Optimization of Dynamical Systems (ACODS)

作者： Bento, Murilo E. C. Univ Fed Rio de Janeiro BR-21941909 Rio De Janeiro RJ Brazil

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/)

关键词： Power System Control Small-Signal Stability Smart Grid Wide-Area Damping Control Cyber-Security crow search algorithm

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Enhanced variants of crow search algorithm boosted with cooperative based island model for global optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartA期238卷

作者： Thaher, Thaer Sheta, Alaa Awad, Mohammed Aldasht, Mohammed Arab Amer Univ Dept Comp Syst Engn Jenin Palestine Southern Connecticut State Univ Comp Sci Dept New Haven CT USA Palestine Polytech Univ Dept Comp Engn Hebron Palestine

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

关键词： crow search algorithm Island model Tournament selection Population diversity Metaheuristics

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Global Best Guided Binary crow search algorithm for Feature Selection

Global Best Guided Binary Crow Search Algorithm for Feature ...

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International Conference on Distributed Computing and Optimization Tech-niques (ICDCOT)

作者： Agarwal, Unnati Sahu, Tirath Prasad Natl Inst Technol Raipur Dept Informat Technol Raipur India

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.

关键词： Classification crow search algorithm Feature selection Optimization

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Topology and size optimization of truss structures using an improved crow search algorithm

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STRUCTURAL ENGINEERING AND MECHANICS 2021年第6期77卷 779-795页

作者： Mashayekhi, Mostafa Yousefi, Roghayeh Vali E Asr Univ Rafsanjan Dept Civil Engn Rafsanjan Iran

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.

关键词： truss structures size optimization topology optimization crow search algorithm cellular automata method

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crow search algorithm: Theory, Recent Advances, and Applications

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IEEE ACCESS 2020年 8卷 173548-173565页

作者： Hussien, Abdelazim G. Amin, Mohamed Wang, Mingjing Liang, Guoxi Alsanad, Ahmed Gumaei, Abdu Chen, Huiling Fayoum Univ Fac Sci Faiyum 63514 Egypt Menou Univ Fac Sci Shibin Al Kawm 32511 Egypt Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Wenzhou Polytech Dept Informat Technol Wenzhou 325035 Peoples R China King Saud Univ Coll Comp & Informat Sci Dept Informat Syst STCs Artificial Intelligence Chair Riyadh 11543 Saudi Arabia Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China

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.

关键词： Optimization Particle swarm optimization Heuristic algorithms Classification algorithms Genetic algorithms Licenses Mathematical model crow search algorithm CSA swarm intelligence meta-heuristics optimization nature-inspired algorithms

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Improved convergence performance of advanced crow search algorithm and application to optimal design of large-scale truss dome structures

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JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 2024年

作者： Lee, Donwoo Kim, Hyukjin Kim, Jeonghyun Lee, Seungjae Korea Univ Technol & Educ Sch Ind Design & Architectural Engn 1600 Chungjeol Ro Cheonan 31253 Chungcheongnam South Korea Wroclaw Univ Sci & Technol Fac Civil Engn Wroclaw Poland

Due to its small number of parameters, the crow search (CS) algorithm is easy to apply to engineering problems and solves optimization problems in various fields. The CS algorithm is not harmoniously used with the exploitation and exploration performance, and the convergence performance varies greatly depending on the initial population. An advanced CS (ACS) algorithm with a new method was proposed to improve the convergence performance of the CS algorithm, and it was proved that the convergence performance was improved by using benchmark functions and engineering example problems. However, the convergence performance according to the probability used in the proposed method for harmonizing the global search and the local search among ACS algorithms was not accurately compared and mentioned, and it was proposed to use a value of 0.5. Therefore, this paper examines the probability that the best convergence performance can be derived to harmonize global and local searches. In addition, it solves the engineering optimization problem of building structures by applying it to the optimal design of large-scale truss dome structures with natural frequencies as constraints. As a result, the minimum weight was derived from all truss dome structures when the probability proposed in this paper was used.

关键词： Meta-heuristics crow search algorithm large-scale structure, truss dome, optimal design

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Binary crow search algorithm for the uncapacitated facility location problem

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NEURAL COMPUTING & APPLICATIONS 2021年第21期33卷 14669-14685页

作者： Sonuc, Emrullah Karabuk Univ Dept Comp Engn TR-78050 Karabuk Turkey

The crow search algorithm (CSA) is a recently proposed population-based optimization algorithm for continuous optimization. Since the original CSA searches for a feasible solution in a continuous search space, it cannot handle binary optimization problems directly. A few binary variants of CSA are presented in the literature. However, these variants search for a new solution in the continuous domain and need transfer functions to adapt the solution to the binary domain. This may cause poor exploration, making some regions in the search space impossible to discover. This paper proposes an effective binary CSA (BinCSA) using bitwise operations that directly searches for a feasible solution in the binary search space. For this purpose, the original update mechanism of the CSA is improved using exclusive-OR and AND logical operators in order to provide a good balance between exploration and exploitation in the binary search space. The effectiveness of the proposed BinCSA is evaluated on the uncapacitated facility location problem (UFLP), one of the most widely investigated pure binary optimization problems. The performance of BinCSA is examined using two different UFLP datasets, ORLIB and M*. The experimental results show that BinCSA obtained the optimal solution for 13 out of 15 instances of ORLIB and 12 out of 20 instances of M*. Moreover, BinCSA exhibits superior performance on ORLIB instances when compared to other methods and is very competitive on M* instances in terms of solution quality and robustness. The source code for BinCSA, as used for the UFLP, is available at https://***/3mrullah/BinCSA.

关键词： Binary encoded problems crow search algorithm Uncapacitated facility layout problem

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Optimization of Running Blade Prosthetics Utilizing crow search algorithm Assisted by Artificial Neural Networks

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STROJNISKI VESTNIK-JOURNAL OF MECHANICAL ENGINEERING 2021年第3期67卷 88-100页

作者： Rosel Solis, Manuel Javier Davalos Ramirez, Jose Omar Molina Salazar, Javier Ruiz Ochoa, Juan Antonio Gomez Roa, Antonio Autonomous Univ Baja California Mexicali Baja California Mexico Autonomous Univ Ciudad Juarez Av Charro 450 Ciudad Juarez Chihuahua Mexico

A crow search algorithm (CSA) was applied to perform the optimization of a running blade prosthetics (RBP) made of composite materials like carbon fibre layers and cores of acrylonitrile butadiene styrene (ABS). Optimization aims to increase the RBP displacement limited by the Tsai-Wu failure criterion. Both displacement and the Tsai-Wu criterion are predicted using artificial neural networks (ANN) trained with a database constructed from finite element method (FEM) simulations. Three different cases are optimized varying the carbon fibre layers orientations: -45 degrees/45 degrees, 0 degrees/90 degrees, and a case with the two-fibre layer orientations intercalated. Five geometric parameters and a number of carbon fibre layers are selected as design parameters. A sensitivity analysis is performed using the Garzon equation. The best balance between displacement and failure criterion was found with fibre layers oriented at 0 degrees/90 degrees. The optimal candidate with -45 degrees/45 degrees orientation presents higher displacement;however, the Tsai-Wu criterion was less than 0.5 and not suitable for RBP design. The case with intercalated fibres presented a minimal displacement being the stiffer RBP design. The damage concentrates mostly in the zone that contacts the ground. The sensitivity study found that the number of layers and width were the most important design parameters.

关键词： optimization crow search algorithm artificial neural networks running blade prosthetics Tsai-Wu criterion finite element method

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A comprehensive survey of crow search algorithm and its applications

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ARTIFICIAL INTELLIGENCE REVIEW 2021年第4期54卷 2669-2716页

作者： Meraihi, Yassine Gabis, Asma Benmessaoud Ramdane-Cherif, Amar Acheli, Dalila Univ MHamed Bougara Boumerdes LIST Lab Ave Independence Boumerdes 35000 Algeria Ecole Natl Super Informat Lab Methodes Concept Syst Algiers Algeria Univ Versailles St Quentin En Yvelines LISV Lab 10-12 Ave Europe F-78140 Velizy Villacoublay France Univ MHamed Bougara Boumerdes Automat Dept Ave Independence Boumerdes 35000 Algeria

crow search algorithm (CSA) is a recent swarm intelligence optimization algorithm inspired by the social intelligent behavior of crows for hiding food. It has been widely used to solve a large variety of optimization problems in several fields and areas of research and has proved its efficiency compared to several state-of-the-art optimization algorithms available in the literature. This paper presents a comprehensive overview of crow search algorithm and its new variants categorized into modified and hybridized versions. It also describes the several applications of CSA in various domains such as feature selection, image processing, scheduling, economic dispatch, distributed generation, and other engineering problems. In addition, the paper suggests some interesting research areas related to CSA enhancement, CSA hybridization, and possible new applications.

关键词： crow search algorithm Optimization Nature-inspired algorithm Swarm intelligence Meta-heuristics

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