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Memory based hybrid crow search algorithm for solving numerical and constrained global optimization problems

ARTIFICIAL INTELLIGENCE REVIEW 2023年第1期56卷 27-99页

作者： Braik, Malik Al-Zoubi, Hussein Ryalat, Mohammad Sheta, Alaa Alzubi, Omar Al Balqa Appl Univ Salt Jordan Yarmouk Univ Irbid Jordan Southern Connecticut State Univ New Haven CT USA

crow search algorithm (CSA) is a promising meta-heuristic method developed based on the intelligent conduct of crows in nature. This algorithm lacks a good representation of its individuals' memory, and as with many other meta-heuristics it faces a problem in efficiently balancing exploration and exploitation. These defects may lead to early convergence to local optima. To cope with such issues, we proposed a Memory based Hybrid CSA (MHCSA) with the use of Particle Swarm Optimization (PSO) algorithm. This hybridization approach was proposed to reinforce the diversity ability of CSA and balance its search abilities for promising solutions to achieve robust search performance. The memory element of MHCSA was initialized with the best solution (pbest) of PSO to exploit the most promising search areas. The best positions of the CSA's individuals are improved using the best solution found so far (gbest) and (pbest) of PSO. Another flaw of CSA is the use of fixed flight length and awareness probability for crows to control exploration and exploitation features, respectively. This issue was circumvented here by replacing these constants with adaptive functions in order to provide a better balance between exploration and exploitation over the course of iterations. The competence of MHCSA was revealed by testing it on seventy-three standard and computationally complex benchmark functions. Its applicability was substantiated by solving seven engineering design problems. The results showed that the problem of early convergence was eliminated by MHCSA and that the balance of exploration and exploitation was further improved. Further, MHCSA ranked first among CSA, PSO, robust variants of CSA and other strong competing methods in terms of accuracy and stability.

关键词： crow search algorithm Particle swarm optimization Optimization techniques Meta-heuristics

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A diversity enhanced hybrid particle swarm optimization and crow search algorithm for feature selection

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APPLIED INTELLIGENCE 2023年第17期53卷 20535-20560页

作者： Osei-kwakye, Jeremiah Han, Fei Amponsah, Alfred Adutwum Ling, Qing-Hua Abeo, Timothy Apasiba Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang Peoples R China Jiangsu Key Lab Secur Technol Ind Cyberspace Zhenjiang Peoples R China Univ Profess Studies Informat Technol Studies Dept Accra Ghana Univ Mines & Technol Comp Sci & Engn Dept Tarkwa Ghana Jiangsu Univ Sci & Technol Sch Comp Sci Zhenjiang Peoples R China Tamale Tech Univ Fac Appl Sci & Technol Dept Comp Sci Tamale Ghana

Feature Selection (FS) is choosing a subcategory of features purposed to construct a machine learning model. Among the copious existing FS algorithms, Binary Particle Swarm Optimization algorithm (BPSO) is prevalent with applications in several domains. However, BPSO suffers from premature convergence that affects exploration, resulting in dilapidation. In this current work, we boost the exploration of BPSO, incorporating the intelligence of crows to hide their food sources from other crows and predators and maintain diversity by implementing a clustering strategy. The clustering technique guarantees that the starting population is evenly distributed over the feature space while including more promising features. Additionally, suppose a crow realizes another crow or a predator is tracking it. In that case, the crow moves randomly to evict the stalker, leading to a better exploration of unexplored regions within the search space. We named the proposed method Hybrid Particle Swarm Optimization and crow search algorithm with clustering initialization strategy (HPSOCSA-CIS). To evaluate the performance of HPSOCSA-CIS, 15 standard UCI datasets are utilized, and the outcomes are compared with recently proposed hybrid and standard optimization algorithms. From observation, HPSOCSA-CIS outperforms the comparing approaches for feature selection challenges on representative datasets that fall in the three-category based on dimensions. The HPSOCSA-CIS improves performance in terms of mean classification accuracy by 8.87%, 17.5%, and 21.90% on Low, medium, and high dimensional datasets, respectively.

关键词： Particle swarm optimization algorithm crow search algorithm Clustering Feature selection

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A robust crow search algorithm based power system state estimation

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ENERGY REPORTS 2023年 9卷 490-501页

作者： Andic, Cenk Ozturk, Ali Turkay, Belgin Istanbul Tech Univ Dept Elect Engn TR-34469 Istanbul Turkiye Duzce Univ Dept Elect Elect Engn TR-81620 Konuralp Duzce Turkiye

The State Estimation (SE) computational procedure plays a crucial role in modern electric power system security control by monitoring and analyzing operational conditions and predicting any emergency. In order to estimate state variables, Power System State Estimation (PSSE) takes into account the magnitudes and phases of voltage on each bus. To address the state estimation challenges in power systems, in this paper, we propose a novel application of the crow search algorithm (CSA) specifically tailored for the state estimation problem. We have assessed the introduced algorithm using the frameworks of both the IEEE 14-bus and IEEE 30-bus test systems. The first formulation is the Weighted Least Square (WLS) method, and the second is the Weighted Least Absolute Value (WLAV) method, both of which are objective function formulations. By comparing the results, it is clear that CSA-based SE is superior to the other metaheuristic algorithms considered, namely Genetic algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Swarm Optimization (ABSO). As a point of comparison, we use the Newton-Raphson method for calculating load flow. It has been shown that the proposed CSA-based SE technique has better accuracy than the other two algorithms in all different test systems. With this study, the power system is operated more accurately and reliably by the operators operating the system. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/). Peer-review under responsibility of the scientific committee of the 2022 7th International Conference on Renewable Energy and Conservation, ICREC, 2022.

关键词： crow search algorithm Power systems State estimation

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A Combined Fractional Order Proportional Integral Derivative Controller for Automatic Generation Control Integrated with Wind and Small Hydropower Plant Using crow search algorithm

ELECTRICA

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ELECTRICA 2023年第3期23卷 584-596页

作者： Karanam, Appala Naidu Shaw, Binod Nayak, Jyoti Ranjan Natl Inst Technol Dept Elect Engn Visakhapatnam Andhra Pradesh India Vignans Inst Informat Technol Dept Elect & Elect Engn Visakhapatnam Andhra Pradesh India

Automatic generation control (AGC) is essential for maintaining the frequency stability of power systems. However, designing an optimal controller for AGC remains a challenge. To overcome this deficiency, a novel two-degree-of-freedom-combined fractional order PID (2DOF-combined FOPID) controller is proposed and tested on two-area interconnected power systems consisting of reheat thermal and hydro units. The controller's performance is compared with conventional PID, FOPID, 2-DOF-PID, and 2DOF-FOPID controllers. The objective is to reduce the area control error. Optimal controller gain parameters are determined using the crow search algorithm (CSA) to minimize the integral time absolute error objective function. The dynamic behavior of the system is investigated in response to a 0.01 p.u. load disturbance in area-1. The designed controller's performance is also explored using wind integration and a CSA-optimized small hydropower plant. Results show that the 2DOF-combined FOPID controller outperforms the other controllers in terms of stability indices, including overshoot (Osh), undershoot (Ush), and settling time response (Ts) in the dynamic responses of the power system.

关键词： Two-degree-of-freedom-combined fractional order proportional integral derivative controller automatic generation control crow search algorithm fractional order controller frequency bias tie-line control

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A Competitive Optimum Bidding in a Restructured Power Market with Elastic Demand using crow search algorithm

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2025年

作者： Datta, Monalisa Sarkar, Dipu Natl Inst Technol Elect & Elect Engn Dept Nagaland 797103 India Techno Int New Town Elect Engn Dept Kolkata 700156 India

The essential phase taken up by an Independent System Operator (ISO) in the auction is the optimal bidding of data for each Generating Company (GENCO) involved in the system at every trading phase. Strategic bidding data is quite a momentous task by an ISO for the profit maximization of the system with an appropriate bidding coefficient strategically. This paper recommends bidding data to maximize profit with a new proposed optimization technique named crow search algorithm. It's a meta-heuristic algorithm performing a heuristic search to obtain a global optimal solution which had been applied to perform and obtain the maximized profit function. This proposed method is applied and tested on an Indian-75 Bus system consisting 15 suppliers to analyze new strategy application verifying the mathematical modeling and seen to have received a better value of profit with the minimum power generation for the system maintaining the demand of the market fulfilling all the inequality constraints. The calculations of the individual GENCO's profit and power, shown under the result section, have been compared to the Grey Wolf Optimization (GWO), Shuffled Frog Leap algorithm (SFLA) and Particle Swarm Optimization (PSO) for a wider explanation, and the bar graph has been included for better understanding.

关键词： Restructured power market bidding strategy power economics profit maximization crow search algorithm

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Massive Conscious Neighborhood-Based crow search algorithm for the Pseudo-Coloring Problem 15th

Massive Conscious Neighborhood-Based Crow Search Algorithm f...

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15th International Conference on Advances in Swarm Intelligence (ICSI)

作者： Viana, Monique Simplicio Contreras, Rodrigo Colnago Pessoa, Paulo Cavalcanti dos Santos Bongarti, Marcelo Adriano Zamani, Hoda Guido, Rodrigo Capobianco Morandin Junior, Orides Univ Fed Sao Carlos Sao Carlos SP Brazil Sao Paulo State Univ UNESP Inst Biosci Letters & Exact Sci Sao Jose Do Rio Preto SP Brazil Univ Sao Paulo Sao Carlos SP Brazil Weierstr Inst Berlin Germany Islamic Azad Univ Fac Comp Engn Najafabad Iran Islamic Azad Univ Najafabad Branch Big Data Res Ctr Najafabad Iran

ISBN: (纸本)9789819771806;9789819771813

The pseudo-coloring problem (PsCP) is a combinatorial optimization challenge that involves assigning colors to elements in a way that meets specific criteria, often related to minimizing conflicts or maximizing some form of utility. A variety of metaheuristic algorithms have been developed to solve PsCP efficiently. However, these algorithms sometimes struggle with the quality of solutions, impacting their ability to achieve optimal or near-optimal results reliably. To overcome these issues, this study introduces an adapted conscious neighborhood-based crow search algorithm (CCSA) and a massive variant of CCSA specifically tailored for PsCP. The performance of CCSA and MCCSA are evaluated on real and synthetic images and compared with state-of-the-art optimizers. The results showed that the adapted CCSA and MCCSA outperformed offering an effective strategy for image pseudo-colorization.

关键词： Optimization crow search algorithm Massive Local search Pseudo-Coloring Problem Color Spaces

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Improved Simultaneous Localization and Mapping Estimation using crow search algorithm Based Particle Filter

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INTERNATIONAL JOURNAL OF ENGINEERING 2023年第10期36卷 1827-1838页

作者： Abedini, M. Jazayeriy, H. Kazemitabar, J. Noshirvani Univ Technol Fac Elect & Comp Engn Babol Iran

Trajectory tracking and positioning are essential requirements in many areas, including robots and autonomous vehicles. In some cases, such as in areas where GPS signals are weak or not available, trajectory tracking is used as an alternative positioning system. In these cases, simultaneous localization and mapping (SLAM), is of great importance as it does not require prior knowledge and empirical offline fingerprint. SLAM can be combined with signal processing algorithms among which, particle filter stands out. However, challenges exist such as particle weights degradation and particles impoverishment that need to be dealt with. In fact, the loss of particle diversity for estimation has led to the lack of particles. To overcome this problem, one solution is to diversify the selection of particles after resampling. In this paper, we proposed a crow search algorithm (CSA) to overcome these issues and improve position estimation. The simulation results showed that this algorithm greatly improved the performance of fast SLAM.

关键词： Particle Filter Fast Simultaneous Localization and Mapping crow search algorithm Sample Depletion

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Exploiting the crow search algorithm to Overcome the Bandpass Problem 2nd

Exploiting the Crow Search Algorithm to Overcome the Bandpas...

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2nd International Conference on Forthcoming Networks and Sustainability in the AIoT Era (FoNeS-AIoT)

作者： Al-Sabaawi, Ali M. Ahmed Hussein, Mohsin Hasan Al Gburi, Hussien Qahtan Marhoon, Hayder Abdulameer Nineveh Univ Fac Informat Technol Software Dept Mosul Iraq Gifted Student Sch Nineveh Minist Educ Mosul Iraq Univ Kerbala Fac Comp Sci & Informat Technol Karbala Iraq Minist Educ Adm Affairs IDs Baghdad Iraq Al Ayen Univ Sci Res Ctr Informat & Commun Technol Res Grp Thi Qar Iraq

ISBN: (纸本)9783031628801;9783031628818

In telecommunication networks, fiber optics is used to transfer data from a source to a destination. One of the common problems in the transmission process is called the Bandpass Problem (BP). BP concentrates on establishing a model that can transfer information on various wavelengths at a minimum cost usingwavelength division multiplexing technology. The data is organized in packets involving various columns. The minimum cost can be obtained by finding the best row permutation in terms of cost in an acceptable time. Sundry studies have been exploited to find the minimum cost at an appropriate time. Although previous studies have reduced the cost and decreased the execution time, they have not reached optimality. Therefore, in this article, a mining technique using a meta-heuristic method called the crow search algorithm (CSA) was applied to achieve the aforementioned goal. The proposed method can find the global minimum cost by keeping the positions of the best row permutation in an acceptable time. The row permutation remains unchanged unless a new better row permutation is computed. The findings exhibited a great deal of insights into how the CSA method outperformed the genetic algorithm, simulated annealing, and the ant bee colony in most cases.

关键词： Bandpass problem crow search algorithm Metaheuristic algorithm

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A robust crow search algorithm-based power system stabilizer for the SMIB system

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NEURAL COMPUTING & APPLICATIONS 2022年第11期34卷 9161-9173页

作者： Yokus, Hamdullah Ozturk, Ali Duzce Univ Dept Elect Elect Engn Konuralp Duzce Turkey

Power system stabilizers (PSSs) are extensively used in generator units to enhance the transient stability of the power system. Hence, optimal tuning and placement of the parameters of PSS are crucial for the efficiency of the stabilizer. Researchers have been proposed many methods for optimizing such parameters. In this paper, the crow search algorithm (CSA), which is based on the intelligence of crows, was employed in a single-machine infinite-bus (SMIB) system to determine the optimum parameters of the PSS. Modeling and simulation of the SMIB and designing of PSS were made by MATLAB/Simulink. PSSs are designed to minimize low-frequency oscillations such as power angle, rotor speed, and field current deviation following a large disturbance. Our objective in this study is the minimization of the rotor speed deviation. The results of the simulations proved the effectiveness and robustness of the optimization process compared to other metaheuristic algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA). When compared to the performance attained by the GA-based and PSO-based PSS controller designs, the simulations show that the CSA-based PSS delivers a far better dynamic response when the system is disrupted. The CSA-based PSS settles 48.1% faster than the PSO-based PSS and 55.7% faster than the GA-based PSS. CSA has just 2 parameters to adjust, making it much easier to implement than other methods. These parameters for PSO and GA are 4 and 6. When CSA-based PSS is used in the SMIB system, overshoot and low-frequency oscillations are also significantly reduced compared to other methods.

关键词： crow search algorithm Genetic algorithm Power system stabilizer Power system transient stability

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Multi-strategies Boosted Mutative crow search algorithm for Global Tasks:Cases of Continuous and Discrete Optimization

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Journal of Bionic Engineering 2022年第6期19卷 1830-1849页

作者： Weifeng Shan Hanyu Hu Zhennao Cai Huiling Chen Haijun Liu Maofa Wang Yuntian Teng School of Emergency Management Institute of Disaster PreventionLangfang065201China Institute of Geophysics China Earthquake AdministrationBeijing100081China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin541004China

crow search algorithm(CSA)is a swarm-based single-objective optimizer proposed in recent years,which tried to inspire the behavior of crows that hide foods in different locations and retrieve them when *** original version of the CSA has simple parameters and moderate ***,it often tends to converge slowly or get stuck in a locally optimal region due to a missed harmonizing strategy during the exploitation and exploration ***,strategies of mutation and crisscross are combined into CSA(CCMSCSA)in this paper to improve the performance and provide an efficient optimizer for various optimization *** verify the superiority of CCMSCSA,a set of comparisons has been performed reasonably with some well-established metaheuristics and advanced metaheuristics on 15 benchmark *** experimental results expose and verify that the proposed CCMSCSA has meaningfully improved the convergence speed and the ability to jump out of the local *** addition,the scalability of CCMSCSA is analyzed,and the algorithm is applied to several engineering problems in a constrained space and feature selection *** results show that the scalability of CCMSCSA has been significantly improved and can find better solutions than its competitors when dealing with combinatorial optimization *** proposed CCMSCSA performs well in almost all experimental ***,we hope the researchers can see it as an effective method for solving constrained and unconstrained optimization problems.

关键词： crow search algorithm Feature selection Global optimization Metaheuristic algorithms Engineering problems Bionic algorithm

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