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A modified crow search algorithm based on group strategy and adaptive mechanism

ENGINEERING OPTIMIZATION 2024年第4期56卷 625-643页

作者： Liu, Zhao Wang, Wenjie Shi, Guohong Zhu, Ping Shanghai Jiao Tong Univ Sch Design Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai Peoples R China Shanghai Jiao Tong Univ Natl Engn Res Ctr Automot Power & Intelligent Cont Sch Mech Engn Shanghai Peoples R China Pan AsiaTechn Automot Ctr Co Ltd Shanghai Peoples R China

As a swarm-based metaheuristic algorithm, the crow search algorithm (CSA) has attracted a lot of attention owing to its simplicity and flexibility. However, CSA tends to have low efficiency. To improve the optimization efficiency, this article proposes a modified version of CSA based on group strategy with an adaptive mechanism (GCSA). On this basis, crows are divided into multiple competing groups, and are assigned different roles and statuses. Then, the group strategy including different search modes is implemented to increase the solution diversity and search efficiency. Moreover, benefiting from the adaptive mechanism, the search range of crows changes in different stages to balance exploration and exploitation capabilities. To evaluate the performance of the proposed algorithm, 35 benchmark test functions (including 10 CEC2020 functions) and three engineering design problems are solved by GCSA and 11 other algorithms. The results prove that GCSA generally provides more competitive results than other metaheuristic algorithms.

关键词： Metaheuristic algorithm crow search algorithm group strategy adaptive mechanism engineering design problems

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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 hybrid crow search algorithm for solving the DNA fragment assembly problem

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 102卷 44-56页

作者： Allaoui, Mohcin Ahiod, Belaid El Yafrani, Mohamed Mohammed V Univ Rabat Fac Sci Associated Unit CNRST URAC 29 LRIT BP 1014 Rabat Morocco

The sequencing of DNA goes through a step of fragment assembly, this step is known as DNA fragment assembly problem (FAP). Fragment assembly is considered as an NP-hard problem, which means there is no known polynomial-time exact approach, hence the need for meta-heuristics. Three major strategies are widely used to tackle this problem: greedy graph-based algorithms, de Bruijn graphs, and the overlay-layout-consensus (OLC) approach. In this paper, we propose an adaptation of the novel crow search algorithm (CSA) to solve the DNA fragment assembly problem following the OLC model. In order to accelerate the search process and improve the quality of the solutions, we combined CSA with a local search method. Using this combination we were able to obtain very accurate solutions for all the instances of the DNA fragment assembly problem we tested. In fact, our algorithm outperformed all other algorithms designed for the same purpose. Our contribution consists in the implementation of a new assembler for the DNA fragment assembly problem capable of finding for the first time the optimal solutions for 20 out of 30 instances. The approach we proposed to adapt CSA for a discrete optimization problem is a novelty. We preserved the semantics of the original algorithm by applying standard operators from evolutionary algorithms. Following the same approach can make adapting new algorithms for discrete problems more accessible and more efficient compared to mapping algorithms designed for continuous optimization to combinatorial problems. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： crow search algorithm DNA fragment assembly Discrete optimization PALS2-many*

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A hybrid traffic controller system based on flower pollination algorithm and type-2 fuzzy logic optimized with crow search algorithm for signalized intersections

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SOFT COMPUTING 2024年第11-12期28卷 7227页

作者： Korkmaz, Ersin Akgungor, Ali Payidar Kirikkale Univ Engn & Nat Sci Fac Dept Civil Engn TR-71451 Kirikkale Turkiye

In this study, a hybrid traffic signal control (HTSC) system based on phase and time optimization was developed. The Flower Pollination algorithm (FPA) approach was used for phase optimization, while Type-2 Fuzzy Logic, optimized with the crow search algorithm (CSA), was utilized for time optimization. The hybrid system's performance was investigated using nine different traffic conditions and four different intersection geometries. The hybrid system was compared with three controller systems which are a fixed-time signal controller, a signal controller based on the FPA approach (FPA_TSC), and the optimized Type-1 fuzzy logic signal controller (Type-1 FL-TSC). The HTSC approach achieved the best performance with about 32% improvement over the fixed-time traffic controller and it showed 5% and 6% better performance than the FPA_TSC and Type-1 FL-TSC, respectively. Considering the performance of the new hybrid system, it is effective in minimizing delays and driver dissatisfaction occurring from signalization. It also contributes to the reduction of emissions and fuel consumption. The HTSC approach can be used as an alternative signal control method in the control of intersections with high traffic volume due to its fast and effective performance.

关键词： Traffic signal controller Flower pollination algorithm Type-2 fuzzy logic crow search algorithm Phase and signal optimization

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Reliability-based design and implementation of crow search algorithm for longitudinal dispersion coefficient estimation in rivers

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ENVIRONMENTAL SCIENCE AND POLLUTION REsearch 2021年第27期28卷 35971-35990页

作者： Ghaemi, Alireza Zhian, Tahmineh Pirzadeh, Bahareh Monfared, Seyedarman Hashemi Mosavi, Amir Univ Sistan & Baluchestan Dept Civil Engn Zahedan Iran Newcastle Univ Sch Engn Water Secur & Sustainable Dev Hub Newcastle Upon Tyne Tyne & Wear England Selye Janos Univ Fac Informat Komarom 94501 Slovakia Norwegian Univ Life Sci Sch Econ & Business N-1430 As Norway Obuda Univ John von Neumann Fac Informat H-1034 Budapest Hungary

The longitudinal dispersion coefficient (LDC) of river pollutants is considered as one of the prominent water quality parameters. In this regard, numerous research studies have been conducted in recent years, and various equations have been extracted based on hydrodynamic and geometric elements. LDC's estimated values obtained using different equations reveal a significant uncertainty due to this phenomenon's complexity. In the present study, the crow search algorithm (CSA) is applied to increase the equation's precision by employing evolutionary polynomial regression (EPR) to model an extensive amount of geometrical and hydraulic data. The results indicate that the CSA improves the performance of EPR in terms of R-2 (0.8), Willmott's index of agreement (0.93), Nash-Sutcliffe efficiency (0.77), and overall index (0.84). In addition, the reliability analysis of the proposed equation (i.e., CSA) reduced the failure probability (P-f) when the value of the failure state containing 50 to 600 m(2)/s is increasing for the P-f determination using the Monte Carlo simulation. The best-fitted function for correct failure probability prediction was the power with R-2 = 0.98 compared with linear and exponential functions.

关键词： Artificial intelligence crow search algorithm Longitudinal dispersion coefficient Machine learning Monte Carlo simulation Natural rivers Reliability analysis

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Merging Lion with crow search algorithm for Optimal Location and Sizing of UPQC in Distribution Network

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2020年第2期31卷 377-392页

作者： Gaddala, Kaladhar Raju, P. Sangameswara St Anns Coll Engn & Technol Chirala Chirala Chittoor India Siddhartha Inst Sci & Technol Puttur Puttur India

Unified power quality conditioner (UPQC) is exploited to alleviate the issues associated with voltage swell/dip in source voltage, and it regulates the load voltage absolutely. It is deployed to resolve the entire issues associated with current and voltage harmonics and enhance power quality. In order to solve those issues, optimal sizing and location of UPQC are adopted by the researchers, and it is still in progress. However, the power quality issues like power loss, UPQC cost and voltage stability index (VSI) are not analysed and considered. Hence, the tactics used in this paper help to design an optimal location and sizing of the UPQC in power system using a nonlinear multi-objective function. The objective function considers the minimization of power loss, UPQC cost and VSI. For attaining this objective, this paper hybridizes two well-performed optimization algorithms called lion algorithm (LA) and crow search algorithm (CSA). Since the mating of LA is carried out on the basis of CSA update, the proposed algorithm is termed as crow search mating-based lion algorithm (CSM-LA). The current experiment on localizing and sizing of UPQC is carried out in IEEE 33 and IEEE 69 benchmark test bus systems. The performance of the proposed model is distinguished with conventional methods in terms of performance and convergence analysis, and the relevant outcomes are attained which proves the superiority of the proposed model.

关键词： UPQC Distortion Voltage Power Optimal location Lion algorithm crow search algorithm

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RSM and crow search algorithm-Based Optimization of Ultrasonicated Transesterification Process Parameters on Synthesis of Polyol Ester-Based Biolubricant

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2019年第6期44卷 5535-5548页

作者： Arumugam, S. Chengareddy, P. Tamilarasan, A. Santhanam, V. Sri Chandrasekharendra Saraswathi Viswa Mahavidya Dept Mech Engn Kanchipuram 631561 Tamil Nadu India Sri Chandrasekharendra Saraswathi Viswa Mahavidya Dept Chem Kanchipuram 631561 Tamil Nadu India

This study investigated the optimization of ultrasound-aided transesterification process variables for the synthesis of polyol ester-based biolubricant. A response surface methodology based on Box-Behnken design coupled with crow search algorithm methodology was adopted to optimize the ultrasonicated transesterification process variables such as an ultrasonic pulse, amplitude, catalyst concentration, and reaction temperature in order to maximize the yield percentage of polyol ester. The formation of pentaerythritol ester was confirmed by Fourier transform infrared spectroscopy and gas chromatography. The significant properties of the new formulation were compared with synthetic grade compressor oil. Improved yield of 81.4% of pentaerythritol ester was accomplished with an ultrasonic pulse of 15s, an ultrasonic amplitude of 60%, a catalyst concentration of 1.5wt%, and the reaction temperature of 100 degrees C. This optimization study suggested that ultrasonicated successive transesterification process is an effective method in substituting conventional successive transesterification process for the synthesis of pentaerythritol ester-based biolubricant.

关键词： Box-Behnken method crow search algorithm Pentaerythritol ester Ultrasonication Response surface methodology Transesterification

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Improved crow search algorithm Optimized Extreme Learning Machine Based on Classification algorithm and Application

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IEEE ACCESS 2021年 9卷 20051-20066页

作者： Cao, Li Yue, Yinggao Zhang, Yong Cai, Yong Wenzhou Univ Oujiang Coll Wenzhou 325035 Peoples R China Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Sichuan Peoples R China Hubei Univ Arts & Sci Comp Sch Xiangyang 441053 Peoples R China

In view of the problems of the connection weights and thresholds of the extreme learning machine are randomly generated before training and remain unchanged during the training process, the number of hidden layer nodes is pre-allocated, and the hidden layer parameters are randomly selected. Too many hidden layer nodes not only make the network more complex but also reduce the generalization ability of the algorithm. Aiming at this problem, an improved crow search algorithm is proposed to optimize the extreme learning machine. Based on the analysis of the limitations of the original crow search algorithm, a particle swarm algorithm search strategy is proposed to enhance the global search capability. In the latter part of the algorithm iteration, Gaussian function is added, and the penalty coefficient of the function is used for local disturbance, gradually reducing the amplitude of the search trajectory, and then adaptively adjusting the parameters to avoid being attracted by local extremum. Finally, the improved crow search algorithm is used to optimize the hidden layer neurons and connection weights of the extreme learning machine neural network, so as to obtain accurate prediction results. Through function fitting, regression data set fitting and classification data set for classification experiment verification, the proposed algorithm has higher training speed and efficiency. At the same time, this method is not only significantly higher than the traditional ELM method, but also obtains a more compact network structure, which is an effective neural network optimization algorithm.

关键词： Classification algorithms Optimization Particle swarm optimization Extreme learning machines Training search problems Neurons Extreme learning machine crow search algorithm particle swarm algorithm fitting regression data classification

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A novel approach based on crow search algorithm for optimal selection of conductor size in radial distribution networks

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2017年第2期20卷 391-402页

作者： Abdelaziz, Almoataz Y. Fathy, Ahmed Ain Shams Univ Fac Engn Elect Power & Machine Dept Cairo Egypt Zagazig Univ Fac Engn Elect Power & Machine Dept Zagazig Egypt

The selection process of conductor size in radial distribution network is very essential issue to improve the performance of the network. The true conductor size selection leads to less power loss, achieve improvement of the bus voltage profile and obtain reduction in the annual operating cost of the system. This paper proposes a novel approach based on crow search algorithm (CSA) for optimal selection of the conductors in a radial distribution network. The CSA is a recent meta-heuristic algorithm which is based on the intelligent behavior of crows. The objective function presented in our work is the sum of conductor capital cost and the conductor energy loss cost. The proposed constraints are bus voltage limits and the current capacity of the conductor. The independent variables are the type and the size of conductor such that minimizing the proposed objective function. The proposed approach is applied on two different network topologies, the first one is 16-bus system and the second is large scale system of 85-bus system. A sensitivity analysis of the CSA controlling parameters are also studied for 16-bus system. The obtained results via CSA are compared to previous works' results;the CSA results encourage the usage of the proposed approach in optimal selecting the conductor type and size in the distribution network. (C) 2017 Karabuk University. Publishing services by Elsevier B.V.

关键词： Optimal conductor size crow search algorithm Distribution network

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