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An Improved crow search algorithm Based on Spiral search Mechanism for Solving Numerical and Engineering Optimization Problems

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IEEE ACCESS 2020年 8卷 92363-92382页

作者： Han, Xiaoxia Xu, Quanxi Yue, Lin Dong, Yingchao Xie, Gang Xu, Xinying Taiyuan Univ Technol Coll Elect & Power Engn Dept Automat Taiyuan 030024 Peoples R China Taiyuan Univ Technol Sch Elect & Informat Engn Taiyuan 030024 Peoples R China

The crow search algorithm (CSA) is a new intelligent optimization algorithm based on the behavior of the crow population, which has been proven to perform well. However, its simple search mechanism also leads to the algorithm & x2019;s slow convergence speed and its ease of falling into local optimization when solving complex optimization problems. In order to overcome these problems, this paper proposes an improved CSA (ISCSA) based on a spiral search mechanism. By introducing a weight coefficient, an optimal guidance position and a spiral search mechanism, the position equation was updated to accelerate the convergence of the algorithm and make the exploration and exploitation of CSA more balanced. Meanwhile, adding Gaussian variation and random perturbation strategy made it difficult for the algorithm to fall into local optimization. The advantages of the proposed ISCSA were evaluated using 23 benchmark functions and four classical engineering design problems. The experimental and statistical results of 23 test functions showed that the proposed ISCSA could escape from the local optima with higher accuracy and faster convergence than both the CSA and other meta-heuristic optimization algorithms. The calculation results of the four engineering optimization problems showed that compared with other algorithms, ISCSA can solve the practical optimization problem well and has been proved to have strong competitiveness and good performance.

关键词： Optimization Spirals search problems Perturbation methods Convergence Mathematical model Benchmark testing crow search algorithm weight coefficient spiral search mechanism engineering design optimization

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A Novel crow search algorithm Auto-Drive PSO for Optimal Allocation and Sizing of Renewable Distributed Generation

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IEEE ACCESS 2020年 8卷 27807-27820页

作者： Farh, Hassan M. H. Al-Shaalan, Abdullah M. Eltamaly, Ali Mohamed Al-Shamma'a, Abdullrahman A. King Saud Univ Coll Engn Elect Engn Dept Riyadh 11421 Saudi Arabia King Saud Univ Sustainable Energy Technol Ctr Riyadh 11421 Saudi Arabia Mansoura Univ Elect Engn Dept Mansoura 35516 Egypt Taiz Univ Coll Engn Dept Mechatron Engn Taizz 6803 Yemen

The penetration of renewable distributed generations (RDGs) into traditional distribution systems (TDSs) remedies many of its deficiencies and shortcomings. Also, it provides mutual technical, economic and environmental benefits for both electricity companies and their customers as well. With a 25% load increase for the standard IEEE 30-bus system, buses 19, 26 and 30 have the lowest voltage magnitudes among all buses. Therefore, these weak buses are selected initially to allocate RDGs. Three cases, namely, one RDG allocated, two RDGs allocated and three RDGs allocated, of RDGs insertion are covered. A novel crow search algorithm auto-drive particle swarm optimization (CSA-PSO) technique is proposed for the first time in this study to specify the optimal allocation, sizing, and number of RDGs based on the total cost and power losses minimization objectives. A new reduction percent formula is used to estimate the reduction in total cost and the total power losses. These will help us to discern between the best cases based on total cost minimization and those based on total power losses minimization to pick up the best among all best cases. In brief, RDGs allocated on buses 19 and 30 is the best among all cases based on total cost reduction and total power losses reduction. Therefore, buses 19 and 30 are recommended to allocate a wind farm and a solar photovoltaic, respectively based on technical and economic issues. Finally, the simulation findings revealed the superiority of the CSA-PSO algorithm in solving the optimal power flow problem with RDGs compared to the state-of-the-art metaheuristic techniques.

关键词： crow search algorithm particle swarm optimization renewable distributed generation sizing solar allocation photovoltaic wind farm

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ELM evaluation model of regional groundwater quality based on the crow search algorithm

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ECOLOGICAL INDICATORS 2017年 81卷 302-314页

作者： Liu, Dong Liu, Chunlei Fu, Qiang Li, Tianxiao Imran, Khan M. Cui, Song Abrar, Faiz M. Northeast Agr Univ Sch Water Conservancy & Civil Engn Harbin 150030 Heilongjiang Peoples R China Northeast Agr Univ Minist Agr Key Lab Effect Utilizat Agr Water Resources Harbin 150030 Heilongjiang Peoples R China Northeast Agr Univ Heilongjiang Prov Collaborat Innovat Ctr Grain Pr Harbin 150030 Heilongjiang Peoples R China Northeast Agr Univ Key Lab Water Saving Agr Ordinary Univ Heilongjia Harbin 150030 Heilongjiang Peoples R China

According to the multi-parameter evaluation of groundwater quality, an evaluation model of groundwater quality based on the improved Extreme Learning Machine (ELM) was proposed to resolve fuzziness of the water quality evaluation and incompatibility of water parameters. A training sample set and testing sample set were randomly generated according to the classification standards of groundwater quality, then crow search algorithm (CSA) was used to optimize the input weights and thresholds of hidden-layer neurons of the ELM;thus, the CSA-ELM evaluation model of groundwater quality was constructed based on optimization of the ELM by the CSA. Base on the training sample set and testing sample set, the CSA-ELM model was tested. The test results indicate that the evaluating precision and generalization ability of the CSA-ELM model reach a high level and can be used for comprehensive evaluations of groundwater quality. The Jiansanjiang Administration in Heilongjiang Province, China, was used as an example;the groundwater quality of 15 farms in this region was evaluated based on the CSA-ELM model. The groundwater quality in this region was generally good, and the groundwater quality appeared to have spatial distribution characteristics. Compared with the Nemerow Index Method (NIM), the CSA-ELM evaluation model of groundwater quality is more reasonable and can be used for the comprehensive evaluation of groundwater quality. The stability of the NIM, ELM model, back propagation (BP) model and CSA-ELM model was analyzed using the theory of serial number summation and Spearman's correlation coefficient. The stability of the NIM and BP model in groundwater quality evaluation was poor, while the stability of the ELM model and CSA-ELM model was relatively superior. The ranked results of stability are CSA-ELM model > ELM model > NIM > BP model. The reliability of the NIM, ELM model, BP model and CSA-ELM model was analyzed using the theory of distinction degree. The reliability of t

关键词： crow search algorithm Extreme learning machine Stability Reliability Groundwater quality

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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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Study on sentiment classification strategies based on the fuzzy logic with crow search algorithm

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SOFT COMPUTING 2022年第22期26卷 12611-12622页

作者： AL-Deen, Mazen Sharaf Yu, Lasheng Aldhubri, Ali Qaid, Gamil R. S. Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Hodeida Univ Fac Comp Sci & Engn Dept Comp Engn Hodeidah Yemen

In recent times, sentiment analysis research has gained wide popularity. That situation causes the importance of online applications that allow users to express their opinions on events, services, or products through social media applications such as Twitter, Facebook, and Amazon. This paper proposes a novel sentiment classification method according to the fuzzy rule-based system (FRBS) with the crow search algorithm (CSA). FRBS is used to classify the polarity of sentences or documents, and the CSA is employed to optimize the best output from the fuzzy logic algorithm. The FRBS is applied to extract the sentiment and classify its polarity into negative, neutral, and positive. Sometimes, the outputs of the FRBS must be enhanced, especially since many variables are present and the rules between them overlap. For such cases, the CSA is used to solve this limitation faced by FRBS to optimize the outputs of FRBS and achieve the best result. This study compares the performance of the proposed model with different machine learning algorithms, such as SVM, maximum entropy, boosting, and SWESA. It tests the model on three famous data sets collected from Amazon, Yelp, and IMDB. Experimental results demonstrate the effectiveness of the proposed model and achieve competitive performance in terms of accuracy, recall, precision, and the F-score.

关键词： Sentiment analysis Fuzzy rule-based system Membership function crow search algorithm

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A crow search algorithm integrated with dynamic awareness probability for cellular network cost management

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JOURNAL OF SUPERCOMPUTING 2022年第17期78卷 19046-19069页

作者： Qamar, Shamimul Azeem, Abdul Alam, Tanweer Ahmad, Izhar King Khalid Univ Coll Sci & Arts Comp Sci & Engn Dept Dhahran Al Janoub Campus Abha 64261 Saudi Arabia Jamia Millia Islamia Dept Elect Engn Fac Engn & Technol Delhi 110025 India Islamic Univ Madinah Fac Comp & Informat Syst Madinah Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Intelligent Secure Syst Dept Math Dhahran 31261 Saudi Arabia

This research presents a crow search algorithm (CSA) for reducing the cellular network cost when reporting the cell planning (RCP) scheme. In cellular systems, the RCP scheme is used to maintain location. We employ CSA as an optimization tool because it is a bio-inspired optimization technique. In this study, CSA uses the cellular network's diversity to optimize the cost of reporting the RCP scheme. The cost of location management is calculated using dynamic awareness probability (DAP) and a CSA for various cellular network sizes. With each iteration of the CSA, the dynamic properties of the DAP are used to change the decision threshold. This provides additional freedom and enhances decision-making abilities. As a result, the set awareness probability allows for a cheaper cost per call arrival. Extensive simulations are used to test and evaluate the suggested method's performance. The experiments are carried out with 4 x 4, 6 x 6, and 8 x 8 cells in current cellular systems. The recommended CSA is used to measure performance in groups of 50, 100, 150, and 200 people. Multiple graphs displaying statistical measurements, convergence rates, and other data are used to present the conclusions. It was determined that scaling up from a smaller to a larger network lowers the cost per call arrival by about 12%. This shows a possible vision of the proposed CSA's vast range of uses and needs more research to improve existing cellular services.

关键词： crow search algorithm Location management Cellular network Data communication Optimization techniques

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Improving multi-criterion optimization with chaos: a novel Multi-Objective Chaotic crow search algorithm (vol 29, pg 319, 2017)

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NEURAL COMPUTING & APPLICATIONS 2018年第8期29卷 337-337页

作者： Hinojosa, Salvador Oliva, Diego Cuevas, Erik Pajares, Gonzalo Avalos, Omar Galvez, Jorge Univ Complutense Madrid Fac Informat Dpto Ingn Software & Inteligencia Artificial E-28040 Madrid Spain Univ Guadalajara Div Elect & Computac CUCEI Ave Revoluc 1500 Guadalajara Jalisco Mexico

In the original publication, algorithm 1 and algorithm 2 are incorrectly published with the same content.

关键词： Multi-Objective optimization Evolutionary algorithms Chaotic maps crow search algorithm

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An improved crow search algorithm for high-dimensional problems

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017年第6期33卷 3597-3614页

作者： Jain, Mohit Rani, Asha Singh, Vijander Univ Delhi ICE Div Netaji Subhas Inst Technol Sec 3 New Delhi India

crow search algorithm (CSA) is a recently proposed metaheuristic optimizer inspired by the intelligent behaviour of crows with attributes like simplicity and ease of implementation. CSA is claimed superior and more effective in optimizing a variety of constrained engineering design problems in comparison to other state-of-art algorithms. In the present work, CSA is applied to high dimensional optimization problems and it is found that CSA suffers from premature convergence which leads to lower precision and less accuracy in optimization or sometimes failure. Therefore an improvement in CSA (ICSA) is suggested to solve high-dimensional global optimization problems efficiently. The balance between exploitation and exploration capabilities of CSA is improved by introducing experience factor, adaptive adjustment operator and Levy flight distribution in position updating mechanism of crows. Levy flight distribution promotes continuous exploration of search space and prevents premature convergence by escaping from local optimum at any stage. The performance of ICSA is validated on high-dimensional nonlinear scalable benchmark test functions. The proposed improvement in CSA makes it highly competitive and less sensitive to function dimensions. ICSA is also found superior to other well established optimizers.

关键词： crow search algorithm Levy flight Unconstrained optimization

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Sine-cosine crow search algorithm: theory and applications

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NEURAL COMPUTING & APPLICATIONS 2020年第12期32卷 7725-7742页

作者： Khalilpourazari, Soheyl Pasandideh, Seyed Hamid Reza Concordia Univ Dept Mech Ind & Aerosp Engn MIAE Montreal PQ Canada Kharazmi Univ Fac Engn Dept Ind Engn Tehran Iran

In this paper, we propose a new hybrid algorithm called sine-cosine crow search algorithm that inherits advantages of two recently developed algorithms, including crow search algorithm (CSA) and sine-cosine algorithm (SCA). The exploration and exploitation capabilities of the proposed algorithm have significantly improved. Performance of the so-called SCCSA was evaluated in unimodal, multimodal, fixed-dimensional multimodal and composite benchmark functions using robust measures. Based on in-depth analyses and statistical information, we showed that the suggested methodology could provide promising solutions comparing to other state-of-the-art algorithms.

关键词： Sine-cosine crow search algorithm Global optimization crow search algorithm Sine-cosine algorithm

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Solving dimension reduction problems for classification using Promoted crow search algorithm (PCSA)

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COMPUTING 2022年第6期104卷 1255-1284页

作者： Samieiyan, Behrouz MohammadiNasab, Poorya Mollaei, Mostafa Abbas Hajizadeh, Fahimeh Kangavari, Mohammadreza Iran Univ Sci & Technol Dept Comp Engn Tehran Iran Univ Kashan Dept Elect & Comp Engn Kashan Iran Univ Tehran Dept Elect & Comp Engn Tehran Iran Kharazmi Univ Dept Elect & Comp Engn Tehran Iran

In recent years, with the increasing volume of databases, the removal of redundant features has become an essential thing in classification. A smaller subset of features is selected using feature selection algorithm. One of the famous algorithms of feature selection methods is the crow search algorithm (CSA). This algorithm's popularity can be mentioned in the algorithm's implementation and process and the impressive results compared to the previous algorithms. Despite all these benefits, this algorithm suffers from problems such as unbalanced global and local search. It is also stuck in local optimization due to the search approach's inadequacy. In this paper, a new algorithm based on CSA is introduced. In order to overcome the shortcoming, four fundamental changes have been made to CSA. (i) The algorithm uses the concept of dynamic awareness probability to solve the balance between exploitation and exploration. Then, a new approach is introduced for each part of the search that improves crows' search performance both (ii) locally and (iii) globally. Also, as the last change, (iv) the concept of chaos is used to increase the algorithm's convergence rate. The proposed method has been tested and compared with ten well-known algorithms in this field on the same datasets and has performed on average 20% better in the feature reduction index and 2.5% in the fitness index, while has a lower performance in accuracy by only 1.5%. Practical results show that the algorithm changes have provided attractive results compared to other algorithms in this field in the mentioned metrics.

关键词： Feature selection crow search algorithm Data reduction Meta-heuristic method Classification

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