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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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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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An improved crow search algorithm based on oppositional forgetting learning

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APPLIED INTELLIGENCE 2022年第7期52卷 7905-7921页

作者： Xu, Wei Zhang, Ruifeng Chen, Lei Tianjin Univ Sch Microelect Tianjin 300072 Peoples R China Tianjin Univ Commerce Sch Informat Engn Tianjin 300134 Peoples R China

crow search algorithm (CSA) is a novel meta-heuristic optimization algorithm based on the intelligent behavior of the crow population. Although the algorithm has the characteristics of few parameters, simple structure, and easy application, it has the shortcomings of low convergence accuracy and imbalance between exploration and exploitation capabilities. The occurrence of these issues is originated from crow learning from only one goal. In this paper, an improved crow search algorithm based on oppositional forgetting learning (OFLCSA) is proposed. In order to solve the shortcomings of CSA, the forgetting mechanism is introduced to help the algorithm jump out of the local optimum. Moreover, the opposition-based learning (OBL) strategy is combined with the forgetting mechanism to increase the probability of approaching the optimal solution. In addition, the elite crow and adaptive flight length are used to improve the convergence accuracy. To verify the performance of OFLCSA, experiments were conducted on the Congress on Evolutionary Computation (CEC) 2014 and CEC 2019 benchmark functions. OFLCSA is compared with the ten state-of-the-art meta-heuristic optimization algorithms. Moreover, OFLCSA is evaluated by four real-world engineering applications. Experimental results and analysis show that OFLCSA is a competitive meta-heuristic optimization algorithm.

关键词： Meta-heuristic algorithm crow search algorithm Function optimization Forgetting mechanism Opposition-based learning

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An improved opposition-based crow search algorithm for biodegradable material classification

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SAR AND QSAR IN ENVIRONMENTAL REsearch 2022年第5期33卷 403-415页

作者： Al-Fakih, A. M. Algamal, Z. Y. Qasim, M. K. Univ Teknol Malaysia Fac Sci Dept Chem Johor Baharu Kagawa Malaysia Sanaa Univ Dept Chem Fac Sci Sanaa Yemen Univ Mosul Dept Stat & Informat Mosul Iraq Univ Mosul Dept Gen Sci Mosul Iraq

The development of a reliable quantitative structure-activity relationship (QSAR) classification model with a small number of molecular descriptors is a crucial step in chemometrics. In this study, an improvement of crow search algorithm (CSA) is proposed by adapting the opposite-based learning (OBL) approach, which is named as OBL-CSA, to improve the exploration and exploitation capability of the CSA in quantitative structure-biodegradation relationship (QSBR) modelling of classifying the biodegradable materials. The results reveal that the performance of OBL-CSA not only manifest in improving the classification performance, but also in reduced computational time required to complete the process when compared to the standard CSA and other four optimization algorithms tested, which are the particle swarm algorithm (PSO), black hole algorithm (BHA), grey wolf algorithm (GWA), and whale optimization algorithm (WOA). In conclusion, the OBL-CSA could be a valuable resource in the classification of biodegradable materials.

关键词： crow search algorithm biodegradable materials descriptor selection opposition-based learning classification QSBR

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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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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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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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Heart Disease Classification using crow search algorithm 5

Heart Disease Classification using Crow Search Algorithm

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Maragatharajan, M. Naveen, P. Nirmala, E. Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh Kothrikalan India Kpr Institute of Engineering and Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798331504403

This work introduces cutting-edge research to predict heart diseases. This application designed for accurate and personalized assessment of cardiovascular health. This work aims to provide an overall understanding of an individual's heart health by integrating these multifaceted data points. This work utilizes a vast dataset to establish correlations and patterns, enabling it to categorize users into specific risk groups or diagnose potential heart diseases. Through continuous learning and adaptation, the work refines its predictive capabilities, ensuring that it remains up-to-date with emerging medical knowledge. The work not only assists in early detection of cardiovascular issues but also aids in personalized risk assessment, guiding users towards lifestyle modifications and preventive measures based on their unique health profiles. This innovative solution holds the potential to revolutionize the field of cardiovascular health assessment, offering a proactive and data-driven approach to identify and manage heart diseases efficiently. This study considered two well-known machine learning models namely Extreme learning machine algorithms (ELM) and crow search algorithm (CSA). The collected dataset consists of 10000 samples. We have attained 92.5% accuracy with the data set. The incorporation of machine learning models ensures scalability, adaptability, and precision, making it a valuable tool for both healthcare professionals and individuals striving for proactive heart care. © 2024 IEEE.

关键词： Accuracy crow search algorithm Data preprocessing Extreme learning machine algorithm Feature selection Heart diseases

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