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13th Computer Science Online Conference

作者： Olivares, Rodrigo Olivares, Pablo Rios, Victor Oliveros, Alejandro Univ Valparaiso Escuela Ingn Informat Valparaiso Chile

ISBN: (纸本)9783031705946;9783031705953

This study introduces a hybrid technique that combines the crow search algorithm (CSA) with Proximal Policy Optimization (PPO) from reinforcement learning for feature selection in large datasets. The integration of PPO allows the algorithm to adapt and learn during its execution, thereby improving efficiency and accuracy in identifying relevant attributes. We evaluate the effectiveness of this hybrid approach using the Parkinson's Disease Classification (PDC) dataset. Our computational results demonstrate promising improvements in solution quality, convergence speed, and reduction in execution time, especially in high-dimensional environments. This work not only highlights the feasibility of combining bioinspired algorithms with reinforcement learning techniques but also opens new avenues for optimizing feature selection processes in machine learning.

关键词： crow search algorithm Proximal Policy Optimization Reinforcement Learning

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Developing a two stage optimized random vector functional link neural network based predictor model utilizing a swift crow search algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第3期28卷 1-36页

作者： Samal, Sidharth Dash, Rajashree Siksha O Anusandhan Comp Sci & Engn Dept Bhubaneswar India

Investment in financial markets, such as indices, derivatives, commodities, currency exchanges, and so forth, is becoming more and more popular among the general public with the growth of economy. Accurate financial time-series prediction is now vital for investors, researchers, and investment firms due to its high-risk, high reward nature. For many years, specialists have tried to look into the underlying patterns of the market, anticipate future prices, and determine which way the market would move. Therefore applying modern forecasting models can be quite useful for comprehending and foreseeing market changes. In light of this, this paper proposes a novel two-stage optimized random vector functional link network (RVFLN) based predictor model with the dual goals of forecasting the upcoming stock index close price and predicting whether the upcoming trend will be upward or downward. For selecting optimal input features and weights of RVFLN, an improved variant of the crow search algorithm (CSA) known as the swift crow search algorithm (SCSA) is suggested in the study. In order to achieve the ideal balance between exploration and exploitation, improving convergence, the SCSA is developed by introducing the good point set (GPS) strategy in initial population generation, the chaotic map and mutation operator of differential evolution (DE) in the position update scheme, and catfish behavior in the search process of the original CSA. Specifically SCSA is used at two stages in the process of building the model. To improve the prediction performance, initially features from multiple domains are accumulated, including raw, statistical, technical, and decomposed domains. Since a broader feature space can lead to "the curse of dimensionality" and "overfitting," a binary feature selection algorithm utilizing SCSA is used in conjunction with RVFLN in the first stage. In the subsequent stage following feature selection, the randomly generated input weights of RVFLN were optimiz

关键词： Financial trend analysis Feature selection Random vector functional link network crow search algorithm

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Optimizing Rural MG's Performance: A Scenario-Based Approach Using an Improved Multi-Objective crow search algorithm Considering Uncertainty

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ENERGIES 2025年第2期18卷 294-294页

作者： Taabodi, Mohammad Hossein Niknam, Taher Sharifhosseini, Seyed Mohammad Aghajari, Habib Asadi Bornapour, Seyyed Mohammad Sheybani, Ehsan Javidi, Giti Shiraz Univ Technol Dept Elect Engn Shiraz *** Iran Univ Yasuj Elect Engn Dept Yasuj *** Iran Univ S Florida Muma Coll Business Sch Informat Syst & Management Tampa FL 33620 USA

In recent years, the growth of utilizing rural microgrids (RMGs) has been accompanied by various challenges. These necessitate the development of appropriate models for optimal generation in RMGs and RMGs' coordination. In this paper, two distinct models for RMGs are presented. The first model includes an islanded rural microgrid (IRMG) and the second model consists of three RMGs that are interconnected with one another and linked to the distribution network. The proposed models take into account the uncertainty in load, photovoltaics (PVs), and wind turbines (WTs) with consideration of their correlation by using a scenario-based technique. Three objective functions are defined for optimization: minimizing operational costs including maintenance and fuel expenses, reducing voltage deviation to maintain power quality, and decreasing pollution emissions from fuel cells and microturbines. A new optimization method, namely the Improved Multi-Objective crow search algorithm (IMOCSA), is proposed to solve the problem models. IMOCSA enhances the standard crow search algorithm through three key improvements: an adaptive chaotic awareness probability to better balance exploration and exploitation, a mutation mechanism applied to the solution repository to prevent premature convergence, and a K-means clustering method to control repository size and increase algorithmic efficiency. Since the proposed problem is a multi-objective non-linear optimization problem with conflicting objectives, the idea of the Pareto front is used to find a group of optimal solutions. To assess the effectiveness and efficiency of the proposed models, they are implemented in two different case studies and the analysis and results are illustrated.

关键词： correlation crow search algorithm distributed power generation microgrid optimization uncertainty

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Feature selection based on dynamic crow search algorithm for high-dimensional data classification

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 250卷

作者： Jiang, He Yang, Ye Wan, Qiuying Dong, Yao Xi An Jiao Tong Univ Sch Econ & Finance Xian 710061 Shannxi Peoples R China Cent China Normal Univ Sch Econ & Business Adm Wuhan 430079 Hubei Peoples R China JiangXi Univ Finance & Econ Sch Stat & Data Sci Nanchang 330013 Jiangxi Peoples R China

High-dimensional biomedical data plays an important role in disease diagnosis and classification. To analyze the high-dimensional biomedical data, machine learning algorithms are widely used. However, a great amount of redundant, irrelevant or weak correlation features are prevalent in these datasets, which significantly deteriorate the capability of machine learning techniques. It is necessary to select the most informative features and discard the useless ones for enhancing the classification accuracy and reducing the dimensionality. To address this issue, an improved crow search algorithm (CSA) which is known as the Dynamic CSA (DCSA) is proposed to find the optimal feature subset in high-dimensional classification tasks. In DCSA, three modifications are investigated. Firstly, we propose a dynamic bi-level awareness probability to guide the transition between global search and local search. Consequently, the balance between exploration and exploitation is improved. Secondly, levy flight with a unfixed step length control parameter is employed as the global search and the random search mechanism of the original CSA is abandoned. Thirdly, a dynamic flight length strategy is adopted to enhance the local search and speed up convergence. All in all, DCSA is developed as a wrapper feature selection model, where KNN classifier is applied as the feature subset evaluator. The performance of DCSA is measured on seven high-dimensional biomedical datasets. Experimental results show that DCSA outperforms other state-of-the-art methods. Specifically, DCSA achieved the highest average accuracy in six data. In terms of numbers of selected features, DCSA gained the smallest subset size in a seven data. And for SRBCT data, the prediction accuracy of DCSA is at 100%.

关键词： Feature selection High-dimensional data classification crow search algorithm

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AN ADAPTIVE DYNAMIC NEIGHBORHOOD crow search algorithm FOR SOLVING PERMUTATION FLOW SHOP SCHEDULING PROBLEMS

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2024年第1期20卷 84-111页

作者： Zhao, Cai Wu, Liang-hong Zuo, Ci-li Zhang, Hong-qiang Xiao, Qing Hunan Univ Sci & Technol Sch Mech Engn Xiangtan 411100 Hunan Peoples R China Hunan Univ Sci & Technolog Sch Informat & Elect Engn Xiangtan 411100 Hunan Peoples R China

To effectively solve the permutation flow-shop scheduling problem (PFSP), an adaptive dynamic neighborhood crow search algorithm (AdnCSA) is proposed to minimize the makespan. Firstly, a modified heuristic algorithm based on nawaz-enscore-ham (NEH) was proposed to improve the quality and diversity of the initial population. Secondly, the smallest-position-value (SPV) rule is used to encode the population so that it can handle the discrete sched-uling problem. Lastly, the top 20% of individuals with best fitness was selected to execute neighborhood search, and an adaptive dynamic neighborhood struc-ture is introduced to balance the global and local search ability of the proposed algorithm. To evaluate the effectiveness of the proposed method, the Rec and Taillard benchmarks were used to test the performance. Compared with nine recent metaheuristic method for solving the PFSP, the numerical results pro-duced by the proposed AdnCSA are promising and show great potential for solving the permutation flow-shop scheduling problem.

关键词： Permutation flow shop population initialization adaptive dynamic neighborhood crow search algorithm nawaz-enscore-ham

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A crow search algorithm integrated with Lévy flight and dynamic awareness probability for optimized numerical control machining parameters

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Journal of Engineering and Applied Science 2025年第1期72卷 1-14页

作者： Tian, Bing Xinxiang Vocational and Technical College Xinxiang453000 China

This paper proposes an efficient method for optimizing machining variables with numerical control (NC) achieved through an Enhanced crow search algorithm (ECSA). In contemporary manufacturing, which relies on numerically controlled machining, the efficiency and accuracy of many processes, such as milling, drilling, and turning, heavily depend on the optimum settings for parameters. Traditional optimization methods often require revisions to account for the nonlinearities and multi-dimensional nature of these problems. Hence, it is proposed that the standard CSA should be improved with dynamic awareness probability and Levy flight mechanisms for better exploration and exploitation capabilities. This approach is applied to enhance the critical machining variables of the depth of cut, feed rate, and cutting speed during the roughing, semi-finishing, and finish milling of the SSCK80 machine tool. Thus, the results depicted very noticeable enhancements in this case study concerning surface roughness and improving the cutting performance and machining time compared to existing techniques. The results of this study provide an effective and efficient framework for parameter optimization, thus contributing to advancements in precision and energy-efficient manufacturing. © The Author(s) 2025.

关键词： crow search algorithm Metaheuristics Numerical control machining Parameter optimization

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Adaptive energy loss optimization in distributed networks using reinforcement learning-enhanced crow search algorithm

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SCIENTIFIC REPORTS 2025年第1期15卷 1-28页

作者： Bharath, S. Vasuki, A. SNS Coll Technol Dept Elect & Elect Engn Coimbatore 641035 India Kumaraguru Coll Technol Dept Elect & Commun Engn Coimbatore 641049 India

Modern power distribution network incorporates distributed generation (DG) for numerous benefits. However, the incorporation creates numerous challenges in energy management and to handle the challenges it requires advanced optimization techniques for an effective operation of the network. Unlike traditional methods such as Genetic algorithm (GA), Particle Swarm Optimization (PSO), and standard crow search algorithm (CSA), which suffer from premature convergence and limited adaptability to real-time variations, Reinforcement Learning Enhanced crow search algorithm (RL-CSA) which is proposed in this research work solves network reconfiguration optimization problem and minimize energy losses. Unlike conventional heuristic methods, which follow predefined search patterns, RL-CSA dynamically refines its search trajectory based on real-time feedback, ensuring superior convergence speed and global search efficiency. The novel RL-CSA enables real-time adaptability and intelligent optimization for energy loss reduction in distributed networks. The proposed model validation is performed on the IEEE 33 and 69 Bus test systems considering diverse performance metrics such as power loss reduction, voltage stability, execution time, utilization efficiency for DG deployment, and energy cost minimization. Comparative results show that RL-CSA achieves a 78% reduction in energy losses, limiting power loss to 5 kW (IEEE 33-Bus) and 8 kW (IEEE 69-Bus) whereas traditional models converge at higher loss levels. The execution time is optimized to 1.4 s (IEEE 33-Bus) and 1.8 s (IEEE 69-Bus), significantly faster than GA, PSO, and CSA, making RL-CSA more efficient for real-time power distribution applications. By balancing exploration-exploitation using CSA while adapting search parameters through reinforcement learning, RL-CSA ensures scalability, improved DG utilization (98%), and better voltage stability (< 0.005 p.u.), making it a robust and intelligent alternative for modern smart grid

关键词： Distributed generation Energy loss optimization Reinforcement learning crow search algorithm Adaptive optimization Voltage stability

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Fuzzy Logic Adaptive crow search algorithm for MPPT of a Partially Shaded Photovoltaic System

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IEEE ACCESS 2024年 12卷 119246-119271页

作者： Zeddini, Mohamed Ali Krim, Saber Mansouri, Majdi Mimouni, Mohamed Faouzi Sakly, Anis Univ Monastir Natl Engn Sch Monastir Monastir 5000 Tunisia Univ Monastir Natl Engn Sch Monastir Lab Automat Elect Syst & Environm LASEE Monastir 5000 Tunisia Univ Kairouan Higher Inst Appl Sci & Technol Kasserine Kairouan 3100 Tunisia Texas A&M Univ Qatar Elect & Comp Engn Program Doha Qatar

The arbitrary selection of the crow search algorithm (CSA) parameters, the Awareness Probability (AP) and the Flight Length (fl) results in poor convergence performance and efficiency even if the CSA performs well when solving global optimization problems. In fact, a more search process variety is the outcome of increasing the fl. Furthermore, a higher value of the fl is preferred to guide the optimization process in the direction of global search, whilst a lower fl value directs the algorithm in the direction of local search. In this regard, this study presents a unique Fuzzy Logic adaptive CSA (FL-CSA) for a freestanding Photovoltaic System (PVS) that is based on a Fuzzy Logic (FL) supervisor. Therefore, it is recommended to use the FL supervisor for the online AP and fl, tuning to get superior performance in terms of quick convergence to the GMPP and in terms of high efficiency. Three distinct situations are used to validate the efficacy and speed of the proposed FL-CSA through numerical modeling and experimental testing. The results demonstrate the superiority of the suggested FL-CSA over other traditional approaches, including the Conventional CSA (CCSA), the Conventional Particle Swarm Optimization (CPSO), and the Perturb and Observe (P&O) method. It is true that the maximum power generated by the PVS is extracted by the suggested FL-CSA-based MPPT with average efficiency of 99.93%, whereas the CCSA, the CPSO and P&O record average efficiency of 99.78%, 99.50% and 96.40%, respectively. Additionally, the proposed FL-CSA-based MPPT strategy reduces the convergence time by an average of 42%, 63% and 61%, respectively, in comparison to the CCSA, the CPSO and the P&O MPPT methods.

关键词： Fuzzy logic Convergence Maximum power point trackers Optimization Metaheuristics Tuning search problems Photovoltaic systems Photovoltaic system global maximum power point tracking partial shading conditions crow search algorithm fuzzy logic supervisor adaptive parameters

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Band selection using hybridization of particle swarm optimization and crow search algorithm for hyperspectral data classification

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第9期83卷 26901-26927页

作者： Giri, Ram Nivas Janghel, Rekh Ram Pandey, Saroj Kumar Natl Inst Technol Dept Informat Technol Raipur 492010 CG India GLA Univ Mathura Dept Comp Engn & applicat Mathura India

A Hyperspectral image (HSI) contains numerous spectral bands, providing better differentiation of ground objects. Although the data from HSI are very rich in information, their processing presents some difficulties in terms of computational effort and reduction of information redundancy. These difficulties stem mainly from the fact that the HSI consists of a large number of bands along with some redundant bands. Band selection (BS) is used to select a subset of bands to reduce processing costs and eliminate spectral redundancy. BS methods based on a metaheuristic approach have become popular in recent years. However, most BS methods based on a metaheuristic approach can get stuck in the local optimum and converge slowly due to a lack of balance between exploration and exploitation. In this paper, three BS methods are proposed for HSI data. The first method applies crow search algorithm (CSA) for BS. The other two proposed methods, HPSOCSA_SP and HPSOCSA_SLP, are based on the hybridization of Particle Swarm Optimization (PSO) and CSA. The purpose of these hybridizations is to balance exploration and exploitation in a search process for optimal band selection and fast convergence. In hybridization techniques, PSO and CSA exchange informative data at each iteration. HPSOCSA_SP split the population into two equal parts. PSO is applied to one part and CSA to the other. HPSOCSA_SLP selects half of the top-performing members based on fitness. PSO and CSA are applied to the selected population sequentially. Our proposed models underwent rigorous testing on four HSI datasets and showed superiority over other metaheuristic techniques.

关键词： Hyperspectral image Dimension reduction Band selection Particle swarm optimization crow search algorithm

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Voltage enhancement and loss minimization in a radial network through optimal capacitor sizing and placement based on crow search algorithm

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ENERGY REPORTS 2024年 12卷 4953-4965页

作者： Mathenge, Stephen W. Mharakurwa, Edwell. T. Mogaka, Lucas Dedan Kimathi Univ Technol Dept Elect & Elect Engn Private Bag 10143 Nyeri Kenya Meru Univ Sci & Technol Dept Food Sci POB 97260200 Meru Kenya

In power systems, the distribution network is crucial as it serves as the final stage in delivering energy from generation plants to end users. However, reactive power demand from consumers can create challenges such as low power factor, voltage drops, and increased power losses depending on the distribution system used. This study aims to enhance the performance of radial distribution networks (RDNs) by optimizing capacitor placement and sizing based on the crow search algorithm (CSA). Additionally, the study addresses the Optimal Power Flow (OPF) within the network by employing the Backward/Forward Sweep (BFS) method. The study accesses CSA's effectiveness compared to other metaheuristic optimization techniques through simulation. The simulation results showed that the Voltage Stability Index improved from 0.61 for base case to 0.68 pu with CSA, compared to 0.66 for Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Genetic algorithm (GA), and Teacher Learner Based Optimization (TLBO and 0.67 for Invasive Weed Optimization (IWO). CSA also achieved a 30.41 % reduction in active power losses, outperforming PSO, ABC, GA, and TLBO having 26.61 %, and IWO at 24.92 %. Additionally, application of CSA led to a 29.33 % reduction in reactive power losses, compared to 21.91 % for PSO, ABC, GA, and TLBO, 18.2 % for IWO. In the simulated IEEE 33 bus Radial Distribution Network (RDN) topology, the lowest bus voltage at bus 18 increased from 0.8820 in the base case to 0.9080 with CSA, indicating a 32.9 % improvement in voltage deviation. Furthermore, the optimization resulted in a significant reduction of 3.841 pu in capacitor cost, demonstrating CSA's efficiency in both technical and economic aspects. CSA not only showed favorable results in minimizing power losses and improving voltage profiles but also provided substantial cost savings in capacitor implementation, making it a robust and cost-effective solution for enhancing RDN performance.

关键词： crow search algorithm Particle Swarm Optimization Power losses reduction Radial distributed network Voltage profile

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