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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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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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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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crow search algorithm Based on Neighborhood search of Non-Inferior Solution Set

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IEEE ACCESS 2019年 7卷 52871-52895页

作者： Qu, Chiwen Fu, Yanming Baise Univ Sch Informat Engn Baise 533000 Peoples R China Guangxi Univ Comp & Elect Informat Coll Nanning 530004 Peoples R China

Characterized by less parameter settings, easy implementation, and strong optimization capacity, the crow search algorithm has been successfully applied to solve the optimization problem. As the basic crow search algorithm is a new kind of swarm intelligent algorithm only based on the crow's memory foraging mode, it also contains defects like slow search speed and low optimization precision in later iterations, which are especially obvious for the optimization of high-dimensional functions. In order to overcome these shortcomings, a new crow search algorithm based on neighborhood search of non-inferior solution set (NICSA) is proposed. The proposed algorithm makes the crow individual choose the memory search mode or neighborhood search mode automatically in the course of evolution by the determination factor of non-inferior solution. With this strategy, the local exploitation and the global exploration of the algorithm became more balanced. In the neighborhood search, the selectivity factor is used to guide non-inferior solutions to adaptively execute neighborhood search of Levy flight or Gaussian flight, to enhance the neighborhood searchability of the algorithm and improve the optimization precision. The result of simulation experiments with 23 benchmark test functions verifies that the proposed algorithm has good optimization effect in the aspects of search veracity, convergence rate, and robustness.

关键词： crow search algorithm determination factor of non-inferior solution neighborhood search selectivity factor

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crow search algorithm: Theory, Recent Advances, and Applications

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IEEE ACCESS 2020年 8卷 173548-173565页

作者： Hussien, Abdelazim G. Amin, Mohamed Wang, Mingjing Liang, Guoxi Alsanad, Ahmed Gumaei, Abdu Chen, Huiling Fayoum Univ Fac Sci Faiyum 63514 Egypt Menou Univ Fac Sci Shibin Al Kawm 32511 Egypt Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Wenzhou Polytech Dept Informat Technol Wenzhou 325035 Peoples R China King Saud Univ Coll Comp & Informat Sci Dept Informat Syst STCs Artificial Intelligence Chair Riyadh 11543 Saudi Arabia Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China

In this article, a comprehensive overview of the crow search algorithm (CSA) is introduced with detailed discussions, which is intended to keep researchers interested in swarm intelligence algorithms and optimization problems. CSA is a new swarm intelligence algorithm recently developed, which simulates crow behavior in storing excess food and retrieving it when needed. In the optimization theory, the crow is the searcher, the surrounding environment is the search space, and randomly storing the location of food is a feasible solution. Among all food locations, the location where the most food is stored is considered to be the global optimal solution, and the objective function is the amount of food. By simulating the intelligent behavior of crows, CSA tries to find optimal solutions to various optimization problems. It has gained a considerable interest worldwide since its advantages like simple implementation, a few numbers of parameters, flexibility, etc. This survey introduces a comprehensive variant of CSA, including hybrid, modified, and multi-objective versions. Furthermore, based on the analyzed papers published in the literature by some publishers such as IEEE, Elsevier, and Springer, the comprehensive application scenarios of CSA such as power, computer science, machine learning, civil engineering have also been reviewed. Finally, the advantages and disadvantages of CSA have been discussed by conducting some comparative experiments with other similar published peers.

关键词： Optimization Particle swarm optimization Heuristic algorithms Classification algorithms Genetic algorithms Licenses Mathematical model crow search algorithm CSA swarm intelligence meta-heuristics optimization nature-inspired algorithms

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crow search algorithm based optimal dynamic performance control of SVC assisted SMIB system 20

Crow search algorithm based optimal dynamic performance cont...

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20th National Power Systems Conference (NPSC)

作者： Kumar, Sudeep Kumar, Akshay Shankar, Gauri Indian Inst Technol ISM Elect Engn Dept Dhanbad Jharkhand India

ISBN: (纸本)9781538661598

A study on improvement of dynamic as well as steady state performance of power system model using static VAR compensator (SVC) is carried out in this paper. SVC refers to a shunt device within the family of flexible alternating current transmission system and is widely used for enhancing voltage profile and power transmission capability. SVC along with power system stabilizer (PSS) is employed to damp out the oscillation and take back the system to a stable state following disturbance. crow search algorithm (CSA) is implemented to optimize conventional parameters of PSS and SVC. Simulation results found by it are compared with others optimization algorithms such as particle swarm optimization (PSO) and teaching-learning-based optimization (TLBO). Also, power system model under the effect of CSA based tuned PSS and SVC is verified using eigenvalue analysis under steady state condition for investigating steady state stability. A comparative time domain simulation under MATLAB/SIMULINK platform employing CSA, PSO and TLBO tuned PSS and SVC with studied single machine infinite bus system is carried for dynamic stability assessment. Results obtained confirm the efficacy of the CSA in better tuning of PSS and SVC than other counterparts.

关键词： crow search algorithm particle swarm optimization algorithm power system stabilizer static VAR compensator teaching-learning-based optimization algorithm

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crow search algorithm for Continuous Optimization Tasks 6

Crow Search Algorithm for Continuous Optimization Tasks

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6th International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Kowalski, Piotr A. Franus, Krystian Lukasik, Szymon AGH Univ Sci & Technol Div Informat Technol & Syst Res Fac Phys & Appl Comp Sci Al Mickiewicza 30 PL-30059 Krakow Poland Polish Acad Sci Syst Res Inst Ul Newelska 6 PL-01447 Warsaw Poland

ISBN: (纸本)9781728105215

The paper provides an insight into the novel metaheuristic of the crow search algorithm (CSA) as used for continuous optimization tasks. The presented procedure is inspired by the social behaviour of crows. Based on established CEC 2017 benchmark tasks instances, the paper concentrates on performed experimental parameter studies and on a comparison with the existing Particle Swarm Optimization strategy. Building on the test experiences, sets of internal parameters have been formulated which constitute recommendations for other numerical calculations. Finally, some concluding remarks on possible algorithm extensions are given.

关键词： Artificial Intelligence Computational Intelligence Optimization Metaheuristic Swarm Intelligence crow search algorithm

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crow search algorithm Boosted by Reinforcement Learning for Feature Selection 13th

Crow Search Algorithm Boosted by Reinforcement Learning for ...

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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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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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