检索结果-内蒙古大学图书馆

Improved convergence performance of advanced crow search algorithm and application to optimal design of large-scale truss dome structures

引用

JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 2024年

作者： Lee, Donwoo Kim, Hyukjin Kim, Jeonghyun Lee, Seungjae Korea Univ Technol & Educ Sch Ind Design & Architectural Engn 1600 Chungjeol Ro Cheonan 31253 Chungcheongnam South Korea Wroclaw Univ Sci & Technol Fac Civil Engn Wroclaw Poland

Due to its small number of parameters, the crow search (CS) algorithm is easy to apply to engineering problems and solves optimization problems in various fields. The CS algorithm is not harmoniously used with the exploitation and exploration performance, and the convergence performance varies greatly depending on the initial population. An advanced CS (ACS) algorithm with a new method was proposed to improve the convergence performance of the CS algorithm, and it was proved that the convergence performance was improved by using benchmark functions and engineering example problems. However, the convergence performance according to the probability used in the proposed method for harmonizing the global search and the local search among ACS algorithms was not accurately compared and mentioned, and it was proposed to use a value of 0.5. Therefore, this paper examines the probability that the best convergence performance can be derived to harmonize global and local searches. In addition, it solves the engineering optimization problem of building structures by applying it to the optimal design of large-scale truss dome structures with natural frequencies as constraints. As a result, the minimum weight was derived from all truss dome structures when the probability proposed in this paper was used.

关键词： Meta-heuristics crow search algorithm large-scale structure, truss dome, optimal design

来源：评论

学校读者我要写书评

暂无评论

Binary crow search algorithm for the uncapacitated facility location problem

引用

NEURAL COMPUTING & APPLICATIONS 2021年第21期33卷 14669-14685页

作者： Sonuc, Emrullah Karabuk Univ Dept Comp Engn TR-78050 Karabuk Turkey

The crow search algorithm (CSA) is a recently proposed population-based optimization algorithm for continuous optimization. Since the original CSA searches for a feasible solution in a continuous search space, it cannot handle binary optimization problems directly. A few binary variants of CSA are presented in the literature. However, these variants search for a new solution in the continuous domain and need transfer functions to adapt the solution to the binary domain. This may cause poor exploration, making some regions in the search space impossible to discover. This paper proposes an effective binary CSA (BinCSA) using bitwise operations that directly searches for a feasible solution in the binary search space. For this purpose, the original update mechanism of the CSA is improved using exclusive-OR and AND logical operators in order to provide a good balance between exploration and exploitation in the binary search space. The effectiveness of the proposed BinCSA is evaluated on the uncapacitated facility location problem (UFLP), one of the most widely investigated pure binary optimization problems. The performance of BinCSA is examined using two different UFLP datasets, ORLIB and M*. The experimental results show that BinCSA obtained the optimal solution for 13 out of 15 instances of ORLIB and 12 out of 20 instances of M*. Moreover, BinCSA exhibits superior performance on ORLIB instances when compared to other methods and is very competitive on M* instances in terms of solution quality and robustness. The source code for BinCSA, as used for the UFLP, is available at https://***/3mrullah/BinCSA.

关键词： Binary encoded problems crow search algorithm Uncapacitated facility layout problem

来源：评论

学校读者我要写书评

暂无评论

A comprehensive survey of crow search algorithm and its applications

引用

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

来源：评论

学校读者我要写书评

暂无评论

Distributed Energy Optimization Protocol using crow search algorithm in Underwater Acoustic Sensor Network for Energy Enhancement Comparing with Depth Based Routing algorithm 14

Distributed Energy Optimization Protocol using Crow Search A...

引用

14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)

作者： Reddy, K. Narasimha Ayyadurai, M. Saveetha Sch Engn Dept Elect & Commun Engn Chennai Tamil Nadu India

ISBN: (纸本)9781665460712

By combining a distributed energy optimization protocol and the crow search algorithm,An underwater acoustic sensor network's sensor nodes can be made to use less energy. ( DEOC SA) in contrast to the DBR protocol for depth-based routing. The Underwater Acoustic Sensor Network (UWASN) uses a 3D geographic zone for cooperative sampling to gather data and uses the crow search algorithm to distribute the data among the nodes.20 samples from each group were collected with a pre-test power of 80%, an error of 0.05, a confidence level of 95%, and 0.05 was chosen as the cutoff point for training the data sets. By changing the node distance, the proposed algorithm routing metrics are examined in a virtual underwater environment using the Aquasim patch and NS2 *** compared to DBR's energy (1mJ) with delay, the proposed DEOCSA performs best for dynamically changing environmental and geographical topological conditions (850ms) The statistical research demonstrates that the least significant value (P0.05) for energy optimization is energy (P=0.05).The simulation results show that by using the recommended crow search algorithm rather than Depth Based Routing algorithm, the sensor network's energy efficiency is increased by shortening the time spent choosing the best nodes.

关键词： UWASN crow search algorithm Depth-based Routing Energy Optimization Green Energy Artificial Intelligence Innovation

来源：评论

学校读者我要写书评

暂无评论

Optimal power scheduling in real-time distribution systems using crow search algorithm for enhanced microgrid performance

引用

SCIENTIFIC REPORTS 2024年第1期14卷 1-20页

作者： Selvaraj, Ganesh Rajangam, Kanimozhi Vishnuram, Pradeep Bajaj, Mohit Zaitsev, Ievgen Grace Coll Engn Dept Elect & Elect Engn Thoothukudi 628005 Tamil Nadu India Anna Univ Univ Coll Engn Dept Elect & Elect Engn BIT Campus Tiruchirappalli 620024 Tamilnadu India SRM Inst Sci & Technol Dept Elect & Elect Engn Chennai 603203 India Graph Era Deemed Univ Dept Elect Engn Dehra Dun 248002 India AL Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman Jordan Univ Business & Technol Coll Engn Jeddah 21448 Saudi Arabia Natl Acad Sci Ukraine Inst Electrodynam Dept Theoret Elect Engn & Diagnost Elect Equipment Beresteyskiy 56 UA-03680 Kyiv 57 Ukraine Natl Acad Sci Ukraine Ctr Informat Analyt & Tech Support Nucl Power Faci Akad Palladina Ave 34-A Kyiv Ukraine

Microgrids (MGs) have gained significant attention over the past two decades due to their advantages in service reliability, easy integration of renewable energy sources, high efficiency, and enhanced power quality. In India, low-voltage side customers face significant challenges in terms of power supply continuity and voltage regulation. This paper presents a novel approach for optimal power scheduling in a microgrid, aiming to provide uninterrupted power supply with improved voltage regulation (VR). To address these challenges, a crow search algorithm is developed for effective load scheduling within the distribution system. The proposed method minimizes the total operating cost (TOC) and maximizes VR under varying loading conditions and distributed generation (DG) configurations. A case study in Tamil Nadu, India, is conducted using a microgrid composed of three distributed generation sources (DGs), modeled and simulated using the Electrical Transient Analyzer Program (ETAP) environment. The proposed approach is tested under three operational scenarios: grid-connected mode, islanded mode, and grid-connected mode with one DG outage. Results indicate that the crow search algorithm significantly optimizes load scheduling, leading to a substantial reduction in power loss and enhancement in voltage profiles across all scenarios. The islanded mode operation using the crow search algorithm demonstrates a remarkable reduction in TOC and maximizes voltage regulation compared to other modes. The main contributions of this work include: (1) developing a new meta-heuristic approach for power scheduling in microgrids using the crow search algorithm, (2) achieving optimal power flow and load scheduling to minimize TOC and improve VR, and (3) successfully implementing the proposed methodology in a real-time distribution system using ETAP. The findings showcase the effectiveness of the crow search algorithm in microgrid power management and its potential for application in other

关键词： Microgrid Load scheduling crow search algorithm Voltage regulation Distributed generation Total operating cost Power management ETAP simulation

来源：评论

学校读者我要写书评

暂无评论

Identification of top-k influential nodes based on discrete crow search algorithm optimization for influence maximization

引用

APPLIED INTELLIGENCE 2021年第11期51卷 7749-7765页

作者： Li, Huan Zhang, Ruisheng Zhao, Zhili Liu, Xin Yuan, Yongna Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Gansu Peoples R China

Influence maximization refers to selecting a small number of influential nodes in a given network to maximize the influence affected by the subset. In social network analysis and viral marketing, influence maximization is greatly significant. The greedy-based algorithm is time-consuming in estimating the expected influence diffusion of a given node set, which is unsuitable for large-scale network. The traditional heuristics often have the problem of low accuracy. In this study, in order to solve the influence maximization problem more effectively, a meta-heuristic discrete crow search algorithm (DCSA) using the intelligence of crow population is proposed. In DCSA, a new coding mechanism and discrete evolution rules are constructed. The degree-based initialization method and the random walk strategy are adopted to enhance the search ability. Moreover, according to the network topology, influential nodes Candidates are generated to avoid blindness in the process of crow search. Extensive experiments are conducted on six real-world social networks under independent cascade (IC) model, the results show that DCSA outperforms other state-of-the-art algorithms and obtains comparable influence diffusion results to CELF but with lower time complexity.

关键词： Social networks Influence maximization crow search algorithm Swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

Application of an improved discrete crow search algorithm with local search and elitism on a humanitarian relief case

引用

ARTIFICIAL INTELLIGENCE REVIEW 2021年第6期54卷 4591-4617页

作者： Eliguzel, Ibrahim Mirac Ozceylan, Eren Gaziantep Univ Ind Engn Dept TR-27100 Gaziantep Turkey

This paper demonstrates an application of the improved crow search algorithm (I-CSA), which is a modified version of the crow search algorithm (CSA). CSA is a recent, nature-inspired meta-heuristic optimization algorithm. I-CSA differs from CSA by allowing application on a discrete problem, which is P-median and fortifying faster convergence to an optimal or near-optimal solution. Improvements are provided by local searches which support escaping from local optima or convergence to the optimal solution, elitism enhances the intensification through the utilization of nodes by selecting the most frequent centers that appeared in hiding better locations for local search, on the P-median problem. The application of the I-CSA is structured in three phases. In the first phase, parameters of I-CSA are analyzed and optimized using well-known data tests. The test datasets for the application of I-CSA on the P-median problem were retrieved from the OR-library to present the effectiveness and applicability of I-CSA. In the second phase, 40-pmed test problems from the library are solved using I-CSA and the results are compared with known optimal results and recorded results of other meta-heuristic approaches. In addition, Wilcoxon signed-rank test is applied in order to demonstrate the performance of I-CSA compared to well-known algorithms. The results of the proposed method demonstrated a faster convergence rate and better solution in most cases when compared with the standard CSA and other well-known meta-heuristic approaches. Finally, the proposed I-CSA approach is tested on a real-life case problem including 2121 nodes in Tunceli, Turkey. Obtaining the optimal results in a reasonable time indicates that the potential of the I-CSA is high and promising. In nutshell, this paper presents an improvement to CSA and evaluates its performance in a three-phase test procedure.

关键词： crow search algorithm Improved CSA Meta‐ heuristic Optimization P-median problem

来源：评论

学校读者我要写书评

暂无评论

An improved crow search algorithm for solving numerical optimization functions

引用

SOFT COMPUTING 2021年第14期25卷 9441-9454页

作者： Gholami, Jafar Mardukhi, Farhad Zawbaa, Hossam M. Islamic Azad Univ Kermanshah Sci & Res Branch Dept Comp Engn Kermanshah Iran Razi Univ Dept Comp Engn & Informat Technol Kermanshah Iran Beni Suef Univ Fac Comp & Artificial Intelligence Bani Suwayf Egypt Technol Univ Dublin Dublin Ireland

Meta-heuristic algorithms have shown promising results in solving various optimization problems. The crow search algorithm (CSA) is a new and effective meta-heuristic algorithm that emulates crows' intelligent group behavior in nature. However, it suffers from several problems, such as trapping into local optimum and premature convergence. This paper proposes an improved crow search algorithm (ICSA), which has been tested and evaluated by a set of well-known benchmark functions. A new update mechanism that uses the merits of the global best position to move toward the best position is proposed. This mechanism increases the convergence of the algorithm and improves its local search-ability. Twenty benchmark functions are used to evaluate the performance of the proposed ICSA. Moreover, the ICSA algorithm is compared with the conventional CSA and other meta-heuristic algorithms such as particle swarm optimization (PSO), dragonfly algorithm (DA), grasshopper optimization algorithm (GOA), gray wolf optimizer (GWO), moth-flame optimization (MFO), and sine-cosine algorithm (SCA). The experimental result shows that the proposed ICSA algorithm has produced promising results and outperformed conventional CSA and other meta-heuristic algorithms. Also, the proposed ICSA has a more robust convergence for optimizing objective functions in terms of solution accuracy and efficiency.

关键词： crow search algorithm Meta-heuristic algorithms Numerical optimization functions Swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Value aided optimal load shedding accounting voltage stability consideration employing crow search algorithm with modification based on Lampinen's criterion

引用

APPLIED SOFT COMPUTING 2023年 143卷

作者： Singh, Pushpendra Arya, Rajesh Titare, L. S. Purey, Pradeep Arya, L. D. Rajkiya Engn Coll Banda Dept Elect Engn Banda 210201 UP India Medi Caps Univ Dept Elect Engn Indore 453331 MP India Govt Engn Coll Dept Elect Engn Ujjain 456010 MP India Maharaja Ranjit Singh Coll Profess Sci Dept Elect Indore 452001 MP India

This paper describes a novel load-shedding strategy that assures adequate voltage stability margin in post-load-shedding conditions. Minimum numbers of buses are selected for load shedding based on the incremental voltage of the load buses. An objective function has been formed that is to be minimized based on inequality constraints on load bus voltage and line flow. This objective function is the weighted sum of the slope of the PV-curve of the weakest bus, transmission losses, and the total load shed amount, which has been normalized with respect to the pre-load shed condition. A modified crow search algorithm (CSA) has been used to obtain optimal load shed. The developed methodology has been implemented on standard IEEE 14 and 25-bus test systems, and comparisons based on statistical inferences have been carried out with the Sine-Cosine, Jaya, and Self-adaptive differential evolution (SaDE) algorithms. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： Load shedding Voltage stability Slope of PV-curve Optimization crow search algorithm Load flow

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：