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

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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

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Finite element model updating using crow search algorithm with Levy flight

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING 2020年第13期121卷 2916-2928页

作者： Wu, Hao Wu, Pingbo Xu, Kai Li, Fansong Southwest Jiaotong Univ State Key Lab Tract Power Chengdu Sichuan Peoples R China Univ Illinois Mech Sci & Engn Urbana IL USA Southwest Jiaotong Univ Sch Mech Engn Chengdu Sichuan Peoples R China

The accuracy of the finite element model is essential for fatigue analysis and vibration of a flexible body. This paper presents a finite element model updating (FEMU) algorithm based on crows search algorithm incorporated with Levy flight (LFCSA). The proposed algorithm is tested on the updating of two different cases: a simple structure (beam) and a complex structure (gearbox housing). To verify the performance of LFCSA, two optimization algorithm, which is the standard CSA and the particle swarm optimization algorithm with Levy flight (LFPSO), are applied to FEMU of the simple beam. The result of the comparison shows that the LFCSA has sufficient convergence speed and global search ability in the implementation of FEMU. In the case of gearbox housing, the error of frequencies and mode shapes are analyzed to indicate that the LFCSA gives a satisfactory result in a complex structure.

关键词： crow search algorithm finite element model updating gearbox housing Levy flight

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Cross entropy based thresholding for magnetic resonance brain images using crow search algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 79卷 164-180页

作者： Oliva, Diego Hinojosa, Salvador Cuevas, Erik Pajares, Gonzalo Avalos, Omar Galvez, Jorge Tecnol Monterrey Dept Ciencias Comp Campus GuadalajaraAve Gral Ramon Corona 2514 Zapopan 45201 Jal Mexico Univ Complutense Madrid Fac Informat Dept Ingn Software & Inteligencia Artificial E-28040 Madrid Spain Univ Guadalajara Dept Elect CUCEI Ave Revolut 1500 Guadalajara Jalisco Mexico Univ Guadalajara Dept Ingn CUTONALA Sede Provis Casa Cultura Adm Calle Morelos 180 Tonala Jalisco Mexico

Segmentation is considered the central part of an image processing system due to its high influence on the posterior image analysis. In recent years, the segmentation of magnetic resonance (MR) images has attracted the attention of the scientific community with the objective of assisting the diagnosis in different brain diseases. From several techniques, thresholding represents one of the most popular methods for image segmentation. Currently, an extensive amount of contributions has been proposed in the literature, where thresholding values are obtained by optimizing relevant criteria such as the cross entropy. However, most of such approaches are computationally expensive, since they conduct an exhaustive search strategy for obtaining the optimal thresholding values. This paper presents a general method for image segmentation. To estimate the thresholding values, the proposed approach uses the recently published evolutionary method called the crow search algorithm (CSA) which is based on the behavior in flocks of crows. Different to other optimization techniques used for segmentation proposes, CSA presents a better performance, avoiding critical flaws such as the premature convergence to sub-optimal solutions and the limited exploration-exploitation balance in the search strategy. Although the proposed method can be used as a generic segmentation algorithm, its characteristics allow obtaining excellent results in the automatic segmentation of complex MR images. Under such circumstances, our approach has been evaluated using two sets of benchmark images;the first set is composed of general images commonly used in the image processing literature, while the second set corresponds to MR brain images. Experimental results, statistically validated, demonstrate that the proposed technique obtains better results in terms of quality and consistency. (c) 2017 Elsevier Ltd. All rights reserved.

关键词： Magnetic resonance images Evolutionary algorithms Minimum cross entropy crow search algorithm

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Enhanced crow search algorithm with multi-stage search integration for global optimization problems

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SOFT COMPUTING 2023年第20期27卷 14877-14907页

作者： He, Jieguang Peng, Zhiping Zhang, Lei Zuo, Liyun Cui, Delong Li, Qirui Guangdong Univ Petrochem Technol Coll Comp Sci Maoming Peoples R China Jiangmen Polytech Sch Informat Engn Jiangmen Peoples R China Guangdong Univ Petrochem Technol Guangdong Prov Key Lab Petrochem Equipment Fault D Maoming Peoples R China Guangdong Univ Petrochem Technol Coll Elect Informat Engn Maoming Peoples R China

crow search algorithm (CSA), as a new swarm intelligence algorithm that simulates the crows' behaviors of hiding and tracking food in nature, performs well in solving many optimization problems. However, while handling complex and high-dimensional global optimization problems, CSA is apt to fall into evolutionary stagnation and has slow convergence speed, low accuracy, and weak robustness. This is mainly because it only utilizes a single search stage, where position updating relies on random following among individuals or arbitrary flight of individuals. To address these deficiencies, a CSA with multi-stage search integration (MSCSA) is presented. Chaos and multiple opposition-based learning techniques are first introduced to improve original population quality and ergodicity. The free foraging stage based on normal random distribution and Levy flight is designed to conduct local search for enhancing the solution accuracy. And the following stage using mixed guiding individuals is presented to perform global search for expanding the search space through tracing each other among individuals. Finally, the large-scale migration stage based on the best individual and mixed guiding individuals concentrates on increasing the population diversity and helping the population jump out of local optima by moving the population to a promising area. All of these strategies form multi-level and multi-granularity balances between global exploration and local exploitation throughout the evolution. The proposed MSCSA is compared with a range of other algorithms, including original CSA, three outstanding variants of CSA, two classical meta-heuristics, and six state-of-the-art meta-heuristics covering different categories. The experiments are conducted based on the complex and high-dimensional benchmark functions CEC 2017 and CEC 2010, respectively. The experimental and statistical results demonstrate that MSCSA is competitive for tackling large-scale complicated problems, and is signi

关键词： crow search algorithm Swarm intelligence Global optimization Multi-stage search search guidance

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A recommender system based on a new similarity metric and upgraded crow search algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第3期39卷 3167-3182页

作者： Sinha, Bam Bahadur Dhanalakshmi, R. Natl Inst Technol Nagaland Comp Sci & Engn Dimapur India Indian Inst Informat Technol Tiruchirappalli Comp Sci & Engn Tiruchirappalli India

In the current era of big data, the recommender system aspires to provide users with a tailored set of personalized items from a pool of a large population. The most popular collaborative filtering system performs this information filtering process by computing similarity among users or items. This paper proposes a similarity metric that comprises of weights and values. Values are calculated by considering the matching set of users for which similarity is to be computed. The optimal values of weights are decided using an upgraded form of the crow search algorithm (CSA). The exploration and exploitation stability of CSA is improvised by making use of Levy flight diffusion, adaptive operator adjustment, and event factor. The performance of the implemented metaheuristic approach is validated on Jester, MovieLens 100K, and MovieLens 1M dataset. Comparative analysis of proposed model against several other traditional metaheuristic based personalization systems reveal that our model is less delicate to the dimension of datasets and it also presents exceptional refinement in terms of prediction complexity and accuracy.

关键词： Collaborative filtering similarity crow search algorithm optimization movielens jester

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An improved crow search algorithm for solving numerical optimization functions

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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

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

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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

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Memory based hybrid crow search algorithm for solving numerical and constrained global optimization problems

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第1期56卷 27-99页

作者： Braik, Malik Al-Zoubi, Hussein Ryalat, Mohammad Sheta, Alaa Alzubi, Omar Al Balqa Appl Univ Salt Jordan Yarmouk Univ Irbid Jordan Southern Connecticut State Univ New Haven CT USA

crow search algorithm (CSA) is a promising meta-heuristic method developed based on the intelligent conduct of crows in nature. This algorithm lacks a good representation of its individuals' memory, and as with many other meta-heuristics it faces a problem in efficiently balancing exploration and exploitation. These defects may lead to early convergence to local optima. To cope with such issues, we proposed a Memory based Hybrid CSA (MHCSA) with the use of Particle Swarm Optimization (PSO) algorithm. This hybridization approach was proposed to reinforce the diversity ability of CSA and balance its search abilities for promising solutions to achieve robust search performance. The memory element of MHCSA was initialized with the best solution (pbest) of PSO to exploit the most promising search areas. The best positions of the CSA's individuals are improved using the best solution found so far (gbest) and (pbest) of PSO. Another flaw of CSA is the use of fixed flight length and awareness probability for crows to control exploration and exploitation features, respectively. This issue was circumvented here by replacing these constants with adaptive functions in order to provide a better balance between exploration and exploitation over the course of iterations. The competence of MHCSA was revealed by testing it on seventy-three standard and computationally complex benchmark functions. Its applicability was substantiated by solving seven engineering design problems. The results showed that the problem of early convergence was eliminated by MHCSA and that the balance of exploration and exploitation was further improved. Further, MHCSA ranked first among CSA, PSO, robust variants of CSA and other strong competing methods in terms of accuracy and stability.

关键词： crow search algorithm Particle swarm optimization Optimization techniques Meta-heuristics

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Multi-objective orthogonal opposition-based crow search algorithm for large-scale multi-objective optimization

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NEURAL COMPUTING & APPLICATIONS 2020年第17期32卷 13715-13746页

作者： Rizk-Allah, Rizk M. Hassanien, Aboul Ella Slowik, Adam Menoufia Univ Fac Engn Shibin Al Kawm Egypt Cairo Univ Fac Comp & Artificial Intelligence Cairo Egypt Koszalin Univ Technol Dept Elect & Comp Sci Koszalin Poland

Many engineering optimization problems are typically multi-objective in their natures and multidisciplinary with a large number of decision variables. Furthermore, Pareto dominance loses its effectiveness in such situations. Thus, developing a robust optimization algorithm undoubtedly becomes a true challenge. This paper proposes a multi-objective orthogonal opposition-based crow search algorithm (M2O-CSA) for solving large-scale multi-objective optimization problems (LSMOPs). In the M2O-CSA, a multi-orthogonal opposition strategy is employed to mitigate the conflicts among the convergence and distribution of solutions. First, two individuals are randomly chosen to undergo the crossover stage and then orthogonal array is presented to obtain nine individuals. Then individuals are used in the opposition stage to improve the diversity of solutions. The effectiveness of the proposed M2O-CSA is investigated by implementing it on different dimensions of multi-objective optimization problems (MOPs). The Pareto front solutions of these MOPs have various characteristics such as convex, non-convex and discrete. It is also applied to solve multi-objective design applications with distinctive features such as four bar truss (FBT) design, welded beam (WB) deign, disk brake (DB) design, and speed reduced (SR) design, where they involve different characteristics. In this context, a new decision making tool based on multi-objective optimization on the basis of ratio analysis (MOORA) technique is employed to help the designer for extracting the operating point as the best compromise or satisfactory solution to execute the candidate engineering design. Simulation results affirm that the proposed M2O-CSA works efficiently and effectively.

关键词： crow search algorithm Orthogonal Opposition Multi-objective optimization Metaheuristic Engineering designs MOORA

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