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Optimization of Running Blade Prosthetics Utilizing crow search algorithm Assisted by Artificial Neural Networks

STROJNISKI VESTNIK-JOURNAL OF MECHANICAL ENGINEERING 2021年第3期67卷 88-100页

作者： Rosel Solis, Manuel Javier Davalos Ramirez, Jose Omar Molina Salazar, Javier Ruiz Ochoa, Juan Antonio Gomez Roa, Antonio Autonomous Univ Baja California Mexicali Baja California Mexico Autonomous Univ Ciudad Juarez Av Charro 450 Ciudad Juarez Chihuahua Mexico

A crow search algorithm (CSA) was applied to perform the optimization of a running blade prosthetics (RBP) made of composite materials like carbon fibre layers and cores of acrylonitrile butadiene styrene (ABS). Optimization aims to increase the RBP displacement limited by the Tsai-Wu failure criterion. Both displacement and the Tsai-Wu criterion are predicted using artificial neural networks (ANN) trained with a database constructed from finite element method (FEM) simulations. Three different cases are optimized varying the carbon fibre layers orientations: -45 degrees/45 degrees, 0 degrees/90 degrees, and a case with the two-fibre layer orientations intercalated. Five geometric parameters and a number of carbon fibre layers are selected as design parameters. A sensitivity analysis is performed using the Garzon equation. The best balance between displacement and failure criterion was found with fibre layers oriented at 0 degrees/90 degrees. The optimal candidate with -45 degrees/45 degrees orientation presents higher displacement;however, the Tsai-Wu criterion was less than 0.5 and not suitable for RBP design. The case with intercalated fibres presented a minimal displacement being the stiffer RBP design. The damage concentrates mostly in the zone that contacts the ground. The sensitivity study found that the number of layers and width were the most important design parameters.

关键词： optimization crow search algorithm artificial neural networks running blade prosthetics Tsai-Wu criterion finite element method

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An Amended crow search algorithm for Hybrid Active Power Filter Design

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PROCESSES 2023年第9期11卷 2550页

作者： Ali, Shoyab Bhargava, Annapurna Saxena, Akash Almazyad, Abdulaziz S. Sallam, Karam M. Mohamed, Ali Wagdy Rajasthan Tech Univ Dept Elect Engn Kota 324010 India Cent Univ Haryana Mahendergarh Dept Elect Engn Mahendergarh 123031 Haryana India King Saud Univ Coll Comp & Informat Sci Dept Comp Engn POB 51178 Riyadh 11543 Saudi Arabia Univ Canberra Fac Sci & Technol Sch IT & Syst Canberra ACT 2601 Australia Cairo Univ Fac Grad Studies Stat Res Operat Res Dept Giza 12613 Egypt Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan

Hybrid Active Power Filter (HAPF) imbibes the advantages of both passive and active power filters. These filters are considered one of the important technologies for mitigating harmonic pollution in electrical systems. Accurate estimation of filter parameters is a key component to reduce harmonic pollution effectively. In recent years, several optimization approaches have been reported to solve this estimation problem;still, this area is worthy of further investigation. This paper is a proposal for an estimator that can estimate the parameter of HAPF configuration accurately. For evolving this estimator, first, an objective function that mathematically embeds filter parameters and harmonic pollution is presented. For handling the optimization process, an Amended crow search algorithm (ACSA) is proposed. ACSA employs a local search algorithm (in the form of a pattern search) for obtaining optimal results. The analysis of the estimation process is carried out on two HAPF configurations. Various analyses that include harmonic pollution statistical analysis along with fitness function value analysis reveal that the proposed algorithm acquires optimal results as compared with other recently published and reported algorithms. Further, the proposed filter configurations are tested with the existing filter. The results prove that the proposed filter shows promising results.

关键词： power quality filter swarm intelligence crow search algorithm design numerical optimization

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Boosted crow search algorithm for handling multi-threshold image problems with application to X-ray images of COVID-19

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第PartB期213卷 119095页

作者： Zhao, Songwei Wang, Pengjun Heidari, Ali Asghar Zhao, Xuehua Chen, Huiling Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China Wenzhou Univ Coll Elect & Elect Engn Wenzhou 325035 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Shenzhen Inst Informat Technol Sch Digital Media Shenzhen 518172 Peoples R China

COVID-19 is pervasive and threatens the safety of people around the world. Therefore, now, a method is needed to diagnose COVID-19 accurately. The identification of COVID-19 by X-ray images is a common method. The target area is extracted from the X-ray images by image segmentation to improve classification efficiency and help doctors make a diagnosis. In this paper, we propose an improved crow search algorithm (CSA) based on variable neighborhood descent (VND) and information exchange mutation (IEM) strategies, called VMCSA. The original CSA quickly falls into the local optimum, and the possibility of finding the best solution is significantly reduced. Therefore, to help the algorithm avoid falling into local optimality and improve the global search capability of the algorithm, we introduce VND and IEM into CSA. Comparative experiments are conducted at CEC2014 and CEC'21 to demonstrate the better performance of the proposed algorithm in optimization. We also apply the proposed algorithm to multi-level thresholding image segmentation using Renyi's entropy as the objective function to find the optimal threshold, where we construct 2-D histograms with grayscale images and non-local mean images and maximize the Renyi's entropy on top of the 2-D histogram. The proposed segmen-tation method is evaluated on X-ray images of COVID-19 and compared with some algorithms. VMCSA has a significant advantage in segmentation results and obtains better robustness than other algorithms. The available extra info can be found at https://***/1234zsw/VMCSA.

关键词： Multi -threshold image segmentation crow search algorithm Renyi ?s entropy 2D histogram COVID-19 Optimization

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Real-time validation of an automatic generation control system considering HPA-ISE with crow search algorithm optimized cascade FOPDN-FOPIDN controller

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ARCHIVES OF CONTROL SCIENCES 2023年第2期33卷 371-390页

作者： Babu, Naladi Ram Chiranjeevi, Tirumalasetty Devarapalli, Ramesh Knypinvski, Lukasz Garcia Marquez, Fausto Pedro Aditya Engn Coll Dept Elect & Elect Engn Surampalem Andhra Pradesh India Rajkiya Engn Coll Sonbhadra Dept Elect Engn Markundi Uttar Pradesh India Inst Chem Technol Dept Elect Elect & Instrumentat Engn Indianoil Odisha Campus Bhubaneswar 751013 India Poznan Univ Technol Piotrowo 3A Fac Control Robot & Elec tr Engn PL-60965 Poznan Poland Univ Castilla La Mancha Ingenium Res Grp La Mancha Spain

This article validates the application of RT-Lab for the AGC studies of three-area systems. All the areas are employed with thermal-DSTS systems. A new controller named cascade FOPDN-FOPPIDN is employed. Its parameters are optimized using a CSA, subjecting to a new PI named HPA-ISE. The responses of the FOPDN-FOPIDN controller are related and are superior over PIDN and TIDN controllers. Moreover, the dominance of HPA-ISE is verified with ISE, and it performs better in terms of system dynamics. Further, the system performance reliability is analyzed with the AC-HVDC and is better than the AC system. Besides, sensitivity analysis recommends that the proposed FOPDN-FOPIDN at diverse conditions is robust and more reliability.

关键词： crow search algorithm dish-stirling solar system AGC RT Lab FOPDN-FOPIDN controller

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Microgrid Optimal Dispatching Based on Improved crow search algorithm

International Core Journal of Engineering

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International Core Journal of Engineering 2023年第6期9卷 270-277页

作者： Zhaohua Cen

In response to the difficulty of optimizing solutions under engineering constraints, dynamic perception probability and Levi's flight strategy were introduced into the traditional crow search algorithm to improve the algorithm's global search ability while increasing its diversity. Simulation in grid connected mode shows that the improved crow search algorithm (PCSA) can improve the global optimization performance of the model, optimize the scheduling system, effectively reduce load peak valley differences, and reduce economic losses.

关键词： Microgrid Optimal Scheduling crow search algorithm Levi Flight Strategy

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CCSA: Cellular crow search algorithm with topological neighborhood shapes for optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期194卷 116431-116431页

作者： Awadallah, Mohammed A. Al-Betar, Mohammed Azmi Abu Doush, Iyad Makhadmeh, Sharif Naser Alyasseri, Zaid Abdi Alkareem Abasi, Ammar Kamal Alomari, Osama Ahmad Sci Al Aqsa Univ Dept Comp Sci POB 4051 Gaza Palestine Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Al Balqa Appl Univ Dept Informat Technol Al Huson Univ Coll POB 50 Irbid Jordan Amer Univ Kuwait Coll Engn & Appl Sci Comp Dept Kuwait Kuwait Yarmouk Univ Comp Sci Dept Irbid Jordan Univ Kufa ECE Dept Fac Engn POB 21 Najaf Iraq Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Artificial Intelligence Technol Bangi 43600 Selangor Malaysia Univ Sharjah MLALP Res Grp Sharjah U Arab Emirates Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia

In evolutionary computation, systematically structuring the population is used to manage the evolution process. Thus controlling the amount of diversity during the algorithm search. Island-based, hierarchical-based, and cellular automata are the most popular structured population models utilized for evolutionary algorithms to improve their diversity and convergence. Specifically, the cellular automata model arranges the population of individuals in a 2D grid spatial structure where the concepts of cells and their neighbors are considered to drive the evolution step. A recent population-based algorithm is proposed called the crow search algorithm (CSA). It stimulates the behavior of crows in keeping their food in hidden places to be retrieved when needed. Like other population-based algorithms, CSA endures from slow convergence because of inadequate diversity. In this paper, the cellular automata model is incorporated with the optimization framework of CSA to control its diversity during the search, thus boosts its efficiency. The proposed method is abbreviated as CCSA. In CCSA, the population is structured as a 2D grid where the active population is iteratively determined. At each iteration, each individual is updated based on current neighboring individuals determined by the topological neighborhood shapes and follows the best neighboring individual in its active population. The CCSA is evaluated using 23 standard benchmark functions well-circulated in the literature. Initially, six cellular topological neighborhood shapes (i.e., L5, L9, C9, C13, C21, and C25) are studied with various problem sizes to investigate their impact on the CCSA convergence. Comparative evaluations against twelve state-of-the-art methods, including four CSA versions, are conducted. The comparative results prove the effectiveness of the proposed CCSA. The diversity of the proposed CCSA has also been stressed using convergence analysis and hamming distance. For further validations, the propo

关键词： crow search algorithm Cellular automata model Structured population Diversity control Metaheuristics Optimization

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An efficient harmonic estimator design based on Augmented crow search algorithm in noisy environment

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期194卷 116470-116470页

作者： Saxena, Akash Swami Keshvanand Inst Technol Management & Gramot Dept Elect Engn Jaipur Rajasthan India

In this paper, an intelligent harmonic estimator based on an improved version of crow search algorithm is proposed for identification of inter, sub and power harmonics. The algorithm is named as Augmented crow search algorithm (ACSA). Harmonic estimation design problem is considered as an estimation problem of phase and amplitude component of harmonic signals. It is a known fact that certain modifications are essential to make an algorithm compatible for real applications. Keeping this fact in consideration, an acceleration factor driven crow search algorithm is proposed that also incorporates opposition-based learning in initialization phase. Further, an error function is derived from the mean square values of difference of real and estimated values of the amplitude and phase components. An iterative optimization process is followed for identification of these components. A fair comparison is carried out on the set of four test benches of diverse properties and shapes. Comparison of different contemporary algorithms and a few of crow search algorithm variants is carried out to showcase efficacy of the proposed version of the crow search. It is observed that proposed version is competitive.

关键词： Harmonic estimation crow search algorithm Power quality issues

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Research on Sustainable Scheduling of Cascade Reservoirs Based on Improved crow search algorithm

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WATER 2023年第3期15卷 578页

作者： Liu, Xiaoshan Lu, Jinyou Zou, Chaowang Deng, Bo Liu, Lina Yan, Shaofeng Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China Hubei Inst Water Resources Survey & Design Wuhan 430010 Peoples R China Changjiang River Sci Res Inst Minist Water Resources Key Lab River Regulat & Control Wuhan 430010 Peoples R China

As an important tool for the development and utilization of river water conservancy and hydropower resources, cascade reservoirs will directly affect human life and ecological environment. Therefore, how to sustainably and rationally dispatch the water resources of cascade reservoirs is of great importance to human society and ecological environment. In order to solve this problem, this paper constructs the objective function by considering the three goals of reservoir power generation target, social benefit and ecological benefit. On this basis, a mathematical model of cascade reservoir scheduling is established considering multi-dimensional constraints such as water transmission and water supply capacity constraints, water level constraints and flow constraints. In addition, we consider the fact that the crow search algorithm (CSA) is easy to fall into as the local optimal solution due to the influence of its flight distance parameters on the search ability when solving large-scale optimization problems. Therefore, a crow search algorithm based on particle swarm optimization (PSO-CSA) is designed to solve the multi-objective scheduling model of cascade reservoir established in this paper. Finally, this paper compares the PSO-CSA algorithm, PSO algorithm, CSA algorithm and genetic algorithm (GA) which is widely used in reservoir water resource dispatch, through a simulation example. The simulation results show the superiority of the algorithm designed in this paper in solving the water resource control problem of cascade reservoirs.

关键词： cascade reservoirs multi-objective optimization crow search algorithm particle swarm optimization sustainable scheduling

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A Novel Approach Based on crow search algorithm for Solving Reactive Power Dispatch Problem

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ENERGIES 2018年第12期11卷 3321-3321页

作者： Meddeb, Asma Amor, Nesrine Abbes, Mohamed Chebbi, Souad Univ Tunis Natl Super Sch Engn Tunis ENSIT Lab Technol Informat Commun & Elect Engn LaTICE Tunis 1002 Tunisia

This paper presents a novel meta-heuristic approach based on the crow search algorithm (CSA) for solving the optimal reactive power dispatch (ORPD) problem. The ORPD is formulated as a nonlinear optimization problem designed to minimize power losses while satisfying the required constraints. The CSA is a recent efficient approach that depends on the intelligent behavior of crows. Nowadays, it has been used to solve many complex engineering optimization problems where it has proven its power and effectiveness. Motivated by the high ability in solving complex optimization problems and faster convergence of CSA, this paper proposes a novel approach to solve the ORPD problem. Furthermore, the settings of control variables such as generator terminal voltage, tap changer positions, and capacitor banks are determined to achieve the minimum total power loss while satisfying a set of nonlinear constraints. The accuracy and the performance of the proposed algorithm were performed and compared to other meta-heuristic algorithms reported in the literature. Several tests are applied on two standard test systems, including IEEE 14-bus and IEEE 30-bus as well as on the large-scale Tunisian 86-bus system. In addition, a sensitivity analysis has been performed to valid the performance of the CSA in solving the ORPD problem. We demonstrate that the proposed CSA provides a supremacy results and statistically significant in solving ORPD problems (for IEEE-14 bus , for IEEE-30 bus p<0.006, and for Tunisian 86-bus p<0.0000001).

关键词： optimal reactive power dispatch real power loss meta-heuristic methods crow search algorithm

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Hybridization of Grey Wolf Optimizer and crow search algorithm Based on Dynamic Fuzzy Learning Strategy for Large-Scale Optimization

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IEEE ACCESS 2020年 8卷 161593-161611页

作者： Rizk-Allah, Rizk Masoud Slowik, Adam Hassanien, Aboul Ella Menoufia Univ Fac Engn Dept Basic Engn Sci Shibin Al Kawm Egypt Koszalin Univ Technol Dept Elect & Comp Sci PL-75452 Koszalin Poland Cairo Univ Fac Comp & Artificial Intelligence Giza 12613 Egypt

A novel optimization algorithm named hybrid grey wolf optimizer with crow search algorithm (GWO-CSA) is developed in this paper for handling large-scale numerical optimization problems. The proposed GWO-CSA algorithm combines the strong points of both grey wolf optimizer (GWO) and crow search algorithm (CSA) with the aim to escape from local optima with faster convergence than the standard GWO and CSA. In this algorithm, GWO operates in enhancing the exploration ability while CSA works as a local searching scheme to emphasize the exploitation capability to achieve global optimal solutions. In this sense, the movement direction and speed of leader grey wolf (alpha) is improved by incorporating the CSA phase. Also, a dynamic fuzzy learning strategy (DFLS) is introduced to enable the occurring of tiny changes in the neighborhood of the best solution to avoid the caught in the local optima and refine the quality of the obtained solution. The robustness and efficiency of the proposed GWO-CSA algorithm are investigated on fifteen CEC 2015 benchmark problems in addition to four large-scale problems and four real applications related to engineering design optimization taken from the literature. The comprehensive comparisons with other algorithms have demonstrated the effectiveness of GWO-CSA to address optimization tasks.

关键词： Optimization Task analysis Benchmark testing search problems Heuristic algorithms Convergence Economics Grey wolf optimizer crow search algorithm numerical optimization hybridization

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