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

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

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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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Novel optimized crow search algorithm for feature selection

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 204卷

作者： Samieiyan, Behrouz MohammadiNasab, Poorya Mollaei, Mostafa Abbas Hajizadeh, Fahimeh Kangavari, Mohammadreza Iran Univ Sci & Technol Dept Comp Engn Tehran Iran Univ Kashan Dept Elect & Comp Engn Kashan Iran Univ Tehran Dept Elect & Comp Engn Tehran Iran Kharazmi Univ Dept Elect & Comp Engn Tehran Iran

Feature selection techniques have been presented to allow us to choose a small subset of the original components' relevant features by removing irrelevant or redundant features. Feature selection is essential for many reasons such as simplification, performance, computational efficiency, and quality interpretability. Owing to the importance mentioned above, many researchers have proposed and developed many algorithms to solve the feature selection problem. Although these approaches produce useful results, they possess some shortcomings like inadequate feature reduction. In this paper, a novel feature selection algorithm based on the crow search algorithm is presented. The algorithm uses dynamic awareness probability to keep the balance between the local and global search processes. Moreover, a novel neighborhood assigning strategy has been introduced to optimize the local search. Considering the best-selected features in each iteration helps attain more benefits in global search. The main superiority of the proposed algorithm is the significant feature reduction along with retaining the accuracy. Compared to enhanced crow search algorithm, the proposed algorithm has improved the feature reduction metric and fitness metric by 27.12% and 5.16%, respectively, while losing the accuracy metric by only 0.53%. Several popular UCI datasets have been employed to evaluate the proposed feature selection algorithm. The experimental results show that the proposed algorithm outperformed other feature selection algorithms in state-of-the-art related works regarding feature reduction and accuracy.

关键词： Feature selection crow search algorithm Metaheuristic method Feature reduction Classification

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RETRACTED: Evaluation of oral English teaching quality based on BP neural network optimized by improved crow search algorithm (Retracted Article)

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第6期45卷 11909-11924页

作者： Tan, Mindong Qu, Liangdong Guangxi Minzu Univ Sch Foreign Studies Nanning Guangxi Peoples R China Guangxi Minzu Univ Sch Artificial Intelligence Nanning 530006 Guangxi Peoples R China

Oral English teaching quality evaluation is a complex nonlinear relationship, which is affected by many factors and has low accuracy. Aiming at the problem, a teaching quality evaluation method based on a BP neural network optimized by the improved crow search algorithm (ICSA) is proposed. First, ICSA is put forward and five algorithms are used to compare with the proposed algorithm on 10 benchmarks functions. The results show that ICSA outperforms the other five algorithms on 10 functions. Second, a feature selection method based on the improved binary crow search algorithm (BICSA) is used to select teaching quality evaluation indexes, and 10 standard datasets from the UCI repository are used for testing experiments. Finally, an oral English teaching evaluation model based on BP neural network is designed, in which BICSA is used for feature selection and ICSA is used to optimize the initial weights of the BP neural network. In the experiment, we designed 5 first-grade indexes and 15 second-grade indexes, and then we collects 23 groups of oral English teaching quality data. BICSA selected 10 features from a set of 15 features. Experimental results show that this method can effectively evaluate the quality of oral English teaching with high accuracy and real-time performance.

关键词： BP neural network crow search algorithm feature selection oral English teaching quality evaluation

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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

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

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

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 194卷

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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A modified crow search algorithm based on group strategy and adaptive mechanism

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ENGINEERING OPTIMIZATION 2024年第4期56卷 625-643页

作者： Liu, Zhao Wang, Wenjie Shi, Guohong Zhu, Ping Shanghai Jiao Tong Univ Sch Design Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai Peoples R China Shanghai Jiao Tong Univ Natl Engn Res Ctr Automot Power & Intelligent Cont Sch Mech Engn Shanghai Peoples R China Pan AsiaTechn Automot Ctr Co Ltd Shanghai Peoples R China

As a swarm-based metaheuristic algorithm, the crow search algorithm (CSA) has attracted a lot of attention owing to its simplicity and flexibility. However, CSA tends to have low efficiency. To improve the optimization efficiency, this article proposes a modified version of CSA based on group strategy with an adaptive mechanism (GCSA). On this basis, crows are divided into multiple competing groups, and are assigned different roles and statuses. Then, the group strategy including different search modes is implemented to increase the solution diversity and search efficiency. Moreover, benefiting from the adaptive mechanism, the search range of crows changes in different stages to balance exploration and exploitation capabilities. To evaluate the performance of the proposed algorithm, 35 benchmark test functions (including 10 CEC2020 functions) and three engineering design problems are solved by GCSA and 11 other algorithms. The results prove that GCSA generally provides more competitive results than other metaheuristic algorithms.

关键词： Metaheuristic algorithm crow search algorithm group strategy adaptive mechanism engineering design problems

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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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