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Feature selection based on a crow search algorithm for big data classification

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2021年 212卷 104288-104288页

作者： Al-Thanoon, Niam Abdulmunim Algamal, Zakariya Yahya Qasim, Omar Saber Univ Mosul Dept Operat Res & Intelligent Tech Mosul Iraq Univ Mosul Dept Stat & Informat Mosul Iraq Univ Mosul Dept Math Mosul Iraq

The common issues of big data are that many of the features may not be relevant. Feature selection has been proven to be an effective way to improve the results of many classification algorithms. Binary crow search algorithm (BCSA) has been developed by getting inspired from natural phenomena to perform feature selection. In BCSA, the flight length parameter plays an important role in the performance of this algorithm. To improve the classification performance with reasonably selected features, an improvement of determining the flight length parameter by employing the concept of opposition-based learning strategy of BCSA is proposed. Experimental results on two datasets show the proposed algorithm, OBL-BCSA, has an advantage over the traditional BCSA in terms of selecting relevant features with a high classification performance. Further, the performance of the OBLBCSA is compared with other algorithms in term of the computational time efficiency which is revealing that OBLBCSA outperforms them.

关键词： crow search algorithm Opposition-based learning Feature selection Big data classification

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A greedy-based crow search algorithm for semiconductor final testing scheduling problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年第1期183卷

作者： Hu, Weiguo Liu, Min Dong, Mingyu Liu, Tao Zhang, Yabin Cheng, Guanyi Tsinghua Univ Beijing Peoples R China Yangtze Memory Technol Co Ltd Wuhan Peoples R China

The semiconductor final testing scheduling problem (SFTSP) is of great importance to the efficiency of integrated circuit firms and has been widely investigated in the field of intelligent optimization. In this paper, a greedy-based crow search algorithm (GCSA) is presented for solving the SFTSP. According to the characteristics of SFTSP, new encoding and decoding strategies are proposed to link the feasible solutions to the scheduling schemes. The search operations are performed only in the operation sequence space, and a corresponding ma-chine allocation vector is generated for each operation sequence vector based on the greed mechanism. Two crow position update strategies named track and hover are redesigned and the improved crow search algorithm is utilized to search the operation sequence space efficiently in order that the GCSA can adapt the SFTSP and make full use of the information obtained during the search process. Moreover, the effect of parameters is investigated based on a multi-factor analysis of variance (ANOVA) approach. Finally, extensive computations and compari-sons on ten test instances derived from the practical production demonstrate that the proposed GCSA out-performs the state-of-the-art methods in the literature to solve the SFTSP.

关键词： Semiconductor final testing scheduling problem crow search algorithm Greedy-based Makespan

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

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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

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Automatic clustering and feature selection using multi-objective crow search algorithm

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APPLIED SOFT COMPUTING 2023年 142卷

作者： Ranjan, Rajesh Chhabra, Jitender Kumar Natl Inst Technol Comp Engn Dept Kurukshetra 136119 India

Today's real-world data is frequently significant in size, with many redundant, missing, and noise -based features and data instances must be addressed before applying various data-mining-based algorithms for further knowledge discovery. Excessive dimensionality may be mitigated by carefully excluding unnecessary characteristics and selecting a reasonable subset of features. When presented as an optimization issue, choosing the best clusters using the most suitable subset of attributes is a challenge that may be handled using practical meta-heuristic approaches. Besides this, the automatic finding of the appropriate cluster number is another challenging task for the real-world dataset in the unsupervised machine-learning study. The present work proposes a multi-objective crow search algorithm for clustering and feature selection (MO-CSACFS) by modifying the crow search algorithm and introducing a levy flight-based two-point cross-over mechanism for a better exploration phase of the crow and further making it suitable for multi-objective optimization problems. MO-CSACFS addresses both issues using the three objective functions to find appropriate cluster numbers and features. MO-CSACFS is implemented over several real-life and synthetic datasets with varying instances, features, and cluster numbers to assess the algorithm's performance;apart from that, the present work is also applied over several gene-expression datasets. MO-CSACFS is compared with two similar recently proposed multi-objective optimization processes used over an automatic, unsupervised machine learning task. The results show that the MO-CSACFS has produced a compact and robust cluster comparable to other similar works from the literature. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： Multi -objective optimization Data clustering Feature selection crow search algorithm

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Power system state estimation using a robust crow search algorithm based on PMUs with limited number of channels

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ELECTRIC POWER SYSTEMS REsearch 2023年第1期217卷

作者： Andic, Cenk Ozturk, Ali Turkay, Belgin Istanbul Tech Univ Fac Elect & Elect Engn Dept Elect Engn Istanbul Turkiye Duzce Univ Fac Engn Dept Elect Elect Engn Duzce Turkiye

In modern electrical power systems, the state estimation results provide real-time data about the current operating state of the system. The state of the system can be estimated by using the system model and the available measurement data set. Measurement data are collected from measuring devices called Phasor Measurement Units (PMUs). The optimal placement of PMUs is determined by observing the entire system and taking into account the cost of placement. Moreover, the number of channels of PMUs is an important constraint that determines the number of measuring meters. This constraint can change the number of the optimal placement of the PMUs. Firstly, the PMUs are optimally placed by taking into account the limited number of channels. Secondly, the states of the system are estimated using the measurement data collected from the placed PMUs. In this study, a robust crow search algorithm (CSA), which is one of the meta-heuristic methods, is proposed for the first time to solve both the optimal placement of PMUs and the optimal static estimate for the entire system. The proposed CSA has been tested on IEEE 14, 30, 57, and 118 bus test systems. The effectiveness of the CSA-based state estimator over other well-known algorithms, which are Genetic algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Swarm Optimization (ABSO), is shown by comparing the obtained results. The wellknown Newton-Raphson power flow solutions are accepted as benchmarks, and the proposed CSA-based state estimator has given reliable results with better accuracy in all test systems compared to other methods. Thus, it has been proved that the proposed CSA is an alternative solution method for the state estimation problem. State estimation results are used in the analysis, operation, and planning of the power system. This study contributes to the more accurate and reliable operation of the system by the operators.

关键词： crow search algorithm Channel limits Phasor measurement units State estimation Power systems

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An experimental investigation and modeling approach of response surface methodology coupled with crow search algorithm for optimizing the properties of jute fiber reinforced concrete

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CONSTRUCTION AND BUILDING MATERIALS 2020年 243卷 118216-000页

作者： Sultana, N. Hossain, S. M. Zakir Alam, Md Shah Hashish, M. M. A. Islam, M. S. Imam Abdulrahman Bin Faisal Univ Coll Comp Sci & Informat Technol Dept Comp Sci Dammam Saudi Arabia Univ Bahrain Coll Engn Dept Chem Engn Zallaq Bahrain Univ Bahrain Coll Engn Dept Civil Engn Zallaq Bahrain Univ Tabuk Coll Engn Dept Civil Engn Tabuk Saudi Arabia

The natural jute fiber can be the effective material to reinforce concrete strength. This study investigates the effect of jute fiber on the compressive and tensile strengths of Jute Fiber Reinforced Concrete Composites (JFRCC). A Response Surface Methodology (RSM) articulated with crow search algorithm (CSA)-based platform was developed for predicting and optimizing the variables that affect JFRCC properties. For this, cylinders and cubes of standard dimensions have been made with varying three independent factors, such as water-cement (W/C) ratio, length and volume of jute fiber. Initially, the RSM-based on Box-Behnken Design (BBD) was utilized for finding the second order polynomial models for both compressive and tensile strengths of JFRCC. Later, a platform integrated RSM with crow search algorithm (CSA) was applied to find the global optimal solution. The predicted maximum compressive and tensile strengths of 35.1 N/mm(2) and 3.5 N/mm(2) (after 28 days of curing), respectively, were found with an optimal set as fiber length of 6 mm, fiber volume of 0.2% and water-cement (W/C) ratio of 0.55. The predicted optimal conditions were validated experimentally and it was found that the experimental strengths using the optimum values were varied by around 5% from the predicted values. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Jute fiber Concrete Reinforcement Response surface methodology crow search algorithm Optimization

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Image Classification Based on Automatic Neural Architecture search Using Binary crow search algorithm

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IEEE ACCESS 2020年 8卷 189891-189912页

作者： Ahmad, Mobeen Abdullah, Muhammad Moon, Hyeonjoon Yoo, Seong Joon Han, Dongil Sejong Univ Dept Comp Engn Seoul 05006 South Korea

Neural architectures have accelerated the advancement in various domains by enabling automatic pattern detection, image classification, audio recognition, and face recognition etc. However, they are computationally expensive to design and expert knowledge in various domains is required. In this paper, a swarm intelligence algorithm is proposed to search for novel architectures without human intervention that can achieve comparable performance to those of human-designed architectures. This work is inspired by current neural architecture search approaches based on reinforcement learning and genetic algorithm. However, not much attention is paid towards swarm intelligence metaheuristics-based neural architecture search. A framework is proposed for automatically designing neural architectures based on a swarm intelligence metaheuristic: crow search algorithm. First, crow search algorithm is integrated with binary network representation. To make it compatible for Neural Architecture search, the original distance metric is replaced with hamming distance-based similarity measure. Second, the tuning parameters of crow search algorithm are reduced by replacing the static flight length parameter with our dynamic flight length distribution algorithm. Third, the target selection method (random selection) is replaced by tournament select method. The proposed framework is used to search for architectures on MNIST, CIFAR10, and CIFAR100 datasets and achieved 0.18% 3.48% and 15.64% test error, respectively. Furthermore, small-scale transfer experiments are conducted to search architectures for Tiny ImageNet and achieved 34.43% test error. Nonparametric statistical analysis is performed to validate the impact of each modification in improving the quality of search space exploration. The proposed framework has achieved comparable performance with the state-of-the-art approaches, with a comparatively simpler approach and minimum human intervention. The proposed framework can be used t

关键词： Neural architecture search hyperparameter optimization AutoML crow search algorithm metaheuristic image classification deep learning

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Forecasting Daily Solar Radiation Using CEEMDAN Decomposition-Based MARS Model Trained by crow search algorithm

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ENERGIES 2019年第8期12卷 1416页

作者： Rezaie-Balf, Mohammad Maleki, Niloofar Kim, Sungwon Ashrafian, Ali Babaie-Miri, Fatemeh Kim, Nam Won Chung, Il-Moon Alaghmand, Sina Grad Univ Adv Technol Dept Civil Engn Kerman *** Iran Pardisan Univ Dept Civil Engn Freidoonkenar *** Iran Dongyang Univ Dept Railrd Construct & Safety Engn Yeongju 36040 South Korea Tabari Univ Babol Dept Civil Engn Babol Sar *** Iran Shahid Bahonar Univ Dept Phys Educ Kerman *** Iran Korea Inst Civil Engn & Bldg Technol Dept Land Water & Environm Res Goyang 10223 South Korea Monash Univ Dept Civil Engn 23 Coll Walk Clayton Vic 3800 Australia

The precise forecasting of daily solar radiation (DSR) is receiving prominent attention among thriving solar energy studies. In this study, three standalone models, including gene expression programing (GEP), multivariate adaptive regression splines (MARS), and self-adaptive MARS (SaMARS), were evaluated to forecast DSR. A SaMARS model was classified as MARS model when using the crow search algorithm (CSA). In addition, to overcome the limitations of the standalone models, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was employed to enhance the accuracy of DSR forecasting. Therefore, three hybrid models including CEEMDAN-GEP, CEEMDAN-MARS, and CEEMDAN-SaMARS were proposed to forecast DSR in Busan and Incheon stations in South Korea. The performance of proposed models were evaluated and affirmed that the accuracy of the CEEMDAN-SaMARS model (NSE = 0.878-0.883) outperformed CEEMDAN-MARS (NSE = 0.819-0.818), CEEMDAN-GEP (NSE = 0.873-0.789), SaMARS (NSE = 0.846-0.769), MARS (NSE = 0.819-0.758), and GEP (NSE = 0.814-0.755) models at both stations. Therefore, it can be concluded that the optimized CEEMDAN-SaMARS model significantly enhanced the accuracy of DSR forecasting compared to that of standalone models.

关键词： solar radiation forecasting multivariate adaptive regression splines crow search algorithm complete ensemble empirical mode decomposition with adaptive noise gene expression programing

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Enhanced variants of crow search algorithm boosted with cooperative based island model for global optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartA期238卷

作者： Thaher, Thaer Sheta, Alaa Awad, Mohammed Aldasht, Mohammed Arab Amer Univ Dept Comp Syst Engn Jenin Palestine Southern Connecticut State Univ Comp Sci Dept New Haven CT USA Palestine Polytech Univ Dept Comp Engn Hebron Palestine

The crow search algorithm (CSA) is a swarm-based metaheuristic algorithm that simulates the intelligent foraging behaviors of crows. While CSA effectively handles global optimization problems, it suffers from certain limitations, such as low search accuracy and a tendency to converge to local optima. To address these shortcomings, researchers have proposed modifications and enhancements to CSA's search mechanism. One widely explored approach is the structured population mechanism, which maintains diversity during the search process to mitigate premature convergence. The island model, a common structured population method, divides the population into smaller independent sub-populations called islands, each running in parallel. Migration, the primary technique for promoting population diversity, facilitates the exchange of relevant and useful information between islands during iterations. This paper introduces an enhanced variant of CSA, called Enhanced CSA (ECSA), which incorporates the cooperative island model (iECSA) to improve its search capabilities and avoid premature convergence. The proposed iECSA incorporates two enhancements to CSA. Firstly, an adaptive tournament-based selection mechanism is employed to choose the guided solution. Secondly, the basic random movement in CSA is replaced with a modified operator to enhance exploration. The performance of iECSA is evaluated on 53 real-valued mathematical problems, including 23 classical benchmark functions and 30 IEEE-CEC2014 benchmark functions. A sensitivity analysis of key iECSA parameters is conducted to understand their impact on convergence and diversity. The efficacy of iECSA is validated by conducting an extensive evaluation against a comprehensive set of well-established and recently introduced meta-heuristic algorithms, encompassing a total of seventeen different algorithms. Significant differences among these comparative algorithms are established utilizing statistical tests like Wilcoxon's rank-sum

关键词： crow search algorithm Island model Tournament selection Population diversity Metaheuristics

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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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