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Feature selection based on dynamic crow search algorithm for high-dimensional data classification

EXPERT SYSTEMS WITH APPLICATIONS 2024年 250卷

作者： Jiang, He Yang, Ye Wan, Qiuying Dong, Yao Xi An Jiao Tong Univ Sch Econ & Finance Xian 710061 Shannxi Peoples R China Cent China Normal Univ Sch Econ & Business Adm Wuhan 430079 Hubei Peoples R China JiangXi Univ Finance & Econ Sch Stat & Data Sci Nanchang 330013 Jiangxi Peoples R China

High-dimensional biomedical data plays an important role in disease diagnosis and classification. To analyze the high-dimensional biomedical data, machine learning algorithms are widely used. However, a great amount of redundant, irrelevant or weak correlation features are prevalent in these datasets, which significantly deteriorate the capability of machine learning techniques. It is necessary to select the most informative features and discard the useless ones for enhancing the classification accuracy and reducing the dimensionality. To address this issue, an improved crow search algorithm (CSA) which is known as the Dynamic CSA (DCSA) is proposed to find the optimal feature subset in high-dimensional classification tasks. In DCSA, three modifications are investigated. Firstly, we propose a dynamic bi-level awareness probability to guide the transition between global search and local search. Consequently, the balance between exploration and exploitation is improved. Secondly, levy flight with a unfixed step length control parameter is employed as the global search and the random search mechanism of the original CSA is abandoned. Thirdly, a dynamic flight length strategy is adopted to enhance the local search and speed up convergence. All in all, DCSA is developed as a wrapper feature selection model, where KNN classifier is applied as the feature subset evaluator. The performance of DCSA is measured on seven high-dimensional biomedical datasets. Experimental results show that DCSA outperforms other state-of-the-art methods. Specifically, DCSA achieved the highest average accuracy in six data. In terms of numbers of selected features, DCSA gained the smallest subset size in a seven data. And for SRBCT data, the prediction accuracy of DCSA is at 100%.

关键词： Feature selection High-dimensional data classification crow search algorithm

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A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm

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COMPUTERS & STRUCTURES 2016年 169卷 1-12页

作者： Askarzadeh, Alireza Grad Univ Adv Technol Inst Sci & High Technol & Environm Sci Dept Energy Management & Optimizat Kerman Iran

This paper proposes a novel metaheuristic optimizer, named crow search algorithm (CSA), based on the intelligent behavior of crows. CSA is a population-based technique which works based on this idea that crows store their excess food in hiding places and retrieve it when the food is needed. CSA is applied to optimize six constrained engineering design problems which have different natures of objective functions, constraints and decision variables. The results obtained by CSA are compared with the results of various algorithms. Simulation results reveal that using CSA may lead to finding promising results compared to the other algorithms. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Metaheuristic optimization crow search algorithm Constrained engineering optimization

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Improved diagnosis of Parkinson's disease using optimized crow search algorithm

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COMPUTERS & ELECTRICAL ENGINEERING 2018年 68卷 412-424页

作者： Gupta, Deepak Sundaram, Shirsh Khanna, Ashish Hassanien, Aboul Ella de Albuquerque, Victor Hugo C. Maharaja Agrasen Inst Technol Delhi India Maharaja Agrasen Inst Technol Dept Comp Sci Delhi India Cairo Univ Fac Comp & Informat Cairo Egypt Univ Fortaleza Grad Program Appl Informat Fortaleza CE Brazil

Diagnosis of Parkinson's disease at its early stage is important in proper treatment of the patients so they can lead productive lives for as long as possible. Although many techniques have been proposed to diagnose the Parkinson's disease at an early stage but none of them are efficient. In this work, to improve the diagnosis of Parkinson's disease, we have introduced a novel improved and optimized version of crow search algorithm(OCSA). The proposed OCSA can be used in predicting the Parkinson's disease with an accuracy of 100% and help individual to have proper treatment at early stage. The performance of OCSA has been measured for 20 benchmark datasets and the results have been compared with the original chaotic crow search algorithm(CCSA). The experimental result reveals that the proposed nature-inspired algorithm finds an optimal subset of features, maximizing the accuracy and minimizing a number of features selected and is more stable.

关键词： Parkinson's disease crow search algorithm Optimized crow search algorithm Feature selection Chaotic crow search algorithm Decision Tree Random Forest k-Nearest Neighbor

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Improved versions of crow search algorithm for solving global numerical optimization problems

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APPLIED INTELLIGENCE 2023年第22期53卷 26840-26884页

作者： Sheta, Alaa Braik, Malik AI-Hiary, Heba Mirjahlili, Seyedali Southern Connecticut State Univ Comp Sci Dept 501 Crescent St New Haven CT 06515 USA Al Balqa Appl Univ Dept Comp Sci Al Salt Jordan Al Balqa Appl Univ Dept Comp Informat Syst Al Salt Jordan Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Adelaide SA Australia Obuda Univ Univ Res & Innovat Ctr H-1034 Budapest Hungary

Over recent decades, research in Artificial Intelligence (AI) has developed a broad range of approaches and methods that can be utilized or adapted to address complex optimization problems. As real-world problems get increasingly complicated, this requires an effective optimization method. Various meta-heuristic algorithms have been developed and applied in the optimization domain. This paper used and ameliorated a promising meta-heuristic approach named crow search algorithm (CSA) to address numerical optimization problems. Although CSA can efficiently optimize many problems, it needs more searchability and early convergence. Its positioning updating process was improved by supporting two adaptive parameters: flight length (fl) and awareness probability (AP) to tackle these curbs. This is to manage the exploration and exploitation conducts of CSA in the search space. This process takes advantage of the randomization of crows in CSA and the adoption of well-known growth functions. These functions were recognized as exponential, power, and S-shaped functions to develop three different improved versions of CSA, referred to as Exponential CSA (ECSA), Power CSA (PCSA), and S-shaped CSA (SCSA). In each of these variants, two different functions were used to amend the values of fl and AP. A new dominant parameter was added to the positioning updating process of these algorithms to enhance exploration and exploitation behaviors further. The reliability of the proposed algorithms was evaluated on 67 benchmark functions, and their performance was quantified using relevant assessment criteria. The functionality of these algorithms was illustrated by tackling four engineering design problems. A comparative study was made to explore the efficacy of the proposed algorithms over the standard one and other methods. Overall results showed that ECSA, PCSA, and SCSA have convincing merits with superior performance compared to the others.

关键词： crow search algorithm Optimization Meta-heuristics Engineering problems

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A probabilistic simplified sine cosine crow search algorithm for global optimization problems

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ENGINEERING WITH COMPUTERS 2023年第3期39卷 1823-1841页

作者： Rao, Yundi He, Dengxu Qu, Liangdong Guangxi Univ Nationalities Sch Math & Phys Nanning 530006 Guangxi Peoples R China Guangxi Univ Nationalities Coll Artificial Intelligence Nanning 530006 Guangxi Peoples R China

crow search algorithm (CSA) is a novel meta-heuristic optimizer that is based on the intelligent behavior of crows. There is rather simple with two adjustable parameters only, which in turn makes it very attractive for applications in different engineering areas. To compensate for the blindness of the location update perceived in CSA when being tracked, this paper introduces a probability simplified sine cosine algorithm to form a new hybrid algorithm called PSCCSA (Probabilistic Simplified Sine Cosine crow search algorithm). In 16 well-known standard test functions, the proposed algorithm was compared with 5 meta-heuristic algorithms for evaluating the effectiveness of the algorithms (crow search algorithm, standard Sine Cosine algorithm, Probability Simplified Sine Cosine algorithm, Multi-Verse Optimizer and Particle Swarm Optimization). In addition, PSCCSA has also been used to solve four classic engineering problems (pressure vessel design, speed reducer design, welded beam design and tension/compression spring design problem). The results show that the proposed algorithm is feasible and effective.

关键词： crow search algorithm Probabilistic simplified Sine cosine algorithm Global optimization Engineering optimization

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Topology and size optimization of truss structures using an improved crow search algorithm

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STRUCTURAL ENGINEERING AND MECHANICS 2021年第6期77卷 779-795页

作者： Mashayekhi, Mostafa Yousefi, Roghayeh Vali E Asr Univ Rafsanjan Dept Civil Engn Rafsanjan Iran

In the recent decades, various optimization algorithms have been considered for the optimization of structures. In this research, a new enhanced algorithm is used for the size and topology optimization of truss structures. This algorithm, which is obtained from the combination of crow search algorithm (CSA) and the Cellular Automata (CA) method, is called CA-CSA method. In the first iteration of the CA-CSA method, some of the best designs of the crow?s memory are first selected and then located in the cells of CA. Then, a random cell is selected from CA, and the best design is chosen from the selected cell and its neighborhood;it is considered as a ?local superior design? (LSD). In the optimization process, the LSD design is used to modify the CSA method. Numerical examples show that the CA-CSA method is more effective than CSA in the size and topology optimization of the truss structures.

关键词： truss structures size optimization topology optimization crow search algorithm cellular automata method

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Feature selection via a novel chaotic crow search algorithm

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NEURAL COMPUTING & APPLICATIONS 2019年第1期31卷 171-188页

作者： Sayed, Gehad Ismail Hassanien, Aboul Ella Azar, Ahmad Taher Cairo Univ Fac Comp & Informat Cairo Egypt Benha Univ Fac Comp & Informat Banha Egypt Nile Univ NISC Giza Egypt

crow search algorithm (CSA) is a new natural inspired algorithm proposed by Askarzadeh in 2016. The main inspiration of CSA came from crow search mechanism for hiding their food. Like most of the optimization algorithms, CSA suffers from low convergence rate and entrapment in local optima. In this paper, a novel meta-heuristic optimizer, namely chaotic crow search algorithm (CCSA), is proposed to overcome these problems. The proposed CCSA is applied to optimize feature selection problem for 20 benchmark datasets. Ten chaotic maps are employed during the optimization process of CSA. The performance of CCSA is compared with other well-known and recent optimization algorithms. Experimental results reveal the capability of CCSA to find an optimal feature subset which maximizes the classification performance and minimizes the number of selected features. Moreover, the results show that CCSA is superior compared to CSA and the other algorithms. In addition, the experiments show that sine chaotic map is the appropriate map to significantly boost the performance of CSA.

关键词： crow search algorithm Feature selection Optimization algorithm Chaos theory

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AN ADAPTIVE DYNAMIC NEIGHBORHOOD crow search algorithm FOR SOLVING PERMUTATION FLOW SHOP SCHEDULING PROBLEMS

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2024年第1期20卷 84-111页

作者： Zhao, Cai Wu, Liang-hong Zuo, Ci-li Zhang, Hong-qiang Xiao, Qing Hunan Univ Sci & Technol Sch Mech Engn Xiangtan 411100 Hunan Peoples R China Hunan Univ Sci & Technolog Sch Informat & Elect Engn Xiangtan 411100 Hunan Peoples R China

To effectively solve the permutation flow-shop scheduling problem (PFSP), an adaptive dynamic neighborhood crow search algorithm (AdnCSA) is proposed to minimize the makespan. Firstly, a modified heuristic algorithm based on nawaz-enscore-ham (NEH) was proposed to improve the quality and diversity of the initial population. Secondly, the smallest-position-value (SPV) rule is used to encode the population so that it can handle the discrete sched-uling problem. Lastly, the top 20% of individuals with best fitness was selected to execute neighborhood search, and an adaptive dynamic neighborhood struc-ture is introduced to balance the global and local search ability of the proposed algorithm. To evaluate the effectiveness of the proposed method, the Rec and Taillard benchmarks were used to test the performance. Compared with nine recent metaheuristic method for solving the PFSP, the numerical results pro-duced by the proposed AdnCSA are promising and show great potential for solving the permutation flow-shop scheduling problem.

关键词： Permutation flow shop population initialization adaptive dynamic neighborhood crow search algorithm nawaz-enscore-ham

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A New Hybrid algorithm Based on Grey Wolf Optimization and crow search algorithm for Unconstrained Function Optimization and Feature Selection

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IEEE ACCESS 2019年 7卷 26343-26361页

作者： Arora, Sankalap Singh, Harpreet Sharma, Manik Sharma, Sanjeev Anand, Priyanka DAV Univ Dept Comp Sci & Engn Jalandhar 144012 Punjab India DAV Univ Dept Comp Sci & Applicat Jalandhar 144012 Punjab India Lovely Profess Univ Dept Comp Sci & Engn Jalandhar 144411 Punjab India

Grey wolf optimizer (GWO) is a very efficient metaheuristic inspired by the hierarchy of the Canis lupus wolves. It has been extensively employed to a variety of practical applications. crow search algorithm (CSA) is a recently proposed metaheuristic algorithm, which mimics the intellectual conduct of crows. In this paper, a hybrid GWO with CSA, namely GWOCSA is proposed, which combines the strengths of both the algorithms effectively with the aim to generate promising candidate solutions in order to achieve global optima efficiently. In order to validate the competence of the proposed hybrid GWOCSA, a widely utilized set of 23 benchmark test functions having a wide range of dimensions and varied complexities is used in this paper. The results obtained by the proposed algorithm are compared to 10 other algorithms in this paper for verification. The statistical results demonstrate that the GWOCSA outperforms other algorithms, including the recent variants of GWO called, enhanced grey wolf optimizer (EGWO) and augmented grey wolf optimizer (AGWO) in terms of high local optima avoidance ability and fast convergence speed. Furthermore, in order to demonstrate the applicability of the proposed algorithm at solving complex real-world problems, the GWOCSA is also employed to solve the feature selection problem as well. The GWOCSA as a feature selection approach is tested on 21 widely employed data sets acquired from the University of California at Irvine repository. The experimental results are compared to the stateof-the-art feature selection techniques, including the native GWO, the EGWO, and the AGWO. The results reveal that the GWOCSA has comprehensive superiority in solving the feature selection problem, which proves the capability of the proposed algorithm in solving real-world complex problems.

关键词： Grey wolf optimizer crow search algorithm hybrid algorithm function optimization feature selection

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Amelioration of task scheduling in cloud computing using crow search algorithm

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NEURAL COMPUTING & APPLICATIONS 2020年第10期32卷 5901-5907页

作者： Kumar, K. R. Prasanna Kousalya, K. Kongu Engn Coll Dept Informat Technol Erode India Kongu Engn Coll Dept Comp Sci & Engn Erode India

Cloud computing is a dynamic and diverse environment across different geographical locations. In reality, it consists of a vast number of tasks and computing resources. In cloud, task scheduling algorithm is the core player which identifies the suitable virtual machine (VM) for a task. The task scheduling algorithm is responsible for reducing the makespan of the schedule. In recent years, nature-inspired algorithms are applied to task scheduling which performs better than conventional algorithms. In this paper, crow search algorithm (CSA) is proposed for task scheduling in cloud. It is inspired from the food collecting habits of crow. In reality, the crow keeps on eyeing on its other mates to find a better food source than current food source. In this way, the CSA finds a suitable VM for the task and minimizes the makespan. Experiments are carried out using cloudsim to measure the performance of the CSA along with Min-Min and ant algorithms. Simulation results reveal that CSA algorithm performs better compared to Min-Min and Ant algorithms.

关键词： algorithms Cloud computing Task scheduling crow search algorithm Nature-inspired

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