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Improved Simultaneous Localization and Mapping Estimation using crow search algorithm Based Particle Filter

INTERNATIONAL JOURNAL OF ENGINEERING 2023年第10期36卷 1827-1838页

作者： Abedini, M. Jazayeriy, H. Kazemitabar, J. Noshirvani Univ Technol Fac Elect & Comp Engn Babol Iran

Trajectory tracking and positioning are essential requirements in many areas, including robots and autonomous vehicles. In some cases, such as in areas where GPS signals are weak or not available, trajectory tracking is used as an alternative positioning system. In these cases, simultaneous localization and mapping (SLAM), is of great importance as it does not require prior knowledge and empirical offline fingerprint. SLAM can be combined with signal processing algorithms among which, particle filter stands out. However, challenges exist such as particle weights degradation and particles impoverishment that need to be dealt with. In fact, the loss of particle diversity for estimation has led to the lack of particles. To overcome this problem, one solution is to diversify the selection of particles after resampling. In this paper, we proposed a crow search algorithm (CSA) to overcome these issues and improve position estimation. The simulation results showed that this algorithm greatly improved the performance of fast SLAM.

关键词： Particle Filter Fast Simultaneous Localization and Mapping crow search algorithm Sample Depletion

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A Study on Darwinian crow search algorithm for Multilevel Thresholding

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2022年第1期22卷 p1-39页

作者： Ehsaeyan, Ehsan Zolghadrasli, Alireza Shiraz Univ Sch Elect & Comp Engn Dept Commun & Elect Engn Shiraz *** Iran

Multilevel thresholding is a basic method in image segmentation. The conventional image multilevel thresholding algorithms are computationally expensive when the number of decomposed segments is high. In this paper, a novel and powerful technique is suggested for crow search algorithm (CSA) devoted to segmentation applications. The main contribution of our work is to adapt Darwinian evolutionary theory with heuristic CSA. First, the population is divided into specified groups and each group tries to find better location in the search space. A policy of encouragement and punishment is set on searching agents to avoid being trapped in the local optimum and premature solutions. Moreover, to increase the convergence rate of the proposed method, a gray-scale map is applied to out-boundary agents. Ten test images are selected to measure the ability of our algorithm, compared with the famous procedure, energy curve method. Two popular entropies i.e. Otsu and Kapur are employed to evaluate the capability of the introduced algorithm. Eight different search algorithms are implemented and compared to the introduced method. The obtained results show that our method, compared with the original CSA, and other heuristic search methods, can extract multi-level thresholding more efficiently.

关键词： Image segmentation multilevel thresholding Darwinian theory crow search algorithm energy curve swarm intelligence

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An hybrid particle swarm optimization with crow search algorithm for feature selection

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MACHINE LEARNING WITH APPLICATIONS 2021年 6卷

作者： Adamu, Abdulhameed Abdullahi, Mohammed Junaidu, Sahalu Balarabe Hassan, Ibrahim Hayatu Ahmadu Bello Univ Dept Comp Sci Zaria Nigeria Coll Educ Akwanga Dept Comp Sci Akwanga Nigeria

The recent advancements in science, engineering, and technology have facilitated huge generation of datasets. These huge datasets contain noisy, redundant, and irrelevant features which negatively affects the performance of classification techniques in machine learning and data mining process. Feature selection is a pre-processing stage for reducing the dimensionality of datasets by selecting the most important attributes while increasing the accuracy of classification at the same time. In this paper, we present a novel hybrid binary version of enhanced chaotic crow search and particle swarm optimization algorithm (ECCSPSOA) to solve feature selection problems. In the proposed ECCSPSOA, in order to navigate the feature space, we hybridized the enhanced version of the CSA algorithm which has a better search strategy and particle swarm optimization (PSO) which is capable of converging into the best global solution in the search field. We further embed opposition -based learning technique in the local search of the hybrid algorithm. The ECCSPSOA was compared using 15 datasets from the UCI repository with four well-known optimization algorithms, such as particle swarm optimization (PSO), binary particle swarm optimization (BPSO), crow search algorithm (CSA), and chaotic crow search algorithm (CCSA). In the experiments with k -Nearest Neighbour (KNN) as a classifier, six different performance metrics were used. To tackle the over -fitting problem, each dataset is divided into training and testing data using K -fold cross -validation. The computational findings demonstrate that the proposed algorithm obtains an average accuracy rate of 89.67 % over 15 datasets, indicating that our technique exceeds state-of-the-art findings in 12 of the 15 datasets studied. Furthermore, the suggested approach outperforms state-of-the-art methods in terms of fitness value and standard deviation, obtaining the lowest value in 13 and 8 of the datasets studied respectively.

关键词： crow search algorithm Particle swarm optimization Opposition based learning Feature selection Wrapper method

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Prediction of Water Quality Using SoftMax-ELM Optimized Using Adaptive crow-search algorithm

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IEEE ACCESS 2023年 11卷 140900-140913页

作者： Chakravarthy, S. R. Sannasi Bharanidharan, N. Venkatesan, Vinoth Kumar Abbas, Mohamed Rajaguru, Harikumar Mahesh, T. R. Venkatesan, Krishnamoorthy Bannari Amman Inst Technol Dept ECE Sathyamangalam 638401 India Vellore Inst Technol Sch Comp Sci Engn & Informat Syst Vellore 632014 India King Khalid Univ Coll Engn Elect Engn Dept Abha 61421 Saudi Arabia JAIN Dept Comp Sci & Engn Bengaluru 562112 India Arba Minch Univ Dept Math Units Basic Sci Sawla Campus Arba Minch 4400 Ethiopia

Water is a predominant source in the survival and development of all human lives. On top of all, predicting water quality is a significant one since water is essential in regulating our human body. In recent days, the advent of machine learning techniques has been supporting a lot in water quality prediction. Accordingly, Adaptive crow search Optimized SoftMax-Extreme Learning Machine (AdCSO-sELM) is proposed to improve the ELM performance by making the flight length adaptively with respect to the iterations. Here, the research novelty lies in making the CSOA parameters as a dynamic one which in turn provides promising ELM performance. Finally, the proposed AdCSO-sELM provides a superior accuracy of 96.54% for classifying water potability using the Kaggle dataset.

关键词： crow search algorithm extreme learning machine neural network optimization water quality

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Application of the crow search algorithm to achieve an optimal solution for a double link manipulator

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 7卷

作者： Sahu, Venkata Satya Durga Manohar Samal, Padarbinda Panigrahi, Chinmoy Kumar Department of Electrical and Electronics Engineering College of Engineering JNTU-GV Vizianagaram India School of Electrical Engineering Kalinga Institute of Industrial Technology Deemed to Be University Bhubaneswar India

Robotic manipulators are mechanical devices that are electronically controlled and have several linkages that allow them to do specific jobs under specified situations. Here, a double-link manipulator is explored and modified using uncertainty factors that impact the robot manipulator's performance in monitoring the angular position. Unlike, the standard Lagrangian technique is employed to derive the motion equations and calculate the performance of the Two Link Robot Manipulator. Because the motion equation involves a nonlinear system of ordinary differential equations, getting to the target place is challenging. Meta-heuristic search algorithms were standard methodologie to solve any optimization problem, mainly real-time problems. Meta-heuristic algorithms depend upon the natural behaviour of the animals or birds. This paper uses a crow search algorithm to minimize the trajectory and position of the double link manipulator. It also demonstrates how the crow search algorithm can easily be used to solve optimal control problems (OCP) with constraints in MATLAB simulation. The crow search algorithm depends on the crow's behaviour of keeping their excess food in a safe place and getting it as needed. The crow search algorithm (CSA) was compared with particle swarm optimization (PSO), differential evolution (DE), and Grey Wolf Optimization (GWO). The double link manipulator's fundamental Model's performance is computed to find a solution. The crow search method has produced notable results in terms of performance, i.e. 25 % to 98 % better, compared to other algorithms such as DE, PSO, and GWO. © 2024 The Authors

关键词： crow search algorithm Double link manipulator(DLM) Optimal Control Problem

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An improved K-medoids clustering approach based on the crow search algorithm

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Journal of Computational Mathematics and Data Science 2022年 3卷

作者： Sureja, Nitesh Chawda, Bharat Vasant, Avani Department of Computer Science and Engineering Krishna School of Emerging Technology & Applied Research KPGU Vadodara India Department of Computer Science and Engineering Bhailabhai & Bhikhabhai Institute of Technology Vadodara India

K-medoids clustering algorithm is a simple yet effective algorithm that has been applied to solve many clustering problems. Instead of using the mean point as the centre of a cluster, K-medoids uses an actual point to represent it. Medoid is the most centrally located object of the cluster, with a minimum sum of distances to other points. K-medoids can correctly represent the cluster centre as it is robust to outliers. However, the K-medoids algorithm is unsuitable for clustering arbitrary shaped groups of objects and large scale datasets. This is because it uses compactness as a clustering criterion instead of connectivity. An improved k-medoids algorithm based on the crow search algorithm is proposed to overcome the above problems. This research uses the crow search algorithm to improve the balance between the exploration and exploitation process of the K-medoids algorithm. Experimental result comparison shows that the proposed improved algorithm performs better than other competitors. © 2022 The Author(s)

关键词： Clustering crow search algorithm K-means clustering K-medoids clustering Nature-inspired algorithm

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A Hybrid Bald Eagle-crow search algorithm for Gaussian mixture model optimisation in the speaker verification framework

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Decision Analytics Journal 2024年 10卷

作者： Nirmal, Asmita Jayaswal, Deepak Kachare, Pramod H. Department Electronics and Telecommunication Engineering Datta Meghe College of Engineering Airoli Maharashtra Navi Mumbai 400708 India Department Electronics and Telecommunication Engineering St. Francis Institute of Technology Borivali Maharashtra Mumbai 400103 India Department Electronics and Telecommunication Engineering Ramrao Adik Institute of Technology D.Y. Patil Deemded to be University Nerul Maharashtra Navi Mumbai 400706 India

A mutualism of nature-inspired algorithms, the Bald Eagle search algorithm (BESA) with the crow search algorithm (CSA), is proposed for Gaussian Mixture Model optimisation in the speaker verification framework. BESA simulates bald eagles’ hunting behaviour, whereas CSA simulates crows’ food-hiding behaviour. BESA excels in exploitation but has limitations in exploration, whereas CSA showcases strong exploration capabilities but faces challenges in effective exploitation. BESA's hunting characteristic combines crows’ memory update to generate a Hybrid Bald Eagle-crow search algorithm (HBE-CSA). The ability of the proposed HBE-CSA is tested on clean speech utterances from the Librispeech dataset and eight real-life noisy conditions from the AURORA dataset at four different Signal-to-Noise Ratios (SNRs). The comparative analysis shows that the proposed HBE-CSA converges with higher log-likelihood than individual search algorithms and the state-of-the-art Expectation Maximisation algorithm. Detection error trade-off curves validate the results of convergence behaviour, indicating that best speaker models are built using the proposed HBE-CSA. The noise analysis shows the smallest equal error rate for the proposed HBE-CSA at different SNRs than competing algorithms. The proposed HBE-CSA holds promise for optimising models not only in speaker verification but also across other areas of speech processing. © 2023 The Author(s)

关键词： Bald Eagle search algorithm crow search algorithm Gaussian mixture model Hybrid optimisation algorithm Speaker verification

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CNN-LSTM Based on crow search Optimization algorithm for Product Review Classification

CNN-LSTM Based on Crow Search Optimization Algorithm for Pro...

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2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Yamsani, Nagendar Jothi, B. Srinivasan, N. Dineshkumar, R. Ramachandra, A.C. Sr University School of Computer Science and Artificial Intelligence Warangal India Srm Institute of Science and Technology Kattankulathur Department of Computational Intelligence Chennai India School of Computing Sathyabama Institute of Science and Technology Department of Cse Chennai India Saveetha School of Engineering Sriperumbudur Department of Ece Thandalam India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

Product reviews means an everyone's opinion or views towards particular products or services that delivered to a specific company. In modern global, reviews of online product play a vital part in the estimation process of product in a company and their customers. The extracted features are selected by using the Chi-Squared technique and for the classification of product review proposed a model by utilizing the combined deep learning techniques like Convolutional Neural Network (CNN) and Long- Short Term Memory (LSTM) and it optimized by crow search algorithm (CSA). CNN is used for capturing the local information and LSTM neural network is used for the long-distance dependencies. By utilizing the benefits of CNN and LSTM and it is optimized by CSA for the effective classification of product review. The proposed model performance is estimated by using the performance metrics like Accuracy, Recall, Specificity, Sensitivity and F1-score are 98.35%, 93.20%, 97.57%, 94.82% and 95.78% respectively. The proposed model is compared with other existing model in terms of performance metrics which shows the better performance results. © 2023 IEEE.

关键词： Chi-Squared Convolutional Neural Network crow search algorithm Long-Short Term Memory and Product Review

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GWTM and crow search algorithm for Face Recognition in Low-Resolution Images

GWTM and Crow Search Algorithm for Face Recognition in Low-R...

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International Conference on Advances in Computing, Communication Control and Networking

作者： Renjith Thomas M. J. S. Rangachar Hindustan Institute of Technology and Science Padur Chennai 603 103 India Department of School of Electrical Sciences Hindustan Institute of Technology and Science Padur Chennai 603 103 India

ISBN: (纸本)9781538641200

Nowadays, face recognition has gained more consideration in the area of image processing and computer vision. The existing face recognition systems provide better performance using the frontal images with high resolution. The major issue in face recognition is the Low-Resolution face images. To alleviate this issue, this paper proposes the face recognition system by integrating the Gabor filter + wavelet + texture (GWTM) operator and the crow search algorithm to increase the classification performance, while deploying the LR images. Initially, the input image is given to the preprocessing, and the low-resolution image is generated. Then, this low-resolution image is applied to the kernel regression model to produce the image with high-resolution. Then, both the low-resolution and the high-resolution images are applied to the GWTM operator for extracting the features. The result of the GWTM is provided to the crow search algorithm for producing the intermediate images. Finally, the intermediate images are given to the spherical SVM classifier for optimal recognition. The performance of the proposed method is analyzed with the existing methods using three evaluation metrics, such as accuracy, FAR, and FRR. From the experimental results, it can be show that the proposed method attains the higher accuracy of 0.9500, minimum FAR and FRR of 0.0500.

关键词： Face recognition GWTM crow search algorithm A low-resolution image Optimal recognition

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Performance Evaluation of Ingenious crow search Optimization algorithm for Protein Structure Prediction

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PROCESSES 2023年第6期11卷 1655页

作者： Alshamrani, Ahmad M. Saxena, Akash Shekhawat, Shalini Zawbaa, Hossam M. Mohamed, Ali Wagdy King Saud Univ Coll Sci Stat & Operat Res Dept POB 2455 Riyadh 11451 Saudi Arabia Cent Univ Haryana Dept Elect Engn Mahendergarh 123031 Haryana India Swami Keshvanand Inst Technol Management & Gramoth Dept Math Jaipur 302017 Rajasthan India Technol Univ Dublin CeADAR Irelands Ctr Appl AI Dublin D7E WV4 Ireland Cairo Univ Fac Grad Studies Stat Res Operat Res Dept Giza 12613 Egypt Appl Sci Private Univ Appl Sci Res Ctr Amman 11937 Jordan

Protein structure prediction is one of the important aspects while dealing with critical diseases. An early prediction of protein folding helps in clinical diagnosis. In recent years, applications of metaheuristic algorithms have been substantially increased due to the fact that this problem is computationally complex and time-consuming. Metaheuristics are proven to be an adequate tool for dealing with complex problems with higher computational efficiency than conventional tools. The work presented in this paper is the development and testing of the Ingenious crow search algorithm (ICSA). First, the algorithm is tested on standard mathematical functions with known properties. Then, the application of newly developed ICSA is explored on protein structure prediction. The efficacy of this algorithm is tested on a bench of artificial proteins and real proteins of medium length. The comparative analysis of the optimization performance is carried out with some of the leading variants of the crow search algorithm (CSA). The statistical comparison of the results shows the supremacy of the ICSA for almost all protein sequences.

关键词： protein structure prediction swarm intelligence crow search algorithm numerical optimization

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