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Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm

SWARM AND EVOLUTIONARY COMPUTATION 2013年 11卷 16-30页

作者： Agrawal, Sanjay Panda, Rutuparna Bhuyan, Sudipta Panigrahi, B. K. Veer Surendra Sai Univ Technol Dept Elect & Telecommun Engn Burla 768018 India IIT Delhi Dept Elect Engn New Delhi 110003 India

In this paper, optimal thresholds for multi-level thresholding in an image are obtained by maximizing the Tsallis entropy using cuckoo search algorithm. The method is considered as a constrained optimization problem. The solution is obtained through the convergence of a meta-heuristic search algorithm. The proposed algorithm is tested on standard set of images. The results are then compared with that of bacteria foraging optimization (BFO), artificial bee colony (ABC) algorithm, particle swarm optimization (PSO) and genetic algorithm (GA). Results are analyzed both qualitatively and quantitatively. It is observed that our results are also encouraging in terms of CPU time and objective function values. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Image segmentation Multi-level thresholding cuckoo search algorithm Tsallis entropy

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Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by cuckoo search algorithm

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ELECTRIC POWER SYSTEMS REsearch 2017年 146卷 270-285页

作者： Zhang, Xiaobo Wang, Jianzhou Zhang, Kequan Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Lanzhou Univ Coll Atmospher Sci Key Lab Arid Climat Change & Reducing Disaster Ga Lanzhou 730000 Peoples R China

Short-term electric load forecasting (STLF) has been one of the most active areas of research because of its vital role in planning and operation of power systems. Additionally, intelligent methods are increasingly popular in forecasting model applications. However, the observed data set is often contaminated and nonlinear by as a result of such that it becomes difficult to enhance the accuracy of STLF. Therefore, the novel model (CS-SSA-SVM) for electric load forecasting in this paper was successfully proposed by the combination of SSA (singular spectrum analysis), SVM (support vector machine) and CS (cuckoo search) algorithms. First, the signal filtering technique (SSA) is applied for data pre-processing and the novel model subsequently models the resultant series with different forecasting strategies using SVM optimized by the CS algorithm. Finally, experiments of electric load forecasting are used as illustrative examples to evaluate the performance of the developed model. The empirical results demonstrated that the proposed model (CS-SSA-SVM) can improve the performance of electric load forecasting considerably in comparison with other methods (SVM, CS-SVM, SSA-SVM, SARIMA and BPNN). (C) 2017 Elsevier B.V. All rights reserved.

关键词： Singular spectrum analysis cuckoo search algorithm Support vector machine Electric load forecasting Forecasting validity

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Optimal frequency reuse scheme based on cuckoo search algorithm in Li-Fi fifth-generation bidirectional communication

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IET COMMUNICATIONS 2020年第15期14卷 2554-2563页

作者： Murugaveni, S. Mahalakshmi, K. Anna Univ Chennai 600025 Tamil Nadu India Kalaignarkarunanidhi Inst Technol Dept CSE Coimbatore 641402 Tamil Nadu India

Huge demand for bandwidth and capacity is exhibited by various wireless users and applications, for that purpose now the light-fidelity (Li-Fi)-based technologies are developing. In visible light communication, the hybrid networks based on Wi-Fi and Li-Fi has emerged recently. It is based on the concept of integrating small cell Li-Fi networks with large coverage radio frequency (RF) communication systems. For bidirectional data communication, the access links use shared bandwidth since the link of wireless backhaul invokes in the visible light spectrum. Due to the RF spectrum shortage, the bandwidth of Wi-Fi access point (AP) and the coverage of Li-Fi AP are limited in the outdoor environment. To tackle this issue, the authors propose a swarm intelligence-based improved cuckoo search into the frequency reuse of a hybrid Wi-Fi-Li-Fi network in the outdoor environment. The proposed approach employed bidirectional visible light propagation with non-orthogonal multiple access (NOMA) which is an effective approach towards fifth-generation wireless networks. The NOMA can help various users at the same time with the same frequency and multiple power levels. The system throughput and spectral efficiency are improved with dynamic frequency reuse scheme. The simulation results are compared with conventional techniques to show its effectiveness.

关键词： optimisation search problems frequency allocation wireless LAN cellular radio data communication 5G mobile communication free-space optical communication multi-access systems outdoor environment bidirectional visible light propagation nonorthogonal multiple access fifth-generation wireless networks dynamic frequency reuse scheme optimal frequency reuse scheme cuckoo search algorithm Li-Fi fifth-generation wireless users light-fidelity-based technologies visible light communication hybrid networks cell Li-Fi networks coverage radio frequency communication systems bidirectional data communication access links wireless backhaul visible light spectrum RF spectrum shortage Wi-Fi access point Li-Fi AP swarm intelligence-based improved cuckoo search hybrid Wi-Fi-Li-Fi network spectral efficiency system throughput

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An improved segmentation technique for multilevel thresholding of crop image using cuckoo search algorithm based on recursive minimum cross entropy

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IET SIGNAL PROCESSING 2022年第6期16卷 630-649页

作者： Kumar, Arun Kumar, Anil Vishwakarma, Amit Lee, Heung-No PDPM Indian Inst Informat Technol Design & Manage Dept ECE Jabalpur Madhya Pradesh India Gwangju Inst Sci & Technol Sch Elect Engn & Comp Sci Gwangju South Korea

Crop image segmentation is widely used for the analysis of crops. A wide variety of crops are present in the agriculture field, which varies in intensity and complex backgrounds. The thresholding method based on entropy is quite popular for the segmentation of an image. Among all, minimum cross entropy has been widely used. However, the complexity of computation increases when it is used for multilevel thresholding (MLT). Recursive minimum cross entropy is used to resolve the complexity of computation, and cuckoo search (CS) using Levy flight is used to find the optimal threshold for this objective function. Because real-time applications require less processing time while maintaining high performance, which is validated by the CS algorithm using recursive minimum cross entropy (R-MCE-CS) without constraint. The proposed method uses one constraint based on the structural similarity index (SSIM), which leads to an increment in the accuracy for a higher level of thresholding. The accuracy of the proposed method has been tested over 10 crop images with complex backgrounds and high dimensions of colour intensity space. The outcome of the proposed technique has been compared with five algorithms such as wind-driven optimisation (WDO), bacterial foraging optimisation (BFO), differential evolution (DE), artificial bee colony (ABC), and firefly algorithm (FFA). The result shows that the proposed method gives the most promising result, and the accuracy is also improved.

关键词： agriculture image segmentation recursive minimum cross entropy colour intensity space multilevel thresholding improved segmentation technique cuckoo search algorithm statistical distributions entropy SSIM R-MCE-CS MLT structural similarity index crops crop image segmentation CS algorithm Levy flight search problems crop analysis image colour analysis

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Availability evaluation of solar photovoltaic systems using markov modeling and cuckoo search algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第1期46卷 2261-2272页

作者： Kumar, Ashish Maan, Vijay Singh Choudhary, Ravi Saini, Monika Manipal Univ Jaipur Dept Math & Stat Jaipur 303007 Rajasthan India

The main objective of present investigation is to evaluate and optimize the operational availability of the solar photovoltaic systems. As the solar energy is a prominent source of renewal energy and contribute a lot in global development having less environmental impacts but the safety and reliability issues of these systems also observed during the operational phase. Availability is an effective tool that is used to discourse the safety and performance issues of renewal energy sources especially solar photovoltaic systems. Here, a stochastic model is developed for solar photovoltaic system having solar photovoltaic plates, solar charger, solar battery, and inverter. The Markov birth-death process is applied for development of the mathematical model of the proposed system. The chapman-Kolmogorov differential difference equations of the proposed solar photovoltaic system used to predict the steady state availability of system. On the basis of literature, the failure and repair rates of all components of solar photovoltaic system are considered as exponentially distributed. In addition, an effort is also made to predict the optimum availability of solar photovoltaic system using well-known optimization technique cuckoo search algorithm. It is revealed that, the predicted availability of the solar photovoltaic system is 0.9988799 at population size 60 after 700 iterations. The estimated parametric values of the failure and repair rates also derived. To highlight the importance of the study the numerical and graphical results are presented and shared with the system designers and maintenance engineers.

关键词： Renewal energy sources solar photovoltaic systems markov models cuckoo search algorithm availability

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A survey on applications and variants of the cuckoo search algorithm

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APPLIED SOFT COMPUTING 2017年 61卷 1041-1059页

作者： Shehab, Mohammad Khader, Ahamad Tajudin Al-Betar, Mohammed Azmi USM Sch Comp Sci George Town Malaysia Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol POB 50 Irbid Jordan

This paper introduces a comprehensive and exhaustive overview of the cuckoo search algorithm (CSA). CSA is a metaheuristic swarm-based approach established by Yang and Deb [10] to emulate the cuckoo breeding behavior. Owing to the successful application of CSA for a wide variety of optimization problems, since then, researchers have developed several new algorithms in this field. This article displays a comprehensive review of all conducting intensive research survey into the pros and cons, main architecture, and extended versions of this algorithm. It is worth mentioning that the materials of this survey paper are categorized in accordance with the structure of the CSA in which the materials are divided into the CSA versions and modification, publication years, the CSA applications areas, and the hybridization of CSA. The survey paper ends with solid conclusions about the current research on CSA and the possible future directions for the relevant audience and readers. The researchers and practitioners on CSA belong to a wide range of audiences from the domains of optimization, engineering, medical, data mining, clustering, etc., who will benefit from this study. (C) 2017 Elsevier B.V. All rights reserved.

关键词： cuckoo search algorithm Metaheuristic Swarm-based approach Nature-inspired algorithms Optimization

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Strategy to Reduce Floor Area and Flow Distance of Products in Multi-Row Parallel Machine Layout Design Using cuckoo search algorithm

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JOURNAL OF ADVANCED MANUFACTURING SYSTEMS 2021年第2期20卷 273-315页

作者： Mahalingam, Siva Kumar Nagarajan, Lenin Vel Tech Rangarajan Dr Sagunthala Red Inst Sci & Mech Engn Chennai 600062 Tamil Nadu India

A multi-row parallel machine layout (MRPML) design is the best alternative solution to overcome the downside associated with a single-row machine layout design. Especially, the reduction in the investment cost on machines by eliminating its duplicates is the greatest advantage of a MRPML over the single-row machine layout. This paper addresses the MRPML design strategy to reduce the floor area of the layout along with a reduction in demand-based flow distance of products. The cuckoo search algorithm is used for this purpose. The efficacy of the proposed method is assessed and proved by comparing the solutions to the problems associated with single-row layout design dealt by [Lenin et al. Eur. J. Ind. Eng. 12(2) (2018) 252-275] and multi-row layout design dealt by [Vitayasak and Pongcharoen Exp. Syst. Appl. 98 (2018) 129-152] along with few randomly generated problems. This work provides a better tool to identify the optimal MRPML design to satisfy the objectives of mass production industries.

关键词： Multi-row parallel machine layout design floor area flow distance of products cuckoo search algorithm

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A novel hybrid deep learning method with cuckoo search algorithm for classification of arrhythmia disease using ECG signals

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NEURAL COMPUTING & APPLICATIONS 2021年第19期33卷 13123-13143页

作者： Sharma, Pooja Dinkar, Shail Kumar Gupta, D., V Uttarakhand Tech Univ Dehra Dun Uttarakhand India Seth Jai Parkash Mukand Lal Inst Engn & Technol J Radaur Haryana India GBPIET Dept Comp Sci & Applicat Pauri Garhwal India Coll Engn Roorkee COER Dept Math Roorkee Uttar Pradesh India

This work presents an efficient hybridized approach for the classification of electrocardiogram (ECG) samples into crucial arrhythmia classes to detect heartbeat abnormalities. The physiological detection using electrocardiogram (ECG) signals has been the most popular means and widely accepted automated detection system to monitor heart health. Additionally, arrhythmia beat classification plays a prominent role in electrocardiogram (ECG) analysis dedicated elucidate cardiac health status while analyzing heart rhythm. The authors aim to classify ECG samples into major arrhythmia classes precisely by removing the inherent noise of ECG signals in preprocessing phase using discrete wavelet transformation (DWT). The QRS complex plays a crucial role in ECG signal identification. Therefore, the position and amplitude of R-peaks are determined to detect the QRS complex. The feature vectors of the QRS complex are further optimized with cuckoo search (CS) optimization algorithm in addition to denoising signals using DWT to select the most relevant set of features. The Support vector machine (SVM)-trained support vector contains the best training information used to train feed-forward back-propagation neural network (FFBPNN) to propose the variant DWT + CS + SVM-FFBPNN to classify signals among five classes. MIT-BIH arrhythmia database is utilized for different types of heartbeats. The classification analysis based on a variant with optimized feature vector using cuckoo search algorithm and SVM-FFBPNN determines heart rate with an accuracy of 98.319%. In contrast, the variant FFBPNN without optimization obtains 97.95% accuracy. The improved performance of the novel combination of classifiers resulted in overall classification accuracy of 98.53% with precision and recall of 98.247% and 95.68%, respectively. The simulation analysis comprising 3600 samples and 1160 heartbeats also outperformed the existing arrhythmia classifications performed based on neural networks. This illust

关键词： Electrocardiogram (ECG) Arrhythmia Support vector machine (SVM) Feed-forward back-propagation neural network (FFBPNN) cuckoo search algorithm

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MOBCSA: Multi-Objective Binary cuckoo search algorithm for Features Selection in Bioinformatics

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IEEE ACCESS 2024年 12卷 21840-21867页

作者： Abdulwahab, Hudhaifa Mohammed Ajitha, S. Saif, Mufeed Ahmed Naji Murshed, Belal Abdullah Hezam Ghanem, Fahd A. Univ Sci & Technol Dept Comp Sci & IT Taizz Yemen VTU Ramaiah Inst Technol Dept Comp Applicat Bengaluru 560054 Karnataka India VTU Sri Jayachamarajendra Coll Engn Dept Comp Applicat JSS TI Campus Mysore 570006 Karnataka India Amran Univ Coll Engn Dept Comp Sci & IT Amran Yemen Mysore Univ Dept Studies Comp Sci Mysore 570006 Karnataka India Mysore Univ PES Coll Engn Dept Comp Sci & Engn Mandya 571401 India Hodeidah Univ Coll Educ Zabid Dept Comp Sci Al Hudaydah Yemen

In bioinformatics, medical diagnosis models might be significantly impacted by high-dimensional data generated by high-throughput technologies. This data includes redundant or irrelevant genes, making it challenging to identify the relevant genes from such high-dimensional data. Therefore, an effective feature selection (FS) technique is crucial to mitigate dimensionality, thereby enhancing the performance and accuracy of medical diagnosis. The cuckoo search algorithm (CSA) has proven effective in gene selection, demonstrating prowess in exploitation, exploration, and convergence. However, most of the current CSA-based FS techniques deal with gene selection problems as a single objective rather than adopting a multi-objective mechanism. This article proposes the Multi-Objective Binary cuckoo search algorithm (MOBCSA) for gene selection. MOBCSA extends the standard CSA by incorporating multiple objectives, including accuracy of classification and number of selected genes. MOBCSA utilizes an S-shaped transfer function for transforming the algorithm's search space from a continuous to a binary search space. MOBCSA integrates two components: an external archive to save the pareto optimal solutions attained during the search process, and an adaptive crowding distance updating mechanism integrated into the archive to maintain diversity and increase the coverage of optimal solutions. To assess MOBCSA's performance, evaluation experiments were conducted on six benchmark biomedical datasets using three different classifiers. Then, the obtained experimental results were compared against four multi-objective-based state-of-the art FS methods. The findings prove that MOBCSA surpasses the other methods in both accuracy of classification and number of selected genes, where it has obtained an average accuracy ranging from 92.79% to 98.42% and an average number of selected genes ranging from 15.67 to 27.88 for different classifiers and datasets.

关键词： Features selection multi-objective optimization cuckoo search algorithm machine learning data mining bioinformatics

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Seismic optimal design of 3D steel frames using cuckoo search algorithm

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STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2015年第3期24卷 210-227页

作者： Kaveh, A. Bakhshpoori, T. Azimi, M. Iran Univ Sci & Technol Ctr Excellence Fundamental Studies Struct Engn Tehran 16 Iran

The present article is concerned with optimization of real size 3D steel structures under seismic loading based on response spectral and equivalent static analyses. The effect of lateral seismic loading distribution on the achieved optimum designs is investigated. An integrated optimization procedure with the objective of minimizing the self-weight of frame is simply performed interfacing SAP2000 and MATLAB (R) software in the form of parallel computing. The meta-heuristic algorithm chosen here is the cuckoo search (CS) algorithm recently developed as a type of population-based algorithm inspired by the behavior of some cuckoo species in combination with the Levy flight behavior. The CS algorithm performs suitable selection of sections from the American Institute of Steel Construction (AISC) wide-flange (W) shapes list. Strength constraints of the AISC load and resistance factor design specification, geometric limitations, and displacement constraints are imposed on the considered frames. Results show similar weights for optimum designs using spectral and equivalent static analyses;however, different material distribution and seismic behaviors are observed. Copyright (c) 2014 John Wiley & Sons, Ltd.

关键词： optimum design 3D steel frame cuckoo search algorithm meta-heuristic response spectral equivalent static

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