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A cross entropy and whale optimization algorithm based image segmentation for aerial images

International Journal of Information Technology (Singapore) 2024年 1-15页

作者： Ahmed, Saifuddin Biswas, Anupam Department of Computer Science and Engineering National Institute of Technology Cachar Silchar 788010 India

There are several image segmentation techniques for image processing such as, thresholding, region based, edge based, watershed etc. However, the multilevel thresholding based image segmentation is the most widely used approach. This approach mostly suffers high computational complexity with the increasing number of threshold levels. The researchers have been using optimization techniques to resolve this issue. This paper presented a multilevel thresholding technique for segmenting aerial images using whale optimization algorithm (WOA). It uses Minimum Cross Entropy (MCE) as an objective function for selecting a set of optimum threshold values. The presented method is evaluated by using a set of standard aerial images. The evaluated performance of the proposed technique is compared with Bacterial Foraging optimization (BFO), Moth-flame Flame optimization (MFO) and Particle Swarm optimization (PSO). The overall performance and the quality of segmented images are evaluated based on the following parameters, Average Mean Structural Similarity Index (MSSIM), Average Feature Similarity Index (FSIM), Average Peak Signal to Noise Ratio (PSNR), average mean objective functions values and average CPU rum time values. In terms of quality of the segmented images based on MSSIM, FSIM and PSNR values the presented method performs better than other methods and we found the average CPU run time also to a considerable range. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Cross entropy Image segmentation Multilevel thresholding optimization technique whale optimization algorithm

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An Effective Approach for Determining Rock Discontinuity Sets Using a Modified whale optimization algorithm

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ROCK MECHANICS AND ROCK ENGINEERING 2023年第8期56卷 6143-6155页

作者： Yi, Xiaoyu Feng, Wenkai Wu, Wenxuan Zhou, Yongjian Dong, Shan Chengdu Univ Technol Coll Environm & Civil Engn Chengdu 610059 Peoples R China Chengdu Univ Technol State Key Lab Geohazard Prevent & Geoenvironm Prot Chengdu 610059 Peoples R China China Univ Geosci Badong Natl Observat & Res Stn Geohazards Wuhan 430074 Peoples R China

HighlightsAn effective method for determining rock discontinuity sets based on the MWOA is *** proposed method has a good clustering effect, convergence speed, and stabilityThe proposed method can determine rock disco... 详细信息

关键词： Rock discontinuity sets Cluster analysis whale optimization algorithm Discontinuity orientation

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An Improvement on Exploration Step of whale optimization algorithm with Levy Distribution for Classification Problems 5

An Improvement on Exploration Step of Whale Optimization Alg...

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5th International Conference on Big Data Analytics and Practices (IBDAP)

作者： Pravesjit, Sakkayaphop Kantawong, Krittika Kamkhad, Natdanai Sabaipom, Saksit Monchanuan, Jantawan Jitkongchuen, Duangjai Thammano, Arit Longpradit, Panchit Univ Phayao Sch Informat & Commun Technol Phayao Thailand Big Data Inst Bangkok Thailand King Mongkuts Inst Technol Ladkrabang Fac Informat Technol Ladkrabang Thailand Mahidol Univ Fac Social Sci & Humanities Salaya Nakhon Pathom Thailand

ISBN: (纸本)9798350391749

The proposed system represents an enhanced movement in search of food of whale optimization algorithm (WOA), based on Levy distribution for image classification of grape leaf disease. In the preprocessing, the proposed system uses convolution kernels to transform images into input data within the range of (0,1). Thereafter, the Levy distribution is incorporated into the WOA model as an exploration search mechanism. A grape leaf dataset from the Plant Village project (***), consisting of 4062 labeled images with dimensions of 256 by 256 pixels and divided into four classes -healthy, Black Rot, Black Measles, and Isariopsis leaf spot -is used to evaluate the performance of the proposed system. Experimental results show that the proposed system is better than Visual Geometry Group (VGG16), Gray Level Co-occurrence Matrix (GLCM) with SVM, Low contrast haze reduction-neighborhood component analysis with SVM, and whale optimization algorithm (WOA).

关键词： whale optimization algorithm levy distribution convolution kernel grape leaf disease

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Mango Pest Detection Using Entropy-ELM with whale optimization algorithm

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Intelligent Automation & Soft Computing 2023年第3期35卷 3447-3458页

作者： U.Muthaiah S.Chitra Department of Computer Science and Engineering Sri Shanmugha College of Engineering and TechnologySankari637304India Department of Computer Science and Engineering Er.Perumal Manimekalai College of EngineeringHosur635117India

Image processing,agricultural production,andﬁeld monitoring are essential studies in the researchﬁ*** diseases have an impact on agricultural production and *** disease detection at a preliminary phase reduces economic losses and improves the quality of *** identifying the agricultural pests is usually evident in plants;also,it takes more time and is an expensive technique.A drone system has been developed to gather photographs over enormous regions such as farm areas and *** atmosphere generates vast amounts of data as it is monitored closely;the evaluation of this big data would increase the production of agricultural *** paper aims to identify pests in mango trees such as hoppers,mealybugs,inﬂorescence midges,fruitﬂies,and stem *** of the massive volumes of large-scale high-dimensional big data collected,it is necessary to reduce the dimensionality of the input for classify-ing *** community-based cumulative algorithm was used to classify the pests in the existing *** proposed method uses the Entropy-ELM method with whale optimization to improve the classiﬁcation in detecting pests in *** Entropy-ELM method with the whale optimization algorithm(WOA)is used for feature selection,enhancing mango pests’classiﬁcation *** Vector Machines(SVMs)are especially effective for classifying while users get var-ious classes in which they are *** are created as suitable classiﬁers to categorize any dataset in Big Data *** proposed Entropy-ELM-WOA is more capable compared to the existing systems.

关键词： whale optimization algorithm Entropy-ELM feature selection pests detection support vector machine mango trees classiﬁcation

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Parameter optimization of PID controller based on an enhanced whale optimization algorithm for AVR system

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OPERATIONAL RESEARCH 2023年第3期23卷 44页

作者： Zhang, Jinzhong Zhang, Tan Zhang, Gang Kong, Min West Anhui Univ Sch Elect & Optoelect Engn Luan 237012 Peoples R China

This paper proposes an enhanced whale optimization algorithm based on a ranking-based mutation operator (EWOA) to stabilize the PID controller's parameters in the AVR system, and the intention is to arrive at the ideal objective function value by modifying the control parameters. The whale optimization algorithm is based on the humpback whale's bubble-net attacking behavior, which imitates shrinkage encircling prey, bubble-net attacking prey and random searching for prey to address the complicated optimization issue. The ranking-based mutation operator can maximize the selection probability, screen out the best search individual, eliminate premature convergence, promote the convergence speed and elevate the exploitation ability. The EWOA not only has substantial robustness and stability to strengthen the optimization efficiency and recognize the optimal solution but also combines exploration with exploitation to expand the convergence rate and calculation precision. The EWOA is contrasted with other algorithms to validate the practicability and usefulness. The experimental results demonstrate that the EWOA has a quicker convergence rate, higher computation precision, shorter execution time and greater stability, which is a remarkable and practical method for addressing the parameter optimization of the PID controller in the AVR system.

关键词： whale optimization algorithm Ranking-based mutation operator Objective function value Control parameters Experimental results

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Parameter Identification for Static Var Compensator Model Using Sensitivity Analysis and Improved whale optimization algorithm

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CSEE Journal of Power and Energy Systems 2022年第2期8卷 535-547页

作者： Qiang Guo Lei Gao Xiaojie Chu Huadong Sun China Electric Power Research Institute Beijing 100192China

A parameter estimation method based on an improved whale optimization algorithm is proposed in this paper to identify the parameters of a static var compensator(SVC)***,a mathematical model of SVC is ***,the reverse learning strategy and Levy flight disturbance strategy are introduced to improve the whale optimization algorithm,and the improved whale optimization algorithm is applied to the parameter identification of the static var compensator ***,a stepwise identification method,by analyzing the local sensitivities of parameters,is proposed which solves the problem of low accuracy caused by multi-parameter *** method provides a new estimation strategy for accurately identifying the parameters of the static var compensator *** results show that the parameter estimation method can be an effective tool to solve the problem of parameter identification for the SVC model.

关键词： Local sensitivity analysis parameter estimation static var compensator model stepwise identification method whale optimization algorithm

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Hybridizing whale optimization algorithm With Particle Swarm optimization for Scheduling a Dual-Command Storage/Retrieval Machine

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IEEE ACCESS 2023年 11卷 21264-21282页

作者： Hsu, Hsien-Pin Wang, Chia-Nan Natl Kaohsiung Univ Sci & Technol Dept Supply Chain Management Kaohsiung 81157 Taiwan Natl Kaohsiung Univ Sci & Technol Dept Ind Engn & Management Kaohsiung 807618 Taiwan

whale optimization algorithm (WOA) and particle swarm optimization (PSO) have been used individually usually. However, a separate use of them has a limitation. Hybridizing WOA with PSO is expected to evolve solutions better due to the cooperation between whales and seabirds. Developing such kind of model is the focus of this research. A framework has been further proposed to best utilize such hybridizations for developing simulation-based optimization approaches. The framework has the advantage of integrating metaheuristic, simulation, and optimization seamlessly. It can waive the rigorous and labor-intensive optimization procedure required for traditional simulation. In this research, simulation-based optimization approaches are used to deal with the dual-command block scheduling problem of a manufacturing firm's storage/retrieval (S/R) machine in an automated storage/retrieval system. The S/R machine is mainly used to store/retrieve stock-keeping units in an automated storage/retrieval system. Three simulation-based optimization approaches, Hybrid1 (WOA+PSO), Hybrid2 (WOA+PSO), and Hybrid3 (WOA+PSO), have been developed. To investigate their effectiveness, experiments have been conducted to compare them with their base models, WOA and PSO, as well as the genetic algorithm (GA) and PWOA. The PWOA is an abbreviation of a hybridization of PSO and WOA proposed in a previous study. The experimental results show that Hybrid3 (WOA+PSO) outperforms Hybrid2 (WOA+PSO), Hybrid1 (WOA+PSO), WOA, PSO, PWOA, and GA. The uses of techniques such as hybridization, Neighborhood heuristic, and adaptive movements of whales empower Hybrid3 (WOA+PSO) the most.

关键词： Metaheuristics Manufacturing Genetic algorithms Job shop scheduling Analytical models Particle swarm optimization whale optimization algorithms whale optimization algorithm particle swarm optimization storage retrieval machine scheduling

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A novel improved whale optimization algorithm to solve numerical optimization and real-world applications

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ARTIFICIAL INTELLIGENCE REVIEW 2022年第6期55卷 4605-4716页

作者： Chakraborty, Sanjoy Sharma, Sushmita Saha, Apu Kumar Saha, Ashim Natl Inst Technol Agartala Dept Comp Sci & Engn Agartala 799046 Tripura India Iswar Chandra Vidyasagar Coll Belonia 799155 Tripura India Natl Inst Technol Agartala Dept Math Agartala 799046 Tripura India

whale optimization algorithm (WOA) has been developed based on the hunting behavior of humpback whales. Though it has a considerable convergence speed, WOA suffers from diversity in the solution due to the low exploration of search space. As a result, it tends to trap in local optima and suffer from low solution accuracy. This study proposes a novel improved WOA method (ImWOA) with increased diversity in the solution to avoid the aforesaid gaps. The random solution selection process in the search prey phase is altered to increase exploration. The whale's cooperative hunting strategy is also incorporated in the algorithm's exploitation phase to balance the exploration and exploitation phase of WOA. Also, the total iterations are divided into two halves explicitly for exploration and exploitation purposes. The modifications facilitate WOA to jump out of local optima, increase solution accuracy, and increase convergence speed. The experiments were carried out evaluating IEEE CEC 2017 functions in dimensions 10, 30, 50, and 100. The performances were compared with basic algorithms as well as recent WOA variants. Three engineering design problems have also been solved to check its problem-solving ability and compared with a wide range of algorithms. Moreover, the image segmentation problem with multiple thresholding approaches has been solved by using the proposed ImWOA. Comparing results with state-of-the-art algorithms and modified WOAs, statistical analysis, diversity analysis, and convergence analysis validate that ImWOA is superior or competitive.

关键词： whale optimization algorithm IEEE CEC 2017 functions Engineering design problem Image segmentation

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Fusion of wavelet packet-fuzzy contraction and whale optimization algorithm for hemoglobin spectral feature extraction

Fusion of wavelet packet-fuzzy contraction and whale optimiz...

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2024 Applied Optics and Photonics China

作者： Fang, Renjie Han, Xin Li, Xiangxian Tong, Jingjing Gao, Minguang Chinese Acad Sci Hefei Inst Phys Sci Hefei 230031 Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China

ISBN: (数字)9781510687769

ISBN: (纸本)9781510687752;9781510687769

Near-infrared spectroscopy (NIRS) technology has a wide range of potential applications in hemoglobin detection, but its accuracy is susceptible to noise and background interference. In order to improve the accuracy of quantitative analysis of hemoglobin in blood NIRS spectra, this study introduces a hybrid method (WPT-FS-WOA), which integrates wavelet packet transform (WPT), fuzzy shrinkage (FS), and whale optimization algorithm (WOA) for hemoglobin feature band extraction. The method uses WPT to decompose the blood near-infrared spectrum at multiple scales, applies FS to denoise the wavelet packet node coefficients for data affiliation assessment, and combines WOA to optimize the wavelet packet nodes at different frequencies, and finally reconstructs the hemoglobin feature spectrum. The analysis of real blood data shows that this method can effectively capture the hemoglobin spectral features compared with the traditional preprocessing techniques.

关键词： Near-infrared spectroscopy hemoglobin wavelet packet transform fuzzy shrinkage whale optimization algorithm

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Nonlinear predictive control based on echo state network and whale optimization algorithm 39

Nonlinear predictive control based on echo state network and...

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39th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Wang, Shuo Zhao, Xiyuan Li, Xuyue Zhou, YuTing Zhang, Danfeng Jin, Xin Liaoning Petrochem Univ Coll Informat & Control Engn Fushun Peoples R China

ISBN: (纸本)9798350390780;9798350379228

In response to the challenge of difficulty in modeling and low prediction accuracy for complex nonlinear processes, this paper proposes a nonlinear predictive control method that combines Echo State Networks and whale optimization algorithm. Firstly, an optimized Echo State Network is utilized to construct a predictive model, capturing the nonlinear dynamic characteristics of the system. Subsequently, this predictive model is integrated with model predictive control to achieve predictive control of the system. During the control process, the whale optimization algorithm is employed to solve the objective function in the rolling optimization loop, facilitating optimization adjustments to the control parameters. Finally, the proposed controller is applied to simulate and validate its effectiveness in a continuous stirred-tank reactor process. Simulation results demonstrate the efficacy of this approach.

关键词： Model Predictive Control Neural network Echo State Network Nonlinear system whale optimization algorithm

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