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horse herd optimization algorithm for economic dispatch problems

ENGINEERING optimization 2023年第5期55卷 806-822页

作者： Basu, Subhamay Kumar, Sajjan Basu, Mousumi Maulana Abul Kalam Azad Univ Technol Dept Elect & Commun Engn Techno Main Salt Lake Kolkata India Aditya Engn Coll A Dept Elect & Elect Engn Surampalem India Jadavpur Univ Dept Power Engn Kolkata India

This article applies the horse herd optimization (HHO) algorithm to convoluted economic dispatch (ED) problems. HHO mimics the social behaviour of horses of different ages using six significant traits: grazing, hierarchy, sociability, imitation, defence mechanism and roam. The efficacy of the HHO method is demonstrated on five different ED problems, namely, valve-point effects, prohibited feasible area, ramp rate limits and multiple fuels. The simulated outcomes of the recommended method are comparable to those obtained by established artificial intelligence methods. Comparative and statistical analyses demonstrate that the proposed HHO algorithm performs well and can produce superior results to some other well-known and established algorithms, namely, differential evolution (DE), success-history based adaptive differential evolution with linear population size reduction (L-SHADE) and covariance matrix adaptation-evolution strategy (CMA-ES).

关键词： horse herd optimization algorithm economic dispatch disallowed feasible area ramp rate limits valve-point effect

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A novel approach for spam detection using horse herd optimization algorithm

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NEURAL COMPUTING & APPLICATIONS 2022年第15期34卷 13091-13105页

作者： Hosseinalipour, Ali Ghanbarzadeh, Reza Islamic Azad Univ Dept Comp Engn Hens Branch Heris Iran Southern Cross Univ Fac Sci & Engn Gold Coast Australia

Communication via email has expanded dramatically in recent decades due to its cost-effectiveness, convenience, speed, and utility for a variety of contexts, including social, scientific, cultural, political, authentication, and advertising applications. Spam is an email sent to a large number of individuals or organizations without the recipient's desire or request. It is increasingly becoming a harmful part of email traffic and can negatively affect the usability of email systems. Such emails consume network bandwidth as well as storage space, causing email systems to slow down, wasting time and effort scanning and eliminating enormous amounts of useless information. Spam is also used for distributing offensive and harmful content on the Internet. The objective of the current study was to develop a new method for email spam detection with high accuracy and a low error rate. There are several methods to recognize, detect, filter, categorize, and delete spam emails, and almost the majority of the proposed methods have some extent of error rate. None of the spam detection techniques, despite the optimizations performed, have been effective alone. A step in text mining and message classification is feature selection, and one of the best approaches for feature selection is the use of metaheuristic algorithms. This article introduces a new method for detecting spam using the horse herd metaheuristic optimization algorithm (HOA). First, the continuous HOA was transformed into a discrete algorithm. The inputs of the resulting algorithm then became opposition-based and then converted to multiobjective. Finally, it was used for spam detection, which is a discrete and multiobjective problem. The evaluation results indicate that the proposed method performs better compared to other methods such as K-nearest neighbours-grey wolf optimisation, K-nearest neighbours, multilayer perceptron, support vector machine, and Naive Bayesian. The results show that the new multiobjective op

关键词： Spam detection Metaheuristic Feature selection horse herd optimization algorithm

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Quantitative structure-property relationship modelling for predicting retention indices of essential oils based on an improved horse herd optimization algorithm

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SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2023年第10期34卷 831-846页

作者： Alharthi, A. M. Kadir, D. H. Al-Fakih, A. M. Algamal, Z. Y. Al-Thanoon, N. A. Qasim, M. K. Taif Univ Turabah Univ Coll Dept Math Taif Saudi Arabia Salahaddin Univ Erbil Coll Adm & Econ Dept Stat Erbil Iraq Cihan Univ Erbil Dept Business Adm Erbil Iraq Univ Teknol Malaysia Fac Sci Dept Chem Skudai Johor Malaysia Sanaa Univ Fac Sci Dept Chem Sanaa Yemen Univ Mosul Dept Stat & Informat Mosul Iraq Univ Mosul Dept Operat Res & Intelligent Tech Mosul Iraq Univ Mosul Dept Gen Sci Mosul Iraq

The horse herd optimization algorithm (HOA), one of the more contemporary metaheuristic algorithms, has demonstrated superior performance in a number of challenging optimization tasks. In the present work, the descriptor selection issue is resolved by classifying different essential oil retention indices using the binary form, BHOA. Based on internal and external prediction criteria, Z-shape transfer functions (ZTF) were tested to verify their efficiency in improving BHOA performance in QSPR modelling for predicting retention indices of essential oils. The evaluation criteria involved the mean-squared error of the training and testing datasets (MSE), and leave-one-out internal and external validation (Q2). The degree of convergence of the proposed Z-shaped transfer functions was compared. In addition, K-fold cross validation with k = 5 was applied. The results show that ZTF, especially ZTF1, greatly improves the performance of the original BHOA. Comparatively speaking, ZTF, especially ZTF1, exhibits the fastest convergence behaviour of the binary algorithms. It chooses the fewest descriptors and requires the fewest iterations to achieve excellent prediction performance.

关键词： QSPR modelling horse herd optimization algorithm transfer function descriptor selection retention indices

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Feature selection strategy based on hybrid horse herd optimization algorithm and perturbation theory: an mRMI approach

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ANNALS OF OPERATIONS RESEARCH 2024年 1-28页

作者： Emami, Nasibeh Kuchaki Rafsanjani, Marjan Shahid Bahonar Univ Kerman Fac Math & Comp Dept Comp Sci Kerman Iran

In the big-data era, conveying information related to data can sometimes lead to redundancy and irrelevance. This large volume of information not only lacks benefits for optimal decision-making but also increases the costs associated with data collection, storage, and processing. In this view, dimension reduction can be an effective solution. Therefore, we propose a new minimum Redundancy and Maximum Interaction (mRMI) feature selection method. The proposed method controls redundancy through perturbation theory and clustering. It then selects representative features from each cluster using horse herd optimization algorithm (HOA) to ensure that the selected subset of features interacts effectively for classification. We call it PHOAFS (perturbation theory HOA feature selection), which is validated on microarray datasets. The effectiveness of PHOAFS is investigated by comparing it with both conventional and new feature selection algorithms found in the literature. The experimental results show that the proposed method (PHOAFS) can achieve a smaller number of features while maintaining or exceeding the accuracy of other algorithms.

关键词： Feature selection Redundancy Clustering Perturbation theory Interaction horse herd optimization algorithm

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Fuel constrained combined heat and power dynamic dispatch using horse herd optimization algorithm

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ENERGY 2022年第0期246卷 123396-123396页

作者： Basu, M. Jadavpur Univ Dept Power Engn Kolkata 700098 India

Due to gradually reduction of fossil fuel, the cost-effective use of available fuel for electric power gen-eration has turn out to be a very significant concern of electric power utilities. This work recommends horse herd optimization algorithm (HOA) to solve fuel constrained combined heat and power dynamic economic dispatch with demand side management integrating wind turbine generators, solar PV plants and pumped storage hydro plant for three different scenarios. The effectiveness of the recommended method is divulged on an archetypal system. Numerical results of archetypal system for three different scenarios are compared with those obtained from self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, fast convergence evolutionary programming, differential evolution and real-coded genetic algorithm. It has been seen from numerical results, that the total cost with fuel constraints is more than the cost without fuel constraints. It has been also observed from the comparison that the recommended HOA has the capability to confer with superior-quality solution.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Fuel constraints Thermal generating units Co-generation units Demand side management Pumped storage hydro plant horse herd optimization algorithm

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Intelligent electric vehicle charging optimization and horse herd-inspired power generation for enhanced energy management

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ENERGY 2024年 291卷

作者： Lin, Guanwu Qi, Bo Ma, Changxi Rostam, Fateh Lanzhou Jiaotong Univ Inst Railway Technol Lanzhou 730070 Gansu Peoples R China Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Gansu Peoples R China Wind Power Inst Lab Wind Energy Advancements Tbilisi Georgia

This article focuses on optimizing electric vehicle charging in distribution networks, emphasizing technical and economic considerations. Unlike traditional methods, the proposed intelligent approach tailors each EV's charging based on specific daily trip energy requirements. Vehicle owners provide trip data to the charge management system, enabling precise charging calculations considering factors such as energy tariffs, distribution network limits, and charging levels. The paper introduces the horse herd optimization algorithm, inspired by horse herd behavior, offering advantages like reduced computational time and improved convergence in maximizing power generation, especially under shading conditions. The comparative analysis of smart EV charging under normal and fast conditions, considering various constraints and load response programs, demonstrates the proposed method's effectiveness. Numerical results reveal a 46.02 % average load reduction and a 20.53 % peak load decrease with a Load Response Program. Charging costs are optimized, with Case 2 exhibiting a 2.61 % cost reduction compared to Case 3. The study delves into charging frequencies, discharge frequencies, total unsupplied energy, and unloaded energy for each case, providing crucial insights into algorithmic performance. The horse herd optimization algorithm-based approach proves superior, offering a promising solution for efficient and cost-effective electric vehicle charging in distribution networks. Furthermore, graphical representations illustrate the algorithm's impact on charging power, energy allocation, power passing through distribution posts, and megavolt-ampere flow through network lines. These numerical and graphical analyses provide a comprehensive understanding of the horse herd optimization algorithm capabilities, emphasizing its potential to optimize power distribution, reduce costs, and enhance the resilience of residential distribution networks.

关键词： Distribution network efficiency Electric vehicle charging optimization horse herd optimization algorithm Intelligent charging management Renewable energy generation

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A secure and efficient authentication and multimedia data sharing approach in IoT-healthcare

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IMAGING SCIENCE JOURNAL 2023年第3期71卷 277-298页

作者： Yempally, Sangeetha Singh, Sanjay Kumar Sarveshwaran, Velliangiri Amity Univ Rajasthan Dept Elect & Commun Engn Jaipur India SRM Inst Sci & Technol Dept Computat Intelligence Chennai India

Currently, healthcare services are encountering challenges, particularly in developing countries wherein remote areas encounter a lack of highly developed hospitals and doctors. IoT devices produce enormous security-sensitive data;therefore, device security is considered an important concept. The main aim of this work is to formulate a secure key generation process in the data-sharing approach by exploiting the Rider horse herd optimization algorithm (RHHO). Here, eight phases, like the initialization phase, registration phase, key generation phase, login phase, data protection phase, authentication phase, verification phase, and data decryption phase are exploited for secure and efficient authentication and multimedia data sharing. The proposed RHHO model is the integration of the Rider optimization algorithm (ROA) and horse herd optimization algorithm (HOA). The proposed RHHO model achieved enhanced performance with a computation cost of 0.235, an accuracy of 0.935and memory usage of 2.425 MB.

关键词： Internet of things blockchain healthcare authentication multimedia data sharing Rider optimization algorithm horse herd optimization algorithm Rider horse herd optimization algorithm

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Using hybrid machine learning for predicting the California bearing ratio based on adaptive neuro-fuzzy inference system

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第2期7卷 1237-1252页

作者： Wang, Hui Xinxiang Vocat & Tech Coll Sch Transportat Xinxiang 453000 Henan Peoples R China

In geotechnical engineering, the California bearing ratio (CBR) is a vital parameter for assessing the soil's strength. To achieve accurate predictions of the CBR, several modeling techniques have been developed, among which is the adaptive neuro-fuzzy inference system (ANFIS). The present study formulated an ANFIS-based predictive model for estimating CBR values, employing pertinent input parameters, including moisture content, compaction features, and soil properties. The present study used a dataset of 109 soil samples to train and test a model and evaluate its efficacy in contrast to conventional regression-oriented models employed for the same purpose. In addition, three optimization algorithms have been coupled with the ANFIS in a hybrid framework: Northern Goshawk optimization, Jellyfish Search Optimizer, and horse herd optimization algorithm. The ANFIS model demonstrated exceptional precision, as evidenced by its R2 value of 0.9981. The present study showed that the ANHH model, a hybrid form of the ANFIS model, exhibited the most superior performance compared to all other hybrid models analyzed. The ANHH model indicates significant potential as a dependable instrument for anticipating CBR values in geotechnical engineering applications, delivering more precise and productive outcomes than conventional methods.

关键词： California bearing ratio Adaptive neuro-fuzzy inference system Northern Goshawk optimization Jellyfish Search Optimizer horse herd optimization algorithm

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E-waste management using hybrid optimization-enabled deep learning in IoT-cloud platform

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ADVANCES IN ENGINEERING SOFTWARE 2023年 176卷

作者： Ramya, Puppala Ramya, V Rao, Babu M. Annamalai Univ Fac Engn & Technol Dept CSE Annamalainagar Tamil Nadu India Gudlavalleru Engn Coll Dept CSE Gudlavalleru India

Electronic waste (E-waste) is generated at a quick pace due to technological expansion and thereby reducing the obsolescence age of electrical and electronic equipment (EEE). This ever-increasing e-waste is not only dangerous for the environment but also to the health of human beings. A robust and effective E-waste classification approach is devised in this paper, named Fractional horse herd Gas optimization-based Shepherd Convolutional Neural Network (FrHHGO-based ShCNN) for classifying E-wastes in the Internet of Things (IoT)-cloud platform. Here, the E-waste images are collected by the IoT nodes and then stored in the cloud data storage which then performs the process of routing using the proposed FrHHGO algorithm. Moreover, effective features are extracted and then augmented to perform the process of E-waste classification. The E-waste classification is performed using FrHHGO, which is the combination of Fractional Henry Gas optimization (FHGO), and horse herd optimization algorithm (HOA). The developed method outperformed various existing approaches with mini-mum energy and delay of 0.301 J, and 0.666 s, maximum accuracy, sensitivity, and specificity of 0.950, 0.934, and 0.967, respectively. Thus, the developed E-waste classification system enhances the social, environmental, and economic sustainability using FrHHGO-based ShCNN in emerging economies.

关键词： Electronic waste Fractional calculus horse herd optimization algorithm Henry gas solubility optimization Shepherd convolutional neural network

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Enhanced breast cancer diagnosis through integration of computer vision with fusion based joint transfer learning using multi modality medical images

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SCIENTIFIC REPORTS 2024年第1期14卷 1-23页

作者： Iniyan, S. Raja, M. Senthil Poonguzhali, R. Vikram, A. Ramesh, Janjhyam Venkata Naga Mohanty, Sachi Nandan Dudekula, Khasim Vali SRM Inst Sci & Technol Sch Comp Dept Comp Technol Chennai 603203 India Periyar Maniammai Inst Sci & Technol Dept Comp Sci & Engn Thanjavur 613403 India Aditya Univ Dept Comp Sci & Engn Surampalem 533437 Andhra Pradesh India Graph Era Hill Univ Dept Comp Sci & Engn Dehra Dun India Graph Era Univ Dept Comp Sci & Engn Dehra Dun Uttarakhand India VIT AP Univ Sch Comp Sci & Engn SCOPE Amravati Andhra Pradesh India

Breast cancer (BC) is a type of cancer which progresses and spreads from breast tissues and gradually exceeds the entire body;this kind of cancer originates in both sexes. Prompt recognition of this disorder is most significant in this phase, and it is measured by providing patients with the essential treatment so their efficient lifetime can be protected. Scientists and researchers in numerous studies have initiated techniques to identify tumours in early phases. Still, misperception in classifying skeptical lesions can be due to poor image excellence and dissimilar breast density. BC is a primary health concern, requiring constant initial detection and improvement in analysis. BC analysis has made major progress recently with combining multi-modal image modalities. These studies deliver an overview of the segmentation, classification, or grading of numerous cancer types, including BC, by employing conventional machine learning (ML) models over hand-engineered features. Therefore, this study uses multi-modality medical imaging to propose a Computer Vision with Fusion Joint Transfer Learning for Breast Cancer Diagnosis (CVFBJTL-BCD) technique. The presented CVFBJTL-BCD technique utilizes feature fusion and DL models to effectively detect and identify BC diagnoses. The CVFBJTL-BCD technique primarily employs the Gabor filtering (GF) technique for noise removal. Next, the CVFBJTL-BCD technique uses a fusion-based joint transfer learning (TL) process comprising three models, namely DenseNet201, InceptionV3, and MobileNetV2. The stacked autoencoders (SAE) model is implemented to classify BC diagnosis. Finally, the horse herd optimization algorithm (HHOA) model is utilized to select parameters involved in the SAE method optimally. To demonstrate the improved results of the CVFBJTL-BCD methodology, a comprehensive series of experimentations are performed on two benchmark datasets. The comparative analysis of the CVFBJTL-BCD technique portrayed a superior accuracy value of

关键词： Transfer learning Breast cancer Computer vision horse herd optimization algorithm Image preprocessing

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