Elephant Herding Optimization Research Articles

Individual Updating Strategies-based Elephant Herding Optimization Algorithm for Effective Load Balancing in Cloud Environments

In this manuscript, an Individual Updating Strategies-based Elephant Herding Optimization Algorithm are proposed to facilitate the effective load balancing (LB) process in cloud computing. Primary goal of proposed Individual Updating Strategies-based Elephant Herding Optimization Algorithm focus on issuing the workloads pertaining to network links by the purpose of preventing over-utilization and under-utilization of the resources. Here, NIUS-EHOA-LB-CE is proposed to exploit the merits of traditional Elephant Herd Optimization algorithm to achieve superior results in all dimensions of cloud computing. In this NIUS-EHOA-LB-CE achieves the allocation of Virtual Machines for the incoming tasks of cloud, when the number of currently processing tasks of a specific VM is less than the cumulative number of tasks. Also, it attains potential load balancing process differences with the help of each individual virtual machine’s processing time and the mean processing time (MPT) incurred by complete virtual machine. Efficacy of the proposed technique activates the Cloudsim platform. Experimental results of the proposed method shows lower Mean Response time 11.6%, 18.4%, 20.34%and 28.1%, lower Mean Execution Time 78.2%, 65.4%, 40.32% and 52.6% compared with existing methods, like Improved Artificial Bee Colony utilizing Monarchy Butterfly Optimization approach for Load Balancing in Cloud Environments (IABC-MBOA-LB-CE), An improved Hybrid Fuzzy-Ant Colony Algorithm Applied to Load Balancing in Cloud Computing Environment (FACOA-LB-CE), Hybrid firefly and Improved Multi-Objective Particle Swarm Optimization for energy efficient LB in Cloud environments (FF-IMOPSO-LB-CE) and A hybrid gray wolf optimization and Particle Swarm Optimization algorithm for load balancing in cloud computing environment (GWO-PSO-LB-CE).

A framework for performance enhancement of classifiers in detection of prostate cancer from microarray gene

Prostate cancer is a major world health problem for men. This shows how important early detection and accurate diagnosis are for better treatment and patient outcomes. This study compares different ways to find Prostate Cancer (PCa) and label tumors as normal or abnormal, with the goal of speeding up current work in microarray gene data analysis. The study looks at how well several feature extraction methods work with three feature selection strategies: Harmonic Search (HS), Firefly Algorithm (FA), and Elephant Herding Optimization (EHO). The techniques tested are Expectation Maximization (EM), Nonlinear Regression (NLR), K-means, Principal Component Analysis (PCA), and Discrete Cosine Transform (DCT). Eight classifiers are used for the task of classification. These are Random Forest, Decision Tree, Adaboost, XGBoost, and Support Vector Machine (SVM) with linear, polynomial, and radial basis function kernels. This study looks at how well these classifiers work with and without feature selection methods. It finds that the SVM with radial basis function kernel, using DCT for feature extraction and EHO for feature selection, does the best of all of them, with an accuracy of 94.8 % and an error rate of 5.15 %.

Optimal scheduling and demand response implementation for home energy management

The optimal scheduling of the loads based on dynamic tariffs and implementation of a direct load control (DLC) based demand response program for the domestic consumer is proposed in this work. The load scheduling is carried out using binary particle swarm optimization and a newly prefaced nature-inspired discrete elephant herd optimization technique, and their effectiveness in minimization of cost and the peak-to-average ratio is analyzed. The discrete elephant herd optimization algorithm has acceptable characteristics compared to the conventional algorithms and has determined better exploring properties for multi-objective problems. A prototype hardware model for a home energy management system is developed to demonstrate and analyze the optimal load scheduling and DLC-based demand response program. The controller effectively schedules and implements DLC on consumer devices. The load scheduling optimization helps to improve PAR by a value of 2.504 and results in energy cost savings of ₹ 12.05 on the scheduled day. Implementation of DLC by 15% results in monthly savings of ₹ 204.18. The novelty of the work is the implementation of discrete elephant herd optimization for load scheduling and the development of the prototype hardware model to show effects of both optimal load scheduling and the DLC-based demand response program implementation.

A Novel Symbol Detection Strategy based on Discrete Elephant Herding Optimization Algorithm for MIMO-FBMC/OQAM System

The rapidly growing technological enhancements in wireless communication have pushed the researchers to develop various transmission schemes in recent decades. Multiple-input multiple-output - filter bank multicarrier/offset quadrate amplitude modulation (MIMO-FBMC/OQAM) is one of these schemes with exclusive features. However, unless an efficient symbol detector is employed, we cannot fully benefit from the superior features of MIMO-FBMC/OQAM system as the symbol vectors cannot be detected accurately at the receiver side. An ideal symbol detector is expected to have the capability of recovering the symbol vectors in the most accurate way without causing too much enhancement in the computational complexity. To meet this expectation, we have modified the conventional maximum likelihood (ML) strategy. Instead of using an exhaustive search procedure, which guarantees the highest detection performance but causes an excessive computational load in the ML scheme, an efficient metaheuristic algorithm called discrete elephant herding optimization (DEHO) was employed for optimizing the symbol vectors. With the integration of DEHO to the conventional ML in the aforementioned way, we have not only obtained a considerable reduction in the complexity of ML, but also reached the near-optimal symbol detection performance by outperforming the other symbol detectors considered in this paper.

The IoT resource allocation and scheduling using Elephant Herding Optimization (EHO-RAS) in IoT environment

The IoT resource allocation and scheduling using Elephant Herding Optimization (EHO-RAS) in IoT environment

Distributed Generation and Demand Response Coordination for Optimal Operation of Microgrid Using Modified Elephant Herd Optimization

The optimal operation and coordination of distributed generation (DG) sources and demand response (DR) programs help to minimize the operating cost of microgrids along with other mutual benefits. The model is developed, and the multiobjective problem subjected to the set of constraints is solved by using the modified elephant herd optimization (MEHO) technique in this work. The expected output of the model is the optimal generation scheduling plan of both the dispatchable and non-dispatchable DG and optimized cost for the isolated microgrid operation. The other objectives related to the environmental aspects and reliability are also considered for optimization. DG operation is coordinated with the day ahead DR programs to minimize the costs while satisfying the operation limits of the DG sources and consumer demand. The novelty of the work lies in finding the optimal solution considering the multiobjective for optimal DG operation and analyzing the impact of different types of DR programs on microgrid operation by the MEHO technique. The effectiveness of the proposed model and validity of the algorithm is further tested on a 14 bus microgrid benchmark system. Through simulation and optimization results it is observed that the proposed algorithm has less computational time and better accuracy.

An Energy-Efficient Routing Protocol Based on Elephant Herding Optimization in MANET

Background: A mobile ad hoc network (MANET) is a collection of self-organizing mobile nodes creating an ad hoc network without fixed infrastructure. Routing is a major issue in mobile networks that may reduce network performance due to frequent network topology changes. Routing protocols are so important in dynamic multi-hop networks that many studies have focused on the routing problem in MANETs. Methods: In this research work, a proposed ad hoc on-demand distance vector (AODV) with an elephant herding optimization (EHO) called AODVEHO is used as an optimal path selection method that creates a set of best paths between destination and source. objective: The research's objectives include load balancing, minimizing latency, increasing network speed, and minimizing energy consumption. Results: To validate the performance of proposed AODVEHO, AntHocNet, AODV, and designation sequence distance vector (DSDV). Various performance measures are considered to validate our proposed research work, such as packet delivery ratio (PDR), end-to-end delay (E2ED), and average energy consumption (AEC). method: elephant herding optimization with MANET utilized to achieved a changes in network topology and circumstances, minimizing latency, increasing throughput, preserving energy, and achieving Quality of Service (QoS) standards. Conclusion: Experimental results are indicated that the AODVEHO achieved a higher routing overhead (73 %), PDR (89.87 %), low E2ED rate (95.22 %), AEC (75.60 %), and dead nodes (75%) when compared to other routing schemes. result: SI can assist in locating close to optimum routes by redefining the routing issue as an optimisation challenge. The present reserch work a novel routing approach is developed based on EHO for finding optimal path in MANET environment. Robustness of the developed ADOVEHO is compared with benchmark routing approaches such as AntHocNet[27], AODV[28] and DSDV[29] conclusion: Optimizing energy utilization in MANET is an important study topic that has recently expanded with a high concentration of advantages from swarm intelligence algorithms and their optimization capabilities. The proposed work introduces the AODVEHO energy-efficient routing algorithm, which is based on AODV and EHO. In studies, the NS-3 tool was used to create four routing protocols, including the newly designed AODVEHO in addition to the AODV protocol, AntHocNet, and DSRV. The recommended protocol outperformed the other deployed protocols, according to the results, when it comes to load balancing, as shown by its superior PDR.

An enhanced and efficient approach for feature selection for chronic human disease prediction: A breast cancer study

Computer-aided diagnosis (CAD) systems play a vital role in modern research by effectively minimizing both time and costs. These systems support healthcare professionals like radiologists in their decision-making process by efficiently detecting abnormalities as well as offering accurate and dependable information. These systems heavily depend on the efficient selection of features to accurately categorize high-dimensional biological data. These features can subsequently assist in the diagnosis of related medical conditions. The task of identifying patterns in biomedical data can be quite challenging due to the presence of numerous irrelevant or redundant features. Therefore, it is crucial to propose and then utilize a feature selection (FS) process in order to eliminate these features. The primary goal of FS approaches is to improve the accuracy of classification by eliminating features that are irrelevant or less informative. The FS phase plays a critical role in attaining optimal results in machine learning (ML)-driven CAD systems. The effectiveness of ML models can be significantly enhanced by incorporating efficient features during the training phase. This empirical study presents a methodology for the classification of biomedical data using the FS technique. The proposed approach incorporates three soft computing-based optimization algorithms, namely Teaching Learning-Based Optimization (TLBO), Elephant Herding Optimization (EHO), and a proposed hybrid algorithm of these two. These algorithms were previously employed; however, their effectiveness in addressing FS issues in predicting human diseases has not been investigated. The following evaluation focuses on the categorization of benign and malignant tumours using the publicly available Wisconsin Diagnostic Breast Cancer (WDBC) benchmark dataset. The five-fold cross-validation technique is employed to mitigate the risk of over-fitting. The evaluation of the proposed approach's proficiency is determined based on several metrics, including sensitivity, specificity, precision, accuracy, area under the receiver-operating characteristic curve (AUC), and F1-score. The best value of accuracy computed through the suggested approach is 97.96%. The proposed clinical decision support system demonstrates a highly favourable classification performance outcome, making it a valuable tool for medical practitioners to utilize as a secondary opinion and reducing the overburden of expert medical practitioners.

Optimized fractional order PID controller with sensorless speed estimation for torque control in induction motor

With rising concern on environmental protection, the exploitation of EV has been widely focused. The IM is said to be the best in comparing varied electric motors for EV applications due to its lower price and robustness. In addition, sensor-less controlling schemes like DTC and FOC are found to be appropriate for crucial appliances owing to their energy savings, improved efficiency, and reliability. The “Optimized Fractional Order Proportional Integral Controller (FoPID)” with a sensor-less speed estimate mechanism is proposed in this work. Additionally, the parameters of FoPID are adjusted using a brand-new hybrid method called the Wolf based Elephant Herding method (W-EHA), which combines the ideas of Grey wolf optimization (GWO) model and Elephant herding optimization (EHO). The superiority of the recommended technique is finally shown in terms of controller performance and error. The RMSE obtained by the developed model at speed = 100 is 3.344, which is 53.89 %, 24.22 %, 1.09 %, and 38.96 % superior to existing approaches like P-controller, MFI-DA + FoPID, EHO-FoPID, and GWO-FoPID respectively.

An Enhanced Energy Efficiency Routing for WSN based on Elephant Herding and Swarm Optimization Approaches

Energy utilization and inadequacy of sensor nodes are considered major drawbacks in wireless sensor networks (WSNs). This is because the sensor nodes use the battery for recharging energy. To overcome this issue WSN utilized a clustering-routing algorithm. This protocol divides the adjacent sensor nodes into separate clusters to choose a cluster head. Thus, the cluster head gathers information from all clusters and transmits it to the base station. In this article, the proposed method used cluster-based routing protocols to enhance energy efficiency and network lifetime. Moreover, this paper follows three stages to maximize energy efficiency. Initially, the clustering process is performed using dolphin swarm optimization (DSO), where a group of clusters is formed. Then the second stage is composed of cluster head selection among the group of clusters by elephant herding optimization (EHO) strategy. Finally, the collected data are necessary to forward to the base station for transferring the information. A specified path (routing) is selected by chicken swarm optimization (CSO). By using these algorithms, the network nodes support the balance of energy utilization. Experimental analysis proves when evaluated with existing methods the proposed technique has improved energy efficiency with an increase in network lifetime.

Optimum exploitation of multiple energy system using IGDT approach and risk aversion strategy and considering compressed air storage with solar energy

The concept of an energy hub within a microgrid has garnered significant attention from investors due to its potential for efficiently managing diverse energy sources and accurately forecasting energy prices. This study presents a model for an energy hub that facilitates the optimal operation of a microgrid with multiple energy carrier infrastructures under future-day scenarios. The primary objective of the optimization process is to minimize operating costs, environmental impacts, and technical limitations. The proposed energy hub model integrates both distributable generation, such as a Combined Cooling, Heating, and Power (CCHP) unit, and non-distributable generation sources, including wind turbines and photovoltaic systems. Additionally, it incorporates various energy storage components, namely Ice Storage Conditioner (ISC), Thermal Energy Storage (TES) systems, and Electric Vehicles (EVs). Furthermore, the study investigates the impact of a novel energy storage system called Solar Powered Compressed Air Energy Storage (SPCAES) on the operational and environmental performance of the energy hub. To address uncertainties stemming from predicted and actual variables, a two-level stochastic optimization problem is formulated based on the Information Gap Decision Theory (IGDT) with a Risk Aversion (RA) strategy. The overarching aim is to mitigate the risks associated with the information gap confronted by microgrid operators. Subsequently, the two-level stochastic optimization problem is transformed into a single-level problem using the Karush-Kuhn-Tucker method. The proposed framework employs the RA-IGDT method and utilizes the Elephant Herd Optimization (EHO) algorithm to effectively solve the optimization problem. By applying this proposed framework to a typical energy hub, this research demonstrates the effectiveness of energy storage systems in reducing operating costs and greenhouse gas emissions during daily energy management. The study highlights the potential of energy hubs and storage systems for cost-effective and eco-friendly microgrid energy management. Scenario 4, featuring the proposed energy hub with SPCAES and ISC storage, demonstrates a 15 % reduction in operating costs and a notable 20 % increase in peak-hour electrical power for sale, underscoring its economic and operational benefits.

Performance enhancement of efficient clustering and routing protocol for wireless sensor networks using improved elephant herd optimization algorithm

Performance enhancement of efficient clustering and routing protocol for wireless sensor networks using improved elephant herd optimization algorithm

Heart Disease Prediction Using Convolutional Neural Network with Elephant Herding Optimization

Heart Disease Prediction Using Convolutional Neural Network with Elephant Herding Optimization

Kernel-Induced Matriarch Path Tracking Elephant Herding Optimization Technique for Identification and Classification of Cancer Types Using Support Vector Machines

Abnormal cells in the human body that keep on mutating are termed to be cancer in medical terms. There are multiple types of cancer identified in human beings. It is very much essential to identify and classify the type of cancer in its earlier stage. This objective can be satisfied by artificial intelligence which has a subfield of machine learning to create a generalized model that could identify and classify cancer with increased performance. To perform the identification and classification of various cancer types, in this paper, two techniques are adopted. The optimized feature set computation was done using the Kernel-Induced Matriarch path tracking Elephant Herding Optimization (KIM-EHO) and the classification for the given samples was done using the Support Vector Machines (SVM). The proposed techniques are implemented with the benchmark datasets and the results proved that the proposed methodologies outperformed the existing methods in terms of accuracy, specificity, sensitivity and time complexity.

A Hybrid Optimization-Based Artificial Neural Network Model for Wireless Power Transfer in Electric Vehicles

Electric Vehicles (EVs) are powered by a battery mounted in the vehicle, which powers the motor and drives the wheels. Most commercial EVs can be charged by plugging them into a charging station. Such conductive recharging has various drawbacks, including physical plugging of the cable, safety concerns, and charging time. Manually charging EVs might be dangerous due to the chance of an electric spark or disaster. Advances in Wireless Power Transfer (WPT) demonstrate the capacity to transfer significant amounts of electricity over short and medium-range distances. The ultimate purpose of this paper is to improve the efficacy of electric car wireless charging systems. Here, a hybrid optimization-based Artificial Neural Network (ANN) model is applied to improve the efficacy of the WPT model in EVs. To optimize the weights of the ANN classifier, a hybrid approach termed as Grasshopper-Assisted Elephant Herd Optimization (GA-EHO) method is proposed. The GA-EHO is derived through the hybridization of Elephant Herd Optimization (EHO) Algorithm and the Grasshopper Optimization Algorithm (GOA) techniques. Finally, the experimental study reveals that at 70% learning rate, the proposed ANN system achieves a minimal MSE value of 0.0528, which is lower than other current classifiers, such as SVM, LSTM, and CNN.

Pilot Tones Design for Channel Estimation Using Elephant Herding Optimization Algorithm in Massive MIMO Systems

Pilot Tones Design for Channel Estimation Using Elephant Herding Optimization Algorithm in Massive MIMO Systems

Association rule hiding using enhanced elephant herding optimization algorithm

Association rule hiding is an efficient solution that helps organizations to avoid the risk caused by sensitive knowledge leakage when sharing data in their collaborations. Cuckoo Optimization Algorithm (COA) sanitizes the transaction database but this method has limitation due to its slow convergence and exploitation capabilities. Hence in this paper, Enhanced Elephant Herding Optimization Algorithm for Association Rule Hiding (EEHOA4ARH) is proposed for association rule hiding. In EEHOA, two core functions such as clan updating operator and separating operator are used for association rule hiding that also realizes the fast convergence and exploitation capabilities. Moreover, the searching strategy in COA4ARH for the selection of best solution is highly time consuming. To reduce the time consumption for the selection of best solution, a Crowding Distance (CD) concept is combined with EEHOA4ARH. By continuously updating the best elephant and replacing the worst elephant in the population, EEHOA4ARH-CD sanitizes the transaction database effectively. Thus the proposed EEHOA4ARH achieves the less computation time, fast convergence and better exploitation capabilities by using crowding distance. The experimental results prove the effectiveness of the proposed EEHOA4ARH–CD method in terms of hiding failure, lost rule and execution time with 44.66 s.

Predicting clay compressibility for foundation design with high reliability and safety: A geotechnical engineering perspective using artificial neural network and five metaheuristic algorithms

This study aims at predicting clay compressibility (clay compressibility) for foundation design with unprecedented reliability and safety. To achieve this goal, we set forth several scientific objectives to enhance the accuracy of clay compressibility predictions and ensure the robustness of our models. Firstly, we explored an updated dataset comprising diverse clay types, focusing on the intricate relationship between clay compressibility and essential soil properties. By incorporating particle size distribution, the ratio between liquid limit (wLL) and plasticity index (PI), and the ratio between void ratio (eLL) and wLL, our objective was to gain a comprehensive understanding of clay behavior, facilitating more accurate predictions. Secondly, we employed Artificial Neural Networks (ANNs) as our primary modeling tool. ANNs, with their capacity to discern complex patterns within data, formed the foundation of our predictive model. Our scientific objective was to harness the power of ANNs to capture subtle relationships among input variables, thereby ensuring a robust foundation for clay compressibility predictions. To further refine our predictions, our third objective was to optimize the ANN model. We utilized five distinct metaheuristic algorithms—Bird Swarm Algorithm (BSA), Dragonfly Optimization (DO), Elephant Herding Optimization (EHO), Jaya Algorithm (JA), and Whale Optimization Algorithm (WOA). Our aim was to fine-tune the ANN model's parameters using these algorithms, capitalizing on their strengths to achieve superior predictive performance. Additionally, our study aimed to pioneer the development and evaluation of hybrid models for forecasting clay compressibility. By integrating the ANN model with the metaheuristic algorithms (BSA-ANN, DO-ANN, EHO-ANN, JA-ANN, and WOA-ANN), our objective was to create hybrid frameworks that synergistically combined the strengths of both approaches. We rigorously evaluated these hybrid models, ensuring their efficacy and reliability in practical engineering applications. Crucially, we validated our models through practical engineering scenarios. For this purpose, we prepared 30 new soil samples mirroring real-world conditions. Our objective was to apply the developed models to these samples, affirming their effectiveness and reliability in actual foundation design applications. In pursuit of identifying the most accurate and reliable predictive model, our final objective focused on the BSA-ANN model. Remarkably, this model demonstrated an exceptional accuracy of 99.8%. Our objective was to establish the BSA-ANN model as the preferred choice for practical engineering applications, ensuring high reliability and safety in foundation design.

Novel Amalgamation of Bat and Elephant Herd Optimization Algorithms for Generation Mix in Renewable Active Distribution Networks

In this article, a hybrid approach is proposed that amalgamates two prominent nature-inspired, swarm-based, meta-heuristic techniques. The amalgamation is done to solve the mix generation, complex real-life optimization problems for renewable active distribution network (ADN). Some observed shortcomings of the bat algorithm (BA) are overcome by an effective introduction of elephant herding optimization (EHO) without altering its internal mechanism. The new transformations are validated by solving a real-life optimization problem of distributed generations for power loss minimization in power distribution networks. The performance of the proposed hybrid method is found to be promising when compared with some of the well-known optimizations in the literature. The proposed amalgamation has strengthened the exploration and exploitation potential of the new algorithm in a way that, their precise balance seeks the global optima. After validations, this technique is deployed to solve a multiobjective problem of optimal generation mix for renewable ADN for various test cases. Key objectives of the designed model of mix generation in ADN include annual energy losses, bus voltage profiles, and net demand variants of the networks over multiple load levels. The optimization problems model is implemented and solved for two test systems that include a benchmark 33 bus and a real-life 108 bus distribution network. The results comparison reveals that the proposed method significantly improved the system performances in terms of reduced annual energy loss by 55.95% and 44.48%, improved bus voltage profiles by 7.94% and 11.20%, and reduced net power demand fluctuations by 52.00% and 37.60% for 33 bus and 108 bus distribution network, respectively. The suggested optimization model outperforms the current existing approaches and has promising exploration and exploitation capabilities to address optimization problems in engineering.

Metaheuristic-Assisted Contextual Post-Filtering Method for Event Recommendation System

In today’s world, the web is a prominent communication channel. However, the variety of strategies available on event-based social networks (EBSNs) also makes it difficult for users to choose the events that are most relevant to their interests. In EBSNs, searching for events that better fit a user’s preferences are necessary, complex, and time consuming due to a large number of events available. Toward this end, a community-contributed data event recommender framework assists consumers in filtering daunting information and providing appropriate feedback, making EBSNs more appealing to them. A novel customized event recommendation system that uses the “multi-criteria decision-making (MCDM) approach” to rank the events is introduced in this research work. The calculation of categorical, geographical, temporal, and social factors is carried out in the proposed model, and the recommendation list is ordered using a contextual post-filtering system that includes Weight and Filter. To align the recommendation list, a new probabilistic weight model is added. To be more constructive, this model incorporates metaheuristic reasoning, which will fine-tune the probabilistic threshold value using a new hybrid algorithm. The proposed hybrid model is referred to as Beetle Swarm Hybridized Elephant Herding Algorithm (BSH-EHA), which combines the algorithms like Elephant Herding Optimization (EHO) and Beetle Swarm Optimization (BSO) Algorithm. Finally, the top recommendations will be given to the users.