Elephant Herding Optimization

Wireless Sensor Networks (WSNs) consist of millions of sensor nodes that operate cooperatively for attaining the objective of sensing and transmitting information to the base station for necessary decision making processes. In WSNs, the problem of hotspot or energy hole is a major issue that arises when the number of sensor nodes in close proximity of the base station decreases rapidly and results in a network partitioning. This issue of energy holes is feasible in the network only when the difference between the energy consumptions of the sensor nodes are quite large and which has the capability of minimizing the network lifespan. This limitation of WSNs needs to be handled through the potential selection of cluster heads with maximized energy efficiency. In this paper, Memetic Particle Gravitation Optimization Algorithm-based Optimal Cluster Head Selection scheme is proposed for handling the issue of the energy hole in order to sustain energy stability and prolonged network lifetime in WSNs. This MPGOA-OCHS scheme facilitates cluster head head selection by integrating the merits of Centralized Particle swarm Optimization (CPSO) and Gravitational search algorithm (GSA) in order to maintain balance between the rate of intensification and diversification in the cluster head selection process. The simulation results proved that the proposed MPGOA-OCHS scheme is predominant in residual energy by 22.21% and prolonged network lifetime by 16.39%, compared to the baseline schemes.

Swarm intelligence algorithms have been in recent years one of the most used tools for planning the trajectory of a mobile robot. Researchers are applying those algorithms to find the optimal path, which reduces the time required to perform a task by the mobile robot. In this paper, we propose a new method based on the grey wolf optimizer algorithm (GWO) and the improved elephant herding optimization algorithm (IEHO) for planning the optimal trajectory of a mobile robot. The proposed solution consists of developing an IEHO algorithm by improving the basic EHO algorithm and then hybridizing it with the GWO algorithm to take advantage of the exploration and exploitation capabilities of both algorithms. The comparison of the IEHO-GWO hybrid proposed in this work with the GWO, EHO, and cuckoo-search (CS) algorithms via simulation shows its effectiveness in finding an optimal trajectory by avoiding obstacles around the mobile robot.

Swarm intelligence algorithms have been in recent years one of the most used tools for planning the trajectory of a mobile robot. Researchers are applying those algorithms to find the optimal path, which reduces the time required to perform a task by the mobile robot. In this paper, we propose a new method based on the grey wolf optimizer algorithm (GWO) and the improved elephant herding optimization algorithm (IEHO) for planning the optimal trajectory of a mobile robot. The proposed solution consists of developing an IEHO algorithm by improving the basic EHO algorithm and then hybridizing it with the GWO algorithm to take advantage of the exploration and exploitation capabilities of both algorithms. The comparison of the IEHO-GWO hybrid proposed in this work with the GWO, EHO, and cuckoo-search (CS) algorithms via simulation shows its effectiveness in finding an optimal trajectory by avoiding obstacles around the mobile robot.

Swarm intelligence algorithms have been in recent years one of the most used tools for planning the trajectory of a mobile robot. Researchers are applying those algorithms to find the optimal path, which reduces the time required to perform a task by the mobile robot. In this paper, we propose a new method based on the grey wolf optimizer algorithm (GWO) and the improved elephant herding optimization algorithm (IEHO) for planning the optimal trajectory of a mobile robot. The proposed solution consists of developing an IEHO algorithm by improving the basic EHO algorithm and then hybridizing it with the GWO algorithm to take advantage of the exploration and exploitation capabilities of both algorithms. The comparison of the IEHO-GWO hybrid proposed in this work with the GWO, EHO, and cuckoo-search (CS) algorithms via simulation shows its effectiveness in finding an optimal trajectory by avoiding obstacles around the mobile robot.

The internet provides a very vast amount of sources of news and the user has to search for desirable news by spending a lot of time because the user always prefers their related interest, desirable and informative news. The clustering of the news article having a great impact on the preferences of the user. The unsupervised learning techniques such that K-means Clustering and Spectral Clustering are proposed to categorize the news articles by extracting discriminant features that help the user to search and get informative news without wasting time. The BBC news articles dataset is used to perform experiments that consist of 2225 news articles. The TF-IDF feature extraction technique is used with K-means clustering and Spectral clustering to get the most similar clusters to categorize the news articles in respective domains. Those domains are sports, tech, entertainment, politics, and business. The clustering algorithms are evaluated using adjusted rand index, V-measure, homogeneity score, completeness score, and Fowlkes mallows score. The experimental results illustrated that K-means clustering performs better than spectral clustering using the TF-IDF feature extraction approach. But to improve the results the canopy centroid selection is used with the grid search optimization technique to optimize the results of the Kmeans and named its as a K-Means using Grid Search based on Canopy (KMGC-Search). The experimental results shows the proposed approach can be used as a viable method for the categorization of news articles.

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-toaverage 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 DLCbased 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.

In this paper, a dynamic information foraging approach is proposed. Game theory and more precisely normal-form is adapted to model the addressed problem. We tackle the issue of Web information foraging as a game played by a set of self-interested agents that aim at reaching relevant Web pages in a short time. We introduce three kinds of agents along with the strategies followed by each one of them. In fact, the actions performed by the agents would highly depend on the strategy they adopt. We propose a pure strategy, a mixed strategy, and a fully mixed strategy respectively for the three kinds of agents. The agents share a common goal, which is satisfying a specific information need and work together in order to achieve it. The cooperative aspect between the self-interested agents allows to get information from the Web in an effective and efficient way. Furthermore, we discuss the dynamic aspect of our information foraging approach which is ensured thanks to a modular multi-agent system architecture. In order to test our Web information foraging system, we performed extensive experiments on the Citation Network Dataset that includes more than 2.3 million scientific publications. A preprocessing step was conducted with the aim of classifying the publications according to the 2012 ACM ontology, the purpose being to make our approach scalable. We also compared the results of our approach with two information access approaches. The first one deals with Web information retrieval whereas the second concerns medical information foraging. The experimental results show the superiority of our approach in terms of efficiency and results quality.

Swarm intelligence algorithms have been in recent years one of the most used tools for planning the trajectory of a mobile robot. Researchers are applying those algorithms to find the optimal path, which reduces the time required to perform a task by the mobile robot. In this paper, we propose a new method based on the grey wolf optimizer algorithm (GWO) and the improved elephant herding optimization algorithm (IEHO) for planning the optimal trajectory of a mobile robot. The proposed solution consists of developing an IEHO algorithm by improving the basic EHO algorithm and then hybridizing it with the GWO algorithm to take advantage of the exploration and exploitation capabilities of both algorithms. The comparison of the IEHO-GWO hybrid proposed in this work with the GWO, EHO, and cuckoo-search (CS) algorithms via simulation shows its effectiveness in finding an optimal trajectory by avoiding obstacles around the mobile robot.

Swarm Intelligence is an active area of researches and one of the most well-known high-level techniques intended to generat, select or find a heuristic that optimize solutions of optimization problems.Elephant Herding optimization algorithm (EHO) is a metaheuristic swarm based search algorithm, which is used to solve various optimi-zation problems. The algorithm is deducted from the behavior of elephant groups in the wild. Were elephants live in a clan with a leader matriarch, while the male elephants separate from the group when they reach adulthood. This is used in the algorithm in two parts. First, the clan updating mechanism. Second, the separation mechanism.U-Turning Ant colony optimization (U-TACO) is a swarm-based algorithm uses the behavior of real ant in finding the shortest way be-tween its current location and a source of food for solving optimization problems. U-Turning Ant colony Optimization based on making partial tour as an initial state for the basic Ant Colony algo...

In this paper, a dynamic information foraging approach is proposed. Game theory and more precisely normal-form is adapted to model the addressed problem. We tackle the issue of Web information foraging as a game played by a set of self-interested agents that aim at reaching relevant Web pages in a short time. We introduce three kinds of agents along with the strategies followed by each one of them. In fact, the actions performed by the agents would highly depend on the strategy they adopt. We propose a pure strategy, a mixed strategy, and a fully mixed strategy respectively for the three kinds of agents. The agents share a common goal, which is satisfying a specific information need and work together in order to achieve it. The cooperative aspect between the self-interested agents allows to get information from the Web in an effective and efficient way. Furthermore, we discuss the dynamic aspect of our information foraging approach which is ensured thanks to a modular multi-agent system architecture. In order to test our Web information foraging system, we performed extensive experiments on the Citation Network Dataset that includes more than 2.3 million scientific publications. A preprocessing step was conducted with the aim of classifying the publications according to the 2012 ACM ontology, the purpose being to make our approach scalable. We also compared the results of our approach with two information access approaches. The first one deals with Web information retrieval whereas the second concerns medical information foraging. The experimental results show the superiority of our approach in terms of efficiency and results quality.

A multi-agent architecture is proposed for an intelligent system capable of foraging information from the Web in an efficient and rapid way. We distinguish three important phases. The first one is a learning process for localizing the most relevant pages that might interest the user. The second consists in an incremental learning taking into account the changes that undergoes the Web. Finally, the last step simulates the information foraging process once the authorities are learned. Experiments were conducted on MedlinePlus, a public site dedicated for research in the domain of Health. The results are promising either for those related to Web regularities and for surfing time.

In this article, we present a novel information access approach inspired by the information foraging theory (IFT) and elephant herding optimization (EHO). First, we propose a model for information access on social media based on the IFT. We then elaborate an adaptation of the original EHO algorithm to apply it to the information access problem. The combination of the IFT and EHO constitutes a good opportunity to find relevant information on social media. However, when dealing with voluminous data, the performance undergoes a sharp drop. To overcome this issue, we developed an enhanced version of EHO for large scale information access. We introduce new operators to the algorithm, including territories delimitation and clan migration using clustering. To validate our work, we created a dataset of more than 1.4 million tweets, on which we carried out extensive experiments. The outcomes reveal the ability of our approach to find relevant information in an effective and efficient way. They also highlight the advantages of the improved version of EHO over the original algorithm regarding different aspects. Furthermore, we undertook a comparative study with two other metaheuristic-based information foraging approaches, namely ant colony system and particle swarm optimization. Overall, the results are very promising.

The present work proposes a new approach to address the energy-based acoustic localization problem. The proposed approach represents an improved version of evolutionary optimization based on Elephant Herding Optimization (EHO), where two major contributions are introduced. Firstly, instead of random initialization of elephant population, we exploit particularities of the problem at hand to develop an intelligent initialization scheme. More precisely, distance estimates obtained at each reference point are used to determine the regions in which a source is most likely to be located. Secondly, rather than letting elephants to simply wander around in their search for an update of the source location, we base their motion on a local search scheme which is found on a discrete gradient method. Such a methodology significantly accelerates the convergence of the proposed algorithm, and comes at a very low computational cost, since discretization allows us to avoid the actual gradient computations. Our simulation results show that, in terms of localization accuracy, the proposed approach significantly outperforms the standard EHO one for low noise settings and matches the performance of an existing enhanced version of EHO (EEHO). Nonetheless, the proposed scheme achieves this accuracy with significantly less number of function evaluations, which translates to greatly accelerated convergence in comparison with EHO and EEHO. Finally, it is also worth mentioning that the proposed methodology can be extended to any population-based metaheuristic method (it is not only restricted to EHO), which tackles the localization problem indirectly through distance measurements.

In this article, the amalgamation of two well-established meta-heuristic optimization methods is presented to solve the multi-objective distributed generation (DG) allocation problem of distribution systems. To overcome some of the shortcomings of newly developed elephant herding optimization (EHO), an improvement is suggested and then, a prominent feature of particle swarm optimization is introduced to the modified version of EHO. The suggested modifications are validated by solving a single objective DG integration problem where various performance parameters of the proposed hybrid method are compared with their individual standard variants. After validation, the proposed technique is exploited to solve a multi-objective DG allocation problem of distribution systems, aiming to minimize power loss and node voltage deviation while simultaneously maximizing the voltage stability index of three benchmark distribution systems namely, 33-bus, 69-bus and 118-bus. The obtained simulation results are further compared with that of the same available in the existing literature. This comparison reveals that the proposed hybrid approach is promising to solve the multi-objective DG integration problem of distribution systems as compared to many existing methods. 1 Introduction Distributed generation (DG) has provided new dimensions to the electric power industry. Usually, the generation capacity of DGs varies from fewer kilowatts to some megawatts and placed in the vicinity of the load [1]. Nowadays, different types of DG technologies are available and each DG technology has a different profound impact on system performance and economics [2]. The increasing concern of eco-friendly power generation has motivated power system planners to increase the share of renewables. It promotes DG integrations in small to large-scale power distribution networks across the globe. The optimal integration of DGs enhances the reliability, quality and performance of

Fixtures are tools and contributes to increasing manufacturing efficiency and productivity. They are utilized in various manufacturing processes to rest, support and locate the workpiece. Welding fixtures are utilized in fabrication shops. They are specifically utilized to hold and locate the metallic plates and piece parts for permanent joining. They appear simple in construction but to develop an effective welding fixture, deep knowledge of design and manufacturing is required. Welding fixtures can be under static load from the workpiece they are supporting as well as under thermal stresses due to heat liberated by the particular welding process. The purpose of this paper is to provide a model and analyze a compact welding fixture and to illustrate few weld joints for small metal plates. The same model can be utilized efficiently to weld metal plates with variations in dimensions within some range. Software used for modeling of parts is CATIA V5R20 and that for the analysis is ANSYS 16.0

This paper deals with optimal allocation of distributed generation in the electrical distribution system. Due to rapidly increasing energy demand on the distribution network, the system is experiencing disturbances like equipment overloading, voltage sags and swell. In this paper, the thermal and power loss constraints are considered for optimal operation. Elephant Herding Optimization (EHO) technique is applied for optimal placement and sizing of distributed generation on electric distribution network. The conventional optimization technique fails due to its complexity while solving the nonlinear problems. The EHO technique is tested on 5 bus radial distribution system. The intelligent and precise allocation of distributed generation in electric distribution network by using EHO reduces the overloading of the equipment, voltage swell & sag, active power, reactive power and production cost of electricity. Furthermore, the suggested optimization technique is expanded to 24 bus radial distribution and practical Indian system.

This paper presents a novel approach to design a PID controller for load frequency control (LFC) of a hydro-thermal power plant. In the modern power system, the load frequency control of hydro-thermal power plants has widely used in frequency control due to having the fast responses. The PID controller parameters are designed using the elephant herding optimization (EHO) as an optimization technique under the minimization of error. The objective function is considered as minimization of the Integral Squared Error (ISE) for optimal design of PID parameters of the system. The prototype of hydro-thermal power plant with PID controller is simulated in MATLAB/SIMULINK platform. The dynamic responses of load frequency control using proposed controller is compared with the responses of LFC without controller and the other method of frequency control of hydro-thermal power plant in literature. The effectiveness of the proposed methodology is better in terms of settling time, rise time, peak and peak time. Keywords: load frequency control, proportional integral derivative (PID) controller, Thermal power plant, hydro power plant, elephant herding optimization (EHO) Cite

This paper deals with optimum allocation of distributed generation in the electrical distribution system. Due to rapidly increasing energy demand on the distribution network, the system is experiencing disturbances like equipment overloading, voltage sags and swell. In this paper the thermal and power loss constraints are considered for optimal operation. The optimal placement and sizing of distributed generation on electric distribution network by Elephant herding Optimization (EHO) technique. The conventional optimization technique fails due to its complexity while solving the nonlinear problems. The EHO technique is tested on 5 bus radial distribution system. The intelligent and precise allocation of distributed generation in electric distribution network by using EHO reduces the overloading of the equipment, voltage swell & sag, active power, reactive power and production cost of electricity. Furthermore, the suggested optimization technique is expanded to 24 bus radial distribution and practical Indian system.

This work addresses the energy-based source localization problem in wireless sensors networks. Instead of circumventing the maximum likelihood (ML) problem by applying convex relaxations and approximations, we approach it directly by the use of metaheuristics. To the best of our knowledge, this is the first time that metaheuristics are applied to this type of problem. More specifically, an elephant herding optimization (EHO) algorithm is applied. Through extensive simulations, the key parameters of the EHO algorithm are optimized such that they match the energy decay model between two sensor nodes. A detailed analysis of the computational complexity is presented, as well as a performance comparison between the proposed algorithm and existing non-metaheuristic ones. Simulation results show that the new approach significantly outperforms existing solutions in noisy environments, encouraging further improvement and testing of metaheuristic methods.

This paper presents a novel approach to design a PID controller for load frequency control (LFC) of a hydro-thermal power plant. In the modern power system, the load frequency control of hydro-thermal power plants has widely used in frequency control due to having the fast responses. The PID controller parameters are designed using the elephant herding optimization (EHO) as an optimization technique under the minimization of error. The objective function is considered as minimization of the Integral Squared Error (ISE) for optimal design of PID parameters of the system. The prototype of hydro-thermal power plant with PID controller is simulated in MATLAB/SIMULINK platform. The dynamic responses of load frequency control using proposed controller is compared with the responses of LFC without controller and the other method of frequency control of hydro-thermal power plant in literature. The effectiveness of the proposed methodology is better in terms of settling time, rise time, peak and peak time. Keywords: load frequency control, proportional integral derivative (PID) controller, Thermal power plant, hydro power plant, elephant herding optimization (EHO)