Wireless sensor network (WSN) is an integration of sensing, communicating, computing in a board range environment. Efficient energy consumption becomes the most challenging task for sensor nodes. The ...clustering and routing techniques are promising methods to resolve the issue and extend the network’s lifespan. The clustering technique is defined as grouping data into classes, every cluster sharing a high degree of similarity in between them, and each cluster being dissimilar with others. This technique is the best data processing model for WSN, and it controls the redundant data inside the network. The nomination of the appropriate cluster head is a major factor in the clustering technique. The object of this proposed paper is to equipoise the energy of the clustering nodes and route the data from cluster head to sink. We propose an improved particle swarm optimization gravitational search algorithm for clustering and routing in WSNs. Here clustering algorithm makes equal energy in the entire network by the uniform distribution of cluster head, routing algorithm decides the ideal routing path to convey data packet between cluster head and sink. The proposed paper integrates the exploration capacity of GSA and the exploitation capability of PSO. Detailed simulation performs using MATLAB based simulator in terms of residual energy, network lifespan, and convergence rate. In comparing our proposed algorithm to other existing algorithms, it outperforms significantly.
The potential availability of renewable energy sources is unquestionable and the government is setting steep targets for renewable energy usage. Renewable‐based DGs, reduce dependence on fossil ...fuels, mitigate global climate change, ensure energy security, and reduce emissions of CO2 and other greenhouse gases. This study addresses microgrid system analysis with hybrid energy sources and reconfiguration simultaneously for efficient operation of the system. Microgrid zones are formulated categorically with the existing distribution system. In this study, wind, solar and small hydro‐based DGs are considered. Uncertainties of renewable power generation and load are also taken care in the optimization problem. A multi‐objective optimisation method proposed in this paper for optimal integration of renewable‐based DGs and reconfiguration of the network to minimise power loss and maximise annual cost savings. Optimal location and sizes of DG units are determined using gravitational search algorithm and general algebraic modelling system respectively. Optimal reconfiguration of the microgrid system is obtained using genetic algorithm. Simulation results are obtained for the IEEE 33‐bus system and compared with existing methods as available in the literature. Furthermore, this study has been carried out with a 24‐hr time‐varying distribution system. The simulation results show the efficiency and accuracy of the proposed technique.
Cloud computing has emerged as a novel technology that offers convenient and cost-effective access to a scalable pool of computing resources over the internet. Task scheduling plays a crucial role in ...optimizing the functionality of cloud services. However, inefficient scheduling practices can result in resource wastage or a decline in service quality due to under- or overloaded resources. To address this challenge, this research paper introduces a hybrid approach that combines gravitational search and genetic algorithms to tackle the task scheduling problem in cloud computing environments. The proposed method leverages the strengths of both gravitational search and genetic algorithms to achieve enhanced scheduling performance. By integrating the unique search capabilities of the gravitational search algorithm with the optimization and adaptation capabilities of the genetic algorithm, a more effective and efficient solution is achieved. The experimental results validate the superiority of the proposed method over existing approaches in terms of total cost optimization. The experimental evaluation demonstrates that the hybrid method outperforms previous scheduling methods in achieving optimal resource allocation and minimizing costs. The improved performance is attributed to the combined strengths of the gravitational search and genetic algorithms in effectively exploring and exploiting the solution space. These findings underscore the potential of the proposed hybrid method as a valuable tool for addressing the task scheduling problem in cloud computing, ultimately leading to improved resource utilization and enhanced service quality.
This study proposes an adaptive gravitational search algorithm (AGSA) which carries out adaptation of depreciation law of the gravitational constant and of a parameter in the weighted sum of all ...forces exerted from the other agents to the iteration index. The adaptation is ensured by a simple single input–two output (SITO) fuzzy block in the algorithm's structure. SITO fuzzy block operates in the iteration domain, the iteration index is the input variable and the gravitational constant and the parameter in the weighted sum of all forces are the output variables. AGSA's convergence is guaranteed by a theorem derived from Popov's hyperstability analysis results. AGSA is embedded in an original design and tuning method for Takagi-Sugeno proportional-integral fuzzy controllers (T-S PI-FCs) dedicated to servo systems modelled by second-order models with an integral component and variable parameters. AGSA solves a minimisation-type optimisation problem based on an objective function which depends on the sensitivity function with respect to process gain variations, therefore a reduced process gain sensitivity is offered. AGSA is validated by a case study that optimally tunes a T-S PI-FC for position control of a laboratory servo system.Representative experimental results are presented.
This paper proposes combining the augmented Lagrangian method (ALM) with evolutionary heuristic methods, as well as quasi-Newton optimization methods applied to the energy efficiency (EE) ...maximization in the optical code division multiple access (OCDMA) communication network. The particle swarm optimization (PSO) and a hybridization between the PSO and the gravitational search algorithm (GSA) called PSOGSA have been deployed. The ALM structure replaces the objective function and allows a best fit to the problem, and ultimately provide more information about the solution. Numerical results demonstrate the robustness and low-complexity of hybrid ALM-PSO, while the ALM associated with PSOGSA attains robustness at cost of high-complexity. In turn, the usually ALM combined with Broyden-Fletcher-Goldfarb-Shanno (BFGS) method presents convergence for a restrict scenarios, failing to perform suitably for networks with large numbers of users.
This paper proposes a novel approach that selects the number of clusters along with relevant features automatically and simultaneously. Gravitational search algorithm is used as metaheuristic. A ...novel agent representation scheme is used for encoding cluster centers and number of features. The algorithm is able to find the optimal number of clusters and the relevant features corresponding to the clusters during the run time. A new concept of threshold setting is used. The variance (statistical property) of the dataset has been exploited. To make the search efficient, a novel clustering criterion is used. The proposed approach is compared with recently developed well-known clustering techniques. This approach is further applied for analysis of microarray data. The statistical and biological significance tests are performed to demonstrate the efficiency of proposed approach. The results prove the effectiveness and the accuracy of the proposed algorithm.
In this paper, a deep learning neural network model predictive controller (DLNNMPC) is designed to analyse the performance of a non-linear continuous stirred tank reactor (CSTR) that performs ...parallel and series reactions. The data generated employing the state space model of CSTR is used to train the designed deep learning neural network controller. Deep Learning Neural Network (DLNN) progresses the training with its weights tuned by the proposed hybrid version of evolutionary algorithms – Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). The developed hybrid PSO – GSA based DLNN model of continuous stirred tank reactor is employed in this paper for model predictive controller design. The effectiveness of the proposed DLNNMPC tuned by hybrid PSO – GSA for CSTR is validated for its performance on comparison with that of other designed Proportional – Integral (PI) and Proportional – Integral – Derivative (PID) controllers as available in early literatures for the same problem under consideration.
The smart city idea has gained much popularity in recent years because of its potential to enhance urban people's quality of life. The idea encompasses a wide range of fields, including smart ...community, smart transportation, and smart healthcare. For intelligent decision-making, most smart city services, particularly those in the smart healthcare field, demand analyzing, processing, and real-time sharing of big healthcare data. The current state of the healthcare industry requires the supply of healthcare products and services, increasing its viability for everyone involved. This paper presents a novel healthcare framework based on clustering sensor nodes, where the physical body is separated into three areas: the bottom, top, and intermediate body regions. The regions are clustered using an enhanced LEACH algorithm. Cluster heads are selected using the Gravitational Search Algorithm (GSA). The MATLAB environment is used to evaluate the proposed framework by comparing it to other approaches. Our framework outperforms previous methods in terms of energy consumption and throughput by 20% and 30%, respectively.
Large excitonic binding energies in monolayers of transition metal dichalcogenides such as molybdenum disulfide (MoS
2
), molybdenum diselenide (MoSe
2
), tungsten disulfide (WS
2
) and tungsten ...diselenide (WSe
2
), were calculated using a gravitational search algorithm. The optimized fitness function is based on a two dimensional (2D) effective mass model of excitons, parameterized by first principle calculations, including a suitable treatment of screening. In addition to the ground state, the binding energies of the first few excited states of the exciton were computed, hence the optical transition energies, as a function of principal quantum number
n,
were obtained for the exciton states. The method was also used to predict the corresponding 2D polarizabilities, and consequently, dielectric constants for the 2D semiconductors. Dependence of the effective dielectric constants on
n
was also investigated. Our results compare favorably with existing theoretical methods based on density function theory or GW approximation and the Bethe–Salpeter equation. Furthermore, our results are in reasonable agreement with recent experimental measurements.
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