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The purpose of this paper is to investigate the performance of chaotic gravitational search algorithm (CGSA) in solving mechanical engineering design frameworks including welded beam design (WBD), compression spring design (CSD) and pressure vessel design (PVD).

In this study, ten chaotic maps were combined with gravitational constant to increase the exploitation power of gravitational search algorithm (GSA). Also, CGSA has been used for maintaining the adaptive capability of gravitational constant. Furthermore, chaotic maps were used for overcoming premature convergence and stagnation in local minima problems of standard GSA.

The chaotic maps have shown efficient performance for WBD and PVD problems. Further, they have depicted competitive results for CSD framework. Moreover, the experimental results indicate that CGSA shows efficient performance in terms of convergence speed, cost function minimization, design variable optimization and successful constraint handling as compared to other participating algorithms.

The use of chaotic maps in standard GSA is a new beginning for research in GSA particularly convergence and time complexity analysis. Moreover, CGSA can be used for solving the infinite impulsive response (IIR) parameter tuning and economic load dispatch problems in electrical sciences.

The hybridization of chaotic maps and evolutionary algorithms for solving practical engineering problems is an emerging topic in metaheuristics. In the literature, it can be seen that researchers have used some chaotic maps such as a logistic map, Gauss map and a sinusoidal map more rigorously than other maps. However, this work uses ten different chaotic maps for engineering design optimization. In addition, non-parametric statistical test, namely, Wilcoxon rank-sum test, was carried out at 5% significance level to statistically validate the simulation results. Besides, 11 state-of-the-art metaheuristic algorithms were used for comparative analysis of the experimental results to further raise the authenticity of the experimental setup.

This paper aims to develop the Dragonfly-based exponential gravitational search algorithm to VMM strategy for effective load balancing in cloud computing. Due to widespread growth of cloud users, load balancing is the essential criterion to deal with the overload and underload problems of the physical servers. DEGSA-VMM is introduced, which calculates the optimized position to perform the virtual machine migration (VMM).

This paper presents an algorithm Dragonfly-based exponential gravitational search algorithm (DEGSA) that is based on the VMM strategy to migrate the virtual machines of the overloaded physical machine to the other physical machine keeping in mind the energy, migration cost, load and quality of service (QoS) constraints. For effective migration, a fitness function is provided, which selects the best fit that possess minimum energy, cost, load and maximum QoS contributing toward the maximum energy utilization.

For the performance analysis, the experimentation is performed with three setups, with Setup 1 composed of three physical machines with 12 virtual machines, Setup 2 composed of five physical machines and 19 virtual machines and Setup 3 composed of ten physical machines and 28 virtual machines. The performance parameters, namely, QoS, migration cost, load and energy, of the proposed work are compared over the other existing works. The proposed algorithm obtained maximum resource utilization with a good QoS at a rate of 0.19, and minimal migration cost at a rate of 0.015, and minimal energy at a rate of 0.26 with a minimal load at a rate of 0.1551, whereas with the existing methods like ant colony optimization (ACO), gravitational search algorithm (GSA) and exponential gravitational search algorithm, the values of QoS, load, migration cost and energy are 0.16, 0.1863, 0.023 and 0.29; 0.16, 0.1863, 0.023 and 0.28 and 0.18, 0.1657, 0.016 and 0.27, respectively.

This paper presents an algorithm named DEGSA based on VMM strategy to determine the optimum position to perform the VMM to achieve a better load balancing.

In this paper, a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm (CPSOGSA) has been employed for training MLP to overcome sensitivity to initialization, premature convergence, and stagnation in local optima problems of MLP.

In this study, the exploration of the search space is carried out by gravitational search algorithm (GSA) and optimization of candidate solutions, i.e. exploitation is performed by particle swarm optimization (PSO). For training the multi-layer perceptron (MLP), CPSOGSA uses sigmoid fitness function for finding the proper combination of connection weights and neural biases to minimize the error. Secondly, a matrix encoding strategy is utilized for providing one to one correspondence between weights and biases of MLP and agents of CPSOGSA.

The experimental findings convey that CPSOGSA is a better MLP trainer as compared to other stochastic algorithms because it provides superior results in terms of resolving stagnation in local optima and convergence speed problems. Besides, it gives the best results for breast cancer, heart, sine function and sigmoid function datasets as compared to other participating algorithms. Moreover, CPSOGSA also provides very competitive results for other datasets.

The CPSOGSA performed effectively in overcoming stagnation in local optima problem and increasing the overall convergence speed of MLP. Basically, CPSOGSA is a hybrid optimization algorithm which has powerful characteristics of global exploration capability and high local exploitation power. In the research literature, a little work is available where CPSO and GSA have been utilized for training MLP. The only related research paper was given by Mirjalili et al., in 2012. They have used standard PSO and GSA for training simple FNNs. However, the work employed only three datasets and used the MSE performance metric for evaluating the efficiency of the algorithms. In this paper, eight different standard datasets and five performance metrics have been utilized for investigating the efficiency of CPSOGSA in training MLPs. In addition, a non-parametric pair-wise statistical test namely the Wilcoxon rank-sum test has been carried out at a 5% significance level to statistically validate the simulation results. Besides, eight state-of-the-art meta-heuristic algorithms were employed for comparative analysis of the experimental results to further raise the authenticity of the experimental setup.

Nowadays, global warming, due to large-scale emissions of greenhouse gasses, is among top environmental issues. The purpose of this paper is to present a problem involving the incorporation of environmental aspects into logistics, which provides a comparison between pollution reduction and distance-based approaches.

In green vehicle routing problem (VRP), the aim is to model and solve an optimization problem in order to minimize the fuel consumption which results in reducing energy consumption as well as air pollution. The Gravitational Search Algorithm (GSA) is adapted and used as a powerful heuristic.

Here, it is shown that a set of routes with minimum length is not an optimal solution for FCVRP model since the total distance is not the only effective factor for fuel consumption and vehicle's load plays an important role too. In many cases, a considerable reduction in emissions can be achieved by only an insignificant increase in costs.

Green transportation is a policy toward reducing carbon emissions. This research focussed on routes problem and introduce FCVRP model. GSA is used as a powerful heuristic to obtain high quality routes in a reasonable time. Considering other factors that affecting fuel consumption could make this study more realistic.

When a distribution center receives all the information it needs about the demand from all the retail stores it supplies, a VRP is produced. So the models are valid for use by all goods producers and distributors. The preliminary assessment of the proposed model and method carried out on benchmark problems up to 200 nodes.

Fuel consumption is one of the most influential factors in transportation costs. This paper introduces an innovative decision-making framework to obtain optimum routes in a vehicle routes problem considering air pollution. The results were compared from fuel consumption as well as total travel distance viewpoints.

The purpose of this paper is to present and solve the problem of adaptive beamforming (ABF) for a uniform linear array (ULA) as an optimization problem. ABF mainly concerns with estimation of weights of antenna array so as to direct the major lobe in the direction of desired user and nulls in the direction of interfering signals with reduced side lobe level (SLL).

The potential of gravitational search algorithm is explored to optimize multi-objective fitness function for ABF using MATLAB software.

The performance of the algorithm has been compared by considering different number of interference signals at different power levels. The proposed algorithm presents good convergence rate and accurate steering of main lobe and nulls with reduced SLL compared to the well-known ABF technique, namely, minimum variance distortionless response (MVDR) and previously reported results. The simulation results are presented in tabular form.

The present work is limited to simulation. The researchers are encouraged to solve the problem of ABF using the proposed approach in hardware.

The application of proposed algorithm is to optimize multi-objective function for ABF with reduced SLL in linear antenna arrays.

This paper aims to present a new hybrid algorithm of Particle Swarm Optimization and the Genetic Algorithm (PSOGA) to optimize the space trusses with continuous design variables. The PSOGA is an efficient hybridized algorithm to solve optimization problems.

These algorithms have shown outstanding performance in solving optimization problems with continuous variables. The PSO conceptually models the social behavior of birds, in which individual birds exchange information about their position, velocity and fitness. The behavior of a flock is influencing the probability of migration to other regions with high fitness. The GAs procedure is based on the mechanism of natural selection. The present study uses mutation, random selection and reproduction to reach the best genetic algorithm by the operators of natural genetics. Thus, only identical chromosomes or particles can be converged.

In this research, using the idea of hybridization PSO and GA algorithms are hybridized and a new meta-heuristic algorithm is developed to minimize the space trusses with continuous design variables. To showing the efficiency and robustness of the new algorithm, several benchmark problems are solved and compared with other researchers.

The results indicate that the hybrid PSO algorithm improved in both exploration and exploitation. The PSO algorithm can be used to minimize the weight of structural problems under stress and displacement constraints.

The purpose of this paper is to improve energy efficiency and further reduction of side lobe level the algorithm proposed is firework algorithm. In this paper, roused by the eminent swarm conduct of firecrackers, a novel multitude insight calculation called fireworks algorithm (FA) is proposed for work enhancement. The FA is introduced and actualized by mimicking the blast procedure of firecrackers. In the FA, two blast (search) forms are utilized and systems for keeping decent variety of sparkles are likewise all around planned. To approve the presentation of the proposed FA, correlation tests were led on nine benchmark test capacities among the FA, the standard PSO (SPSO) and the clonal PSO (CPSO).

The antenna arrays are used to improve the capacity and spectral efficiency of wireless communication system. The latest communication systems use the antenna array technology to improve the spectral efficiency, fill rate and the energy efficiency of the communication system can be enhanced. One of the most important properties of antenna array is beam pattern. A directional main lobe with low side lobe level (SLL) of the beam pattern will reduce the interference and enhance the quality of communication. The classical methods for reducing the side lobe level are differential evolution algorithm and PSO algorithm. In this paper, roused by the eminent swarm conduct of firecrackers, a novel multitude insight calculation called fireworks algorithm (FA) is proposed for work enhancement. The FA is introduced and actualized by mimicking the blast procedure of firecrackers. In the FA, two blast (search) forms are utilized and systems for keeping decent variety of sparkles are likewise all around planned. To approve the presentation of the proposed FA, correlation tests were led on nine benchmark test capacities among the FA, the standard PSO (SPSO) and the clonal PSO (CPSO). It is demonstrated that the FA plainly beats the SPSO and the CPSO in both enhancement exactness and combination speed. The results convey that the side lobe level is reduced to −34.78dB and fill rate is increased to 78.53.

Samples including 16-element LAAs are conducted to verify the optimization performances of the SLL reductions. Simulation results show that the SLLs can be effectively reduced by FA. Moreover, compared with other benchmark algorithms, fireworks has a better performance in terms of the accuracy, the convergence rate and the stability.

With the use of algorithms radiation is prone to noise one way or other. Even with any optimizations we cannot expect radiation to be ideal. Power dissipation or electro magnetic interference is bound to happen, but the use of optimization algorithms tries to reduce them to the extent that is possible.

16-element linear antenna array is available with latest versions of Matlab.

The latest technologies and emerging developments in the field of communication and with exponential growth in users the capacity of communication system has bottlenecks. The antenna arrays are used to improve the capacity and spectral efficiency of wireless communication system. The latest communication systems use the antenna array technology which is to improve the spectral efficiency, fill rate and the energy efficiency of the communication system can be enhanced.

By using FA, the fill rate is increased to 78.53 and the side lobe level is reduced to 35dB, when compared with the bench mark algorithms.

Leakage current is one of the factors, which can contribute towards degradation of surge arresters. Thus, the purpose of this paper is to study on leakage current within surge arresters and improvement on their design.

In this work, a three-dimensional model geometry of 11 kV zinc oxide surge arrester was designed in finite element analysis and was applied to calculate the leakage current under normal operating condition and being verified with measurement results. The optimisation methods were used to improve the arrester design by minimising the leakage current across the arrester using imperialist competitive algorithm (ICA) and gravitational search algorithm (GSA).

The arrester design in reducing leakage current was successfully optimised by varying the glass permittivity, silicone rubber permittivity and the width of the ground terminal of the surge arrester. It was found that the surge arrester design obtained using ICA has lower leakage current than GSA and the original design of the surge arrester.

The comparison between measurement and simulation enables factors that affect the mechanism of leakage current in surge arresters to be identified and provides the ideal design of arrester.

Surge arrester design was optimised by ICA and GSA, which has never been applied in past works in designing surge arrester with minimum leakage current.

This paper aims to optimally plan distributed generation (DG) and capacitor in distribution network by optimizing multiple conflicting operational objectives simultaneously so as to achieve enhanced operation of distribution system. The multi-objective optimization problem comprises three important objective functions such as minimization of total active power loss (P^loss_total), reduction of voltage deviation and balancing of current through feeder sections.

In this study, a hybrid configuration of weight improved particle swarm optimization (WIPSO) and gravitational search algorithm (GSA) called hybrid WIPSO-GSA algorithm is proposed in multi-objective problem domain. To solve multi-objective optimization problem, the proposed hybrid WIPSO-GSA algorithm is integrated with two components. The first component is fixed-sized archive that is responsible for storing a set of non-dominated pareto optimal solutions and the second component is a leader selection strategy that helps to update and identify the best compromised solution from the archive.

The proposed methodology is tested on standard 33-bus and Indian 85-bus distribution systems. The results attained using proposed multi-objective hybrid WIPSO-GSA algorithm provides potential technical and economic benefits and its best compromised solution outperforms other commonly used multi-objective techniques, thereby making it highly suitable for solving multi-objective problems.

A novel multi-objective hybrid WIPSO-GSA algorithm is proposed for optimal DG and capacitor planning in radial distribution network. The results demonstrate the usefulness of the proposed technique in improved distribution system planning and operation and also in achieving better optimized results than other existing multi-objective optimization techniques.

Automated crop prediction is needed for the following reasons: First, agricultural yields were decided by a farmer's ability to work in a certain field and with a particular crop previously. They were not always able to predict the crop and its yield solely on that idea alone. Second, seed firms frequently monitor how well new plant varieties would grow in certain settings. Third, predicting agricultural production is critical for solving emerging food security concerns, especially in the face of global climate change. Accurate production forecasts not only assist farmers in making informed economic and management decisions but they also aid in the prevention of famine. This results in farming systems’ efficiency and productivity gains, as well as reduced risk from environmental factors.

This research paper proposes a machine learning technique for effective autonomous crop and yield prediction, which makes use of solution encoding to create solutions randomly, and then for every generated solution, fitness is evaluated to meet highest accuracy. Major focus of the proposed work is to optimize the weight parameter in the input data. The algorithm continues until the optimal agent or optimal weight is selected, which contributes to maximum accuracy in automated crop prediction.

Performance of the proposed work is compared with different existing algorithms, such as Random Forest, support vector machine (SVM) and artificial neural network (ANN). The proposed method support vector neural network (SVNN) with gravitational search agent (GSA) is analysed based on different performance metrics, such as accuracy, sensitivity, specificity, CPU memory usage and training time, and maximum performance is determined.

Rather than real-time data collected by Internet of Things (IoT) devices, this research focuses solely on historical data; the proposed work does not impose IoT-based smart farming, which enhances the overall agriculture system by monitoring the field in real time. The present study only predicts the sort of crop to sow not crop production.

The paper proposes a novel optimization algorithm, which is based on the law of gravity and mass interactions. The search agents in the proposed algorithm are a cluster of weights that interact with one another using Newtonian gravity and motion principles. A comparison was made between the suggested method and various existing strategies. The obtained results confirm the high-performance in solving diverse nonlinear functions.