Gravitational search algorithm (GSA) is one of the heuristic algorithms proposed in recent years, which is inspired by the law of universal gravitation between masses. However, many practical ...applications and researches show that when the region affected by global optimum occupies less search space, GSA is prone to fall into local optimum, especially when the optimal value is close to the boundary of the search space and there are sub-optimal solutions in the center of the space. By microscopic analysis of the particle motion process of GSA in the above optimization situation, we find that the Kbest mechanism and the characteristic of central convergence are the two main factors affecting the GSA optimization performance. In this paper, an improved algorithm called Balanced Gravitational Search Algorithm is proposed, in which the balance operator is designed to solve two inherent problems in GSA. Then the proposed method is firstly tested on 10 benchmark functions provided by CEC 2020 compared with the state-of-the-art variant algorithms of the GSA and other typical meta-heuristics. Further, the algorithms are tested and compared on the real-world optimization problems including CEC 2011 real-world optimization problems and the Multi-Layer Neural Network (MLNN) training problem based on wine dataset. The simulation results show that BGSA can significantly improve the optimization performance of GSA and it can be a good choice for solving real-world optimization problems.
•Proposing balanced gravitational search algorithm.•Central convergence characteristic of GSA.•Detailed analysis of gravitational forces and particle motion progress from a microscopic view.
Due to enormous growth of Internet of Things (IoT) in the last decade, the amount of data generated through smart devices is increasing exponentially. Fog computing has emerged as a potential ...technology to deal such a huge volume of data in which task offloading is the most important aspect which has attracted significant attention. Many research works have been carried out, however, task offloading with latency sensitivity, reliability and result migration over a mobile user environment is still not widely addressed. In this paper, we propose a method for delay-sensitive and fault minimized task offloading for service requests made through a mobile/vehicular end user environment implemented via Software Defined Network (SDN) controllers integrated with the fog layer. This is a novel multi-phased model involving determining the optimal number of SDN controllers, clustering of the fog nodes (FNs) on the basis of SDN proximities, task prioritization and Gravitational Search Algorithm (GSA) based target FN selection. The simulation outcomes of our proposed approach show that there is a reduction in delay by around 23%–30% and around 60%–80% lesser number of tasks unassigned in each round as compared to two base algorithms.
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•Chaotic maps have been embedded into Gravitational Search Algorithms (GSA) for the first time.•The problem of trapping in local minima in GSA has been improved by the chaotic ...maps.•The convergence rate of GSA has been improved.•The statistical test allowed us to judge about the significance of the results.•An adaptive normalization is proposed to smoothly transit from the exploration phase to the exploitation phase.
In a population-based meta-heuristic, the search process is divided into two main phases: exploration versus exploitation. In the exploration phase, a random behavior is fruitful to explore the search space as extensive as possible. In contrast, a fast exploitation toward the promising regions is the main objective of the latter phase. It is really challenging to find a proper balance between these two phases because of the stochastic nature of population-based meta-heuristic algorithms. The literature shows that chaotic maps are able to improve both phases. This work embeds ten chaotic maps into the gravitational constant (G) of the recently proposed population-based meta-heuristic algorithm called Gravitational Search Algorithm (GSA). Also, an adaptive normalization method is proposed to transit from the exploration phase to the exploitation phase smoothly. As case studies, twelve shifted and biased benchmark functions evaluate the performance of the proposed chaos-based GSA algorithms in terms of exploration and exploitation. A statistical test called Wilcoxon rank-sum is done to judge about the significance of the results as well. The results demonstrate that sinusoidal map is the best map for improving the performance of GSA significantly.
Gravitational Search Algorithm (GSA) is an optimization method inspired by the theory of Newtonian gravity in physics. Till now, many variants of GSA have been introduced, most of them are motivated ...by gravity-related theories such as relativity and astronomy. On the one hand, to solve different kinds of optimization problems, modified versions of GSA have been presented such as continuous (real), binary, discrete, multimodal, constraint, single-objective, and multi-objective GSA. On the other hand, to tackle the difficulties in real-world problems, the efficiency of GSA has been improved using specialized operators, hybridization, local search, and designing the self-adaptive algorithms. Researchers have utilized GSA to solve various engineering optimization problems in diverse fields of applications ranging from electrical engineering to bioinformatics. Here, we discussed a comprehensive investigation of GSA and a brief review of GSA developments in solving different engineering problems to build up a global picture and to open the mind to explore possible applications. We also made a number of suggestions that can be undertaken to help move the area forward.
The PM2.5 is the culprit of air pollution, and it leads to respiratory system disease when the fine particles are inhaled. Therefore, it is increasingly significant to develop an effective model for ...PM2.5 forecasting and warnings that informs people to foresee the air quality. People can reduce outdoor activities and take preventive measures if they know the air quality is bad ahead of time. In addition, reliable forecasting results can remind the relevant departments to control and reduce pollutants discharge. According to our knowledge, the current hybrid forecasting techniques of PM2.5 do not take the meteorological factors into consideration. Actually, meteorological factors affect the concentrations of air pollution, but it is unclear whether meteorological factors are helpful for improving the PM2.5 forecasting results or not. This paper proposes a hybrid model called CEEMD-PSOGSA-SVR-GRNN, based on complementary ensemble empirical mode decomposition (CEEMD), particle swarm optimization and gravitational search algorithm (PSOGSA), support vector regression (SVR), generalized regression neural network (GRNN) and grey correlation analysis (GCA), for the daily PM2.5 concentrations forecasting. The main steps of proposed model are described as follows: the original PM2.5 data decomposition with CEEMD, optimal SVR selection with PSOGCA, meteorological factors selection with GCA, residual revision by GRNN and forecasting results analysis. Three cities (Chongqing, Harbin and Jinan) in China with different characteristics of climate, terrain and pollution sources are selected to verify the effectiveness of proposed model, and CEEMD-PSOGSA-SVR*, EEMD-PSOGSA-SVR, PSOGSA-SVR, CEEMD-PSO-SVR, CEEMD-GSA-SVR, CEEMD-GWO-SVR are considered to be compared models. The experimental results show that the hybrid CEEMD-PSOGSA-SVR-GRNN model outperforms other six compared models. Therefore, the proposed CEEMD-PSOGSA-SVR-GRNN model can be used to develop air quality forecasting and warnings.
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•Meteorological factors are studied for PM2.5 forecasting.•Effective information of PM2.5 is extracted by CEEMD.•The proposed CEEMD-PSOGSA-SVR-GRNN model is effective for PM2.5 forecasting.•The proposed theory can be used to effectively forecast other pollutions.
Recently, electricity generation from solar photovoltaic (PV) has gained popularity throughout the world due to its profuse availability and eco-friendly nature. Consequently, extraction of maximum ...power from solar PV energy systems was the point of interest in the current researches. Various techniques have been proposed to track the maximum power point (MPP) from solar PV energy systems under variable environmental conditions. Conventional maximum power point tracking (MPPT) techniques have demonstrated the ability to track MPP with uniform solar irradiance. However, the ability of these techniques to track the accurate MPP with the condition of partial shading (PS) is not guaranteed. Hence, this paper intended to present novel optimization techniques to mitigate the PS effect and proficiently track the global maximum power point (GMPP). Grey Wolf Optimization (GWO), Moth-Flame Optimization (MFO), Salp Swarm Algorithm (SSA) and Hybrid Particle Swarm Optimization-Gravitational Search Algorithm (PSO-GSA) techniques have been proposed to handle this dilemma. The proposed techniques have been simulated and analyzed using MATLAB/SIMULINK. Furthermore, these techniques have been compared with the conventional PSO algorithm for validation. Statistical and sensitivity analysis have been established to compare the performance, check the stability, and determine the best technique out of the proposed techniques. Results showed the superiority of GWO in the speed of convergence and the time to catch GMPP. Moreover, the sensitivity analysis demonstrated the stability, successfully rate, and tracking efficiency of PSO-GSA technique. Finally, this paper gives an open reference to these optimizers to attempt mass research works in PV systems under PS.
•Implementation of distinctive meta-heuristic optimization algorithms for increasing the PV system efficiency under PSC.•The proposed algorithms are GWO, MFO, PSO-GSA, and SSA.•Determination of the GMPP from the multiple local peaks caused by different irradiances.•Comparing the proposed algorithms with PSO algorithm for approval.•Introducing statistical and sensitivity analysis to compare the performance and check the stability of proposed algorithms.
► Gravitational search algorithm (GSA) has been proposed to find the solution for OPF. ► We determine optimal settings of control variables of OPF problem. ► The performance of the GSA has been ...sought and tested on the IEEE 30-bus and 57-bus test systems. ► The comparison verifies the influence of the proposed GSA approach over stochastic techniques.
In this paper, gravitational search algorithm (GSA) is proposed to find the optimal solution for optimal power flow (OPF) problem in a power system. The proposed approach is applied to determine the optimal settings of control variables of the OPF problem. The performance of the proposed approach examined and tested on the standard IEEE 30-bus and 57-bus test systems with different objective functions and is compared to other heuristic methods reported in the literature recently. Simulation results obtained from the proposed GSA approach indicate that GSA provides effective and robust high-quality solution for the OPF problem.
The Gravitational Search Algorithm (GSA) is a novel heuristic optimization method based on the law of gravity and mass interactions. It has been proven that this algorithm has good ability to search ...for the global optimum, but it suffers from slow searching speed in the last iterations. This work proposes a hybrid of Particle Swarm Optimization (PSO) and GSA to resolve the aforementioned problem. In this paper, GSA and PSOGSA are employed as new training methods for Feedforward Neural Networks (FNNs) in order to investigate the efficiencies of these algorithms in reducing the problems of trapping in local minima and the slow convergence rate of current evolutionary learning algorithms. The results are compared with a standard PSO-based learning algorithm for FNNs. The resulting accuracy of FNNs trained with PSO, GSA, and PSOGSA is also investigated. The experimental results show that PSOGSA outperforms both PSO and GSA for training FNNs in terms of converging speed and avoiding local minima. It is also proven that an FNN trained with PSOGSA has better accuracy than one trained with GSA.
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•Real time industrial zone that consists of wind power and solar power.•The Southern Regional Load Dispatch Centre (SRLDC) 72 bus system.•The results are verified by conventional ...method, modeled in Simulink and by algorithmic method.
The optimized position of placing FACT device in an industrial zone is a challenging task. If the FACT device is perfectly placed, the reactive power losses can be controlled within a limit and can improve the real power flow in the power system network. Many algorithms have been developed in the recent years using Swarm intelligence, Genetic algorithm, Honey Bee search and fish schooling algorithms. Gravitational search algorithm based on Newtonian law of gravity between masses is used to find the minimum value accurately. In Opposition based GSA (OGSA) instead of considering both active and passive masses, the passive mass alone is considered which is equivalent to reactive power force component. The active force is not considered. The resultant force obtained is less accurate. But in Cumulative Gravitational Search Algorithm (CGSA) active and passive mass interactions are together considered so the resultant force obtained between the masses will be effectively taken into account. Two different mass inertia, namely active mass and passive mass are applied in CGSA, and exact results can be found. In this algorithm, the search agents are a collection of masses which interacts with each other based on Newtonian gravity and laws of motion. In this research, 72 bus southern grid system and a 16 bus real time industrial zone are tested by conventional method, modeled using MATLAB/Simulink and by using the proposed CGSA. The optimized place to connect the FACT device is found and compared with conventional and modeling method.
Workflow Scheduling in cloud computing has drawn enormous attention due to its wide application in both scientific and business areas. This is particularly an NP-complete problem. Therefore, many ...researchers have proposed a number of heuristics as well as meta-heuristic techniques by considering several issues, such as energy conservation, cost and makespan. However, it is still an open area of research as most of the heuristics or meta-heuristics may not fulfill certain optimum criterion and produce near optimal solution. In this paper, we propose a meta-heuristic based algorithm for workflow scheduling that considers minimization of makespan and cost. The proposed algorithm is a hybridization of the popular meta-heuristic, Gravitational Search Algorithm (GSA) and equally popular heuristic, Heterogeneous Earliest Finish Time (HEFT) to schedule workflow applications. We introduce a new factor called cost time equivalence to make the bi-objective optimization more realistic. We consider monetary cost ratio (MCR) and schedule length ratio (SLR) as the performance metrics to compare the performance of the proposed algorithm with existing algorithms. With rigorous experiments over different scientific workflows, we show the effectiveness of the proposed algorithm over standard GSA, Hybrid Genetic Algorithm (HGA) and the HEFT. We validate the results by well-known statistical test, Analysis of Variance (ANOVA). In all the cases, simulation results show that the proposed approach outperforms these algorithms.
•Proposed an efficient hybrid scheme of GSA and HEFT, called HGSA for workflow scheduling.•Systematic derivation of fitness function based on makespan and cost.•Novelty in introducing a proficient elimination strategy of inferior agents.•Demonstration of better performance through simulation results and statistical test ANOVA.
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