•A new aggregative learning gravitational search algorithm is proposed.•A self-adaptive gravitational constant is introduced into the algorithm.•Extensive performance comparison with other ...state-of-the-art algorithms is done.•Neural network learning tasks show the proposed algorithm’s practicability.•The time complexity of the proposed algorithm is analyzed.
The gravitational search algorithm (GSA) is a meta-heuristic algorithm based on the theory of Newtonian gravity. This algorithm uses the gravitational forces among individuals to move their positions in order to find a solution to optimization problems. Many studies indicate that the GSA is an effective algorithm, but in some cases, it still suffers from low search performance and premature convergence. To alleviate these issues of the GSA, an aggregative learning GSA called the ALGSA is proposed with a self-adaptive gravitational constant in which each individual possesses its own gravitational constant to improve the search performance. The proposed algorithm is compared with some existing variants of the GSA on the IEEE CEC2017 benchmark test functions to validate its search performance. Moreover, the ALGSA is also tested on neural network optimization to further verify its effectiveness. Finally, the time complexity of the ALGSA is analyzed to clarify its search performance.
•Applying the gravitational search algorithm (GSA) to solve the combined heat and power economic dispatch (CHPED) problem.•Modeling the valve-point loading effect and transmission losses in the CHPED ...problem.•Verifying the effectiveness of the proposed method on different case studies.•Comparing the results of GSA-based CHPED problem with various heuristic algorithms such as GA, HS, EDHS, CPSO, and TVAC-PSO.
This paper presents the application of a novel optimization algorithm, namely gravitational search algorithm (GSA) to solve the non-convex combined heat and power economic dispatch (CHPED) problems. The proposed approach is based on the gravitational law and the law of particles motion. The effectiveness of the suggested algorithm is tested on study-cases which include modeling of valve-point loading effect and transmission losses. Results of GSA-based CHPED problem in terms of quality solution and computational performance are compared with various algorithms to show the ability of the introduced algorithm in finding an operating point with lower fuel cost.
In this article, the Gravitational Search Algorithm (GSA) has been proposed to find the optimal solution for Combined Economic and Emission Dispatch (CEED) problems. It is aimed, in the CEED problem, ...that scheduling of generators should operate with both minimum fuel costs and emission levels, simultaneously, while satisfying the load demand and operational constraints. In this paper, the CEED problem is formulated as a multi-objective problem by considering the fuel cost and emission objectives of generating units. The bi-objective optimization problem is converted into a single objective function using a price penalty factor in order to solve it with GSA. The proposed algorithm has been implemented on four different test cases, having a valve point effect with transmission loss and having no valve point effect without transmission loss. In order to see the effectiveness of the proposed algorithm, it has been compared with other algorithms in the literature. Results show that the GSA is more powerful than other algorithms.
•A hybrid model is developed for short-term wind power prediction.•The model is based on LSSVM and gravitational search algorithm.•Gravitational search algorithm is used to optimize parameters of ...LSSVM.•Effect of different kernel function of LSSVM on wind power prediction is discussed.•Comparative studies show that prediction accuracy of wind power is improved.
Wind power forecasting can improve the economical and technical integration of wind energy into the existing electricity grid. Due to its intermittency and randomness, it is hard to forecast wind power accurately. For the purpose of utilizing wind power to the utmost extent, it is very important to make an accurate prediction of the output power of a wind farm under the premise of guaranteeing the security and the stability of the operation of the power system. In this paper, a hybrid model (LSSVM–GSA) based on the least squares support vector machine (LSSVM) and gravitational search algorithm (GSA) is proposed to forecast the short-term wind power. As the kernel function and the related parameters of the LSSVM have a great influence on the performance of the prediction model, the paper establishes LSSVM model based on different kernel functions for short-term wind power prediction. And then an optimal kernel function is determined and the parameters of the LSSVM model are optimized by using GSA. Compared with the Back Propagation (BP) neural network and support vector machine (SVM) model, the simulation results show that the hybrid LSSVM–GSA model based on exponential radial basis kernel function and GSA has higher accuracy for short-term wind power prediction. Therefore, the proposed LSSVM–GSA is a better model for short-term wind power prediction.
Metaheuristic optimization algorithms address two main tasks in the process of problem solving: i) exploration (also called diversification) and ii) exploitation (also called intensification). ...Guaranteeing a trade-off between these operations is critical to good performance. However, although many methods have been proposed by which metaheuristics can achieve a balance between the exploration and exploitation stages, they are still worse than exact algorithms at exploitation tasks, where gradient-based mechanisms outperform metaheuristics when a local minimum is approximated. In this paper, a quasi-Newton method is introduced into a Chaotic Gravitational Search Algorithm as an exploitation method, with the purpose of improving the exploitation capabilities of this recent and promising population-based metaheuristic. The proposed approach, referred to as a Memetic Chaotic Gravitational Search Algorithm, is used to solve forty-five benchmark problems, both synthetic and real-world, to validate the method. The numerical results show that the adding of quasi-Newton search directions to the original (Chaotic) Gravitational Search Algorithm substantially improves its performance. Also, a comparison with the state-of-the-art algorithms: Particle Swarm Optimization, Genetic Algorithm, Rcr-JADE, COBIDE and RLMPSO, shows that the proposed approach is promising for certain real-world problems.
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•A memetic framework for hybridizing GSA algorithm using descent direction methods.•The memetic (chaotic) GSA outperforms the original (chaotic) GSA.•M-CGSA-9 is competitive with regard to several state-of-art metaheuristics.
•Modified version of the Gravitational Search Algorithm (GSA).•We incorporate manifold learning to evaluate more fitted magnitude for the attraction forces.•We incorporate elitism.•We benchmark a ...large set of functions to show the performance of the algorithm compared to the state-of-the-art metaheuristic optimization algorithms.•The comparative study show that the improved method has advantages in terms of the final cost value and rate of convergence.
Metaheuristic algorithms provide a practical tool for optimization in a high-dimensional search space. Some mimic phenomenons of nature such as swarms and flocks. Prominent one is the Gravitational Search Algorithm (GSA) inspired by Newton’s law of gravity to manipulate agents modeled as point masses in the search space. The law of gravity states that interaction forces are inversely proportional to the squared distance in the Euclidean space between two objects. In this paper we claim that when the set of solutions lies in a lower-dimensional manifold, the Euclidean distance would yield unfitted forces and bias in the results, thus causing suboptimal and slower convergence. We propose to modify the algorithm and utilize geodesic distances gained through manifold learning via diffusion maps. In addition, we incorporate elitism by storing exploration data. We show the high performance of this approach in terms of the final solution value and the rate of convergence compared to other meta-heuristic algorithms including the original GSA. In this paper we also provide a comparative analysis of the state-of-the-art optimization algorithms on a large set of standard benchmark functions.
The deformation of a Geosynthetic reinforced soil (GRS) structure is a key factor in designing this type of retaining structures. On the other hand, the feasibility of artificial intelligence ...techniques in solving geotechnical engineering problems is underlined in literature. This paper is aimed to show the workability of two soft computing techniques in predicting the deformation of GRS structures. For this reason, first a relevant case study was modelled into ABAQUS, a finite element (FE) software. Then, the FE results (GRS deformations) were checked against the recorded deformations of the full-scale test. Subsequently, 166 finite element analyses were performed for dataset construction. Then, two predictive models of GRS deformations were constructed. For intelligent model construction, two artificial neural networks (ANN) were coupled with Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO), respectively. It was found that both GSA-based ANN and PSO-based ANN predictive models work good enough. However, the correlation coefficient (R) of 0.981 as well as the system error of 0.0101 for testing data suggest that the GSA-based ANN predictive model outperforms the PSO-based ANN model with R value of 0.973 and system error of 0.0127. Overall, findings recommend that the proposed models can be implemented in assessing the performance of geosynthetic reinforced soil structures.
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•The role of a carbon tax in the sizing of the hybrid energy system was examined.•Forty-five different cases were compared to determine the optimal system.•A low carbon economic model ...was proposed to meet the load demand.•The effect of carbon dioxide emissions on the damage to human health was evaluated.•The importance of design objectives for sizing different systems was investigated.
Today, the use of renewable energy-based hybrid energy systems is widely welcomed due to their reduced environmental impact. Therefore, it is vital to optimally design these systems in terms of economy, reliability, and CO2 emissions. This work presents a new method based on Newton's Law of Gravity to achieve the above goals. For this purpose, the role of the carbon tax and the loss of power supply probability (LPSP) in sizing the hybrid energy systems is evaluated. Hence, distinct combinations of wind turbines, photovoltaic panels, and diesel generators are optimized by providing single and multi-purpose functions. Also, to produce cleaner energy, the carbon tax is enacted as a penalty function to control CO2 emissions. Furthermore, in this work, 45 cases are analyzed for achieving an optimal framework and some of the results obtained by the gravitational search algorithm (GSA) method are compared with the simulated annealing (SA) method. Therefore, according to the meteorological data, a PV/Wind/DG system with a total annual cost of 56,175 $/year and CO2 emission of 110,086 kg/year is suggested for the study area. In this case, by claiming a carbon tax of 0.05 $/kg, about 9 % of CO2 emission is prevented, and damage to human health is reduced by 8.9 %. Also, the outcomes display that more than 95 % of carbon dioxide emission is associated with using a diesel generator.
Gravitational search algorithm (GSA) inspired by the law of gravity is a swarm intelligent optimization algorithm. It utilizes the gravitational force to implement the interaction and evolution of ...individuals. The conventional GSA achieves several successful applications, but it still faces a premature convergence and a low search ability. To address these two issues, a hierarchical GSA with an effective gravitational constant (HGSA) is proposed from the viewpoint of population topology. Three contrastive experiments are carried out to analyze the performances between HGSA and other GSAs, heuristic algorithms and particle swarm optimizations (PSOs) on function optimization. Experimental results demonstrate the effective property of HGSA due to its hierarchical structure and gravitational constant. A component-wise experiment is also established to further verify the superiority of HGSA. Additionally, HGSA is applied to several real-world optimization problems so as to verify its good practicability and performance. Finally, the time complexity analysis is discussed to conclude that HGSA has the same computational efficiency in comparison with other GSAs.
Feature Selection (FS) is an important aspect of knowledge extraction as it helps to reduce dimensionality of data. Among the numerous FS algorithms proposed over the years, Gravitational Search ...Algorithm (GSA) is a popular one which has been applied to various domains. However, GSA suffers from the problem of pre-mature convergence which affects exploration leading to performance degradation. To aid exploration, in the present work, we use a clustering technique in order to make the initial population distributed over the entire feature space and to increase the inclusion of features which are more promising. The proposed method is named Clustering based Population in Binary GSA (CPBGSA). To assess the performance of our proposed model, 20 standard UCI datasets are used, and the results are compared with some contemporary methods. It is observed that CPBGSA outperforms other methods in 12 out of 20 cases in terms of average classification accuracy. The relevant codes of the entire CPBGSA model can be found in the provided link: https://github.com/ManosijGhosh/Clustering-based-Population-in-Binary-GSA.
•Exploration ability of BGSA is enhanced through guided initial population creation.•Application of CPBGSA over 20 popular UCI datasets with varying feature dimensions.•Successful comparison of the proposed algorithm with 11 state-of-the-art FS models.
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