Recently, gravitational search algorithm (GSA) was considered as one method for optimizing functions and solving real problems. For the sake of better adjust the values of recurrent RBF neural network (RRBFNN) to make...
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Recently, gravitational search algorithm (GSA) was considered as one method for optimizing functions and solving real problems. For the sake of better adjust the values of recurrent RBF neural network (RRBFNN) to make the network achieve better performance, the MGSA is essential in this article. The advised work achieves a better compromise between exploration and development. At the same time, by increasing the guidance of the global optimal particle, the problem that the gravitational search algorithm converges slowly in the later iteration is solved. The Experiment found that the network has better convergence speed and better test accuracy than the RRBFN optimized by the conventional optimization algorithm.
A new design optimization approach using the gravitational search algorithm is developed to obtain optimal configuration of a shell and tube heat exchanger from economic point of view. The objective function considere...
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A new design optimization approach using the gravitational search algorithm is developed to obtain optimal configuration of a shell and tube heat exchanger from economic point of view. The objective function considered for the optimization process is the total annual cost including the investment cost and the operating cost. The gravitational search algorithm (GSA) is a heuristic searchalgorithm based on the law of gravity and mass interactions. Taking into account the importance of shell and tube heat exchangers in industrial applications and the complexity in their geometry, the GSA methodology is adopted to obtain an optimal geometric configuration. The developed algorithm is applied to two case studies and the results are compared with the original design and other optimization methods available in literature such as Genetic algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Biogeography Based Optimization (BBO), Cuckoo searchalgorithm (CSA) and Firefly algorithm (FFA). The simulation results show that the operating cost can be reduced by 61.5% while the total cost can be reduced by 22.3% as compared to the original design for a shell and tube heat exchanger of heat duty 434 MW. The comparison of the obtained results with other algorithms indicates that the GSA algorithm can be successfully applied for design optimization of a shell and tube heat exchanger from economic point of view. (C) 2016 Elsevier Ltd. All rights reserved.
gravitational search algorithm (GSA) is an optimization algorithm inspired from Newton's law of gravitation. Moth flame optimization (MFO) is another optimization algorithm, motivated by the locomotion of moths ar...
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ISBN:
(纸本)9781509064359
gravitational search algorithm (GSA) is an optimization algorithm inspired from Newton's law of gravitation. Moth flame optimization (MFO) is another optimization algorithm, motivated by the locomotion of moths around a light source. Both of these algorithms have tried to model the search agents and altered properties like mass, gravitational constant, fitness, location, etc. in order to find the most optimal value. Optimization algorithms usually solve only a class of problems and therefore the search for a faster and more comprehensive algorithm is always on. By hybridizing MFO and GSA, the performance is expected to improve across various measures. This paper presents a hybrid optimization algorithm by using concepts of moth flame optimization and gravitational search algorithm and applies this hybrid algorithm to image segmentation. An optimized K-means algorithm and an optimized thresholding algorithm have been proposed. The results of the segmentation are then used to classify apples into different classes.
Analysis of medical data for disease prediction requires efficient feature selection techniques, as the data contains a large number of features. Researchers have used evolutionary computation (EC) techniques like gen...
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Analysis of medical data for disease prediction requires efficient feature selection techniques, as the data contains a large number of features. Researchers have used evolutionary computation (EC) techniques like genetic algorithms, particle swarm optimization etc. for FS and have found them to be faster than traditional techniques. We have explored a relatively new EC technique called gravitational search algorithm (GSA) for feature selection in medical datasets. This wrapper based method, that we have employed, using GSA and k-nearest neighbors reduces the number of features by an average of 66% and considerably improves the accuracy of prediction. (C) 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 7th International Conference on Advances in Computing & Communications.
In this paper, an improved gravitational search algorithm, revolving gravitational search algorithm(RGSA) is proposed. RGSA optimizes the selection of in GSA by importing revolving operator, lets agents not included i...
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In this paper, an improved gravitational search algorithm, revolving gravitational search algorithm(RGSA) is proposed. RGSA optimizes the selection of in GSA by importing revolving operator, lets agents not included in have opportunity to influence other agents' movement. RGSA could further exploit potential districts and strengthen the ability of exploration than GSA. Experiments confirm the high efficiency of RGSA.
The basic gravitational search algorithm(GSA) could fall into local optima solution easily, and thus we proposed a hybrid gravitational search algorithm(HGSA) in order to overcome the shortcoming of GSA. This improved...
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The basic gravitational search algorithm(GSA) could fall into local optima solution easily, and thus we proposed a hybrid gravitational search algorithm(HGSA) in order to overcome the shortcoming of GSA. This improved gravitational search algorithm, which only uses the position update formula that was affected by the Gbest in its iteration process, is combined with the Differential Evolution(DE) algorithm. Ten benchmark functions have been introduced for testing the improved algorithms' performance. We also use the statistical method "T-test" to verify the difference in results. Experimental results show that HGSA is superior to the basic gravitational search algorithm and its three improved algorithms in terms of both convergence accuracy and convergence rate.
The congestion is a vital concern for the independent system operator in the deregulated energy market because it entails additional cost and poses a threat to power system security. To relieve congestion, this paper ...
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The congestion is a vital concern for the independent system operator in the deregulated energy market because it entails additional cost and poses a threat to power system security. To relieve congestion, this paper presents optimal power flow (OPF) and available transfer capability (ATC) based methods for allocating thyristor controlled series compensator (TCSC) using the congestion rent contribution approach based on location marginal price. The dc power transfer distribution factors are utilized to compute ATC in bilateral transactions environment. The gravitationalsearch assisted algorithm is employed to manage congestion in a pool electricity market. Its performance is compared with OPF based on other heuristics. The congestion management is investigated for normal and contingency due to line outage and abrupt load variation. The efficacy of both the methods is investigated on IEEE 30-bus and IEEE 57-bus test systems.
This paper introduces gravitational search algorithm (GSA) to find numerical solutions of Diophantine equations, for which there exists no general method of finding solutions. This algorithm finds upon introducing ran...
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This paper introduces gravitational search algorithm (GSA) to find numerical solutions of Diophantine equations, for which there exists no general method of finding solutions. This algorithm finds upon introducing randomization concept along with the two of the four primary parameters 'velocity' and 'gravity' in physics. The performance of this algorithm has been evaluated on a set of random values. Computed result shows that the gravitational search algorithm - based heuristic is capable of producing high quality solutions, can offer many solutions of such equations.
To improve effluent quality of a wastewater treatment plant (WWTP), an optimized model order reduction (MOR) for the high order WWTP system is proposed. A high order model may lead to inefficient analysis of the syste...
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ISBN:
(纸本)9789811065026;9789811065019
To improve effluent quality of a wastewater treatment plant (WWTP), an optimized model order reduction (MOR) for the high order WWTP system is proposed. A high order model may lead to inefficient analysis of the system and can be computationally expensive. Hence, an accurate and suitable reduced order model needs to be obtained. In this research, an optimized MOR algorithm is proposed by the combination of Frequency Domain Gramian based Model Reduction (FDIG) and Singular Perturbation Approximation (SPA). To reduce the high order model to lower order model with minimum reduction error, optimization techniques of Firefly algorithm (FFA) and gravitational search algorithm (GSA) is applied. To show the effectiveness of the proposed technique, a case study on WWTP is utilized. From the results obtained, the optimized reduced order models obtained is a 9th order system which yield the lowest reduction error while preserving the stability of the original system.
Aiming at the shortcomings that the gravitational search algorithm (GSA) is easy to fall into the local optima, this paper proposes a simplified gravitational search algorithm (SGSA). This improved gravitational searc...
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ISBN:
(纸本)9781538606124
Aiming at the shortcomings that the gravitational search algorithm (GSA) is easy to fall into the local optima, this paper proposes a simplified gravitational search algorithm (SGSA). This improved gravitational search algorithm has the characteristics of faster optimization process and better convergence accuracy for solving unconstrained optimization problems. In the search process, SGSA discards the velocity and only performs the particles' position update including the particles acceleration. Ten benchmark functions are used to verify the performance of the SGSA algorithm, and the experimental results show that SGSA is better than the other four approaches with different improvement strategies for most cases.
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