The focus of this paper is gravitational search algorithm which is a relatively new heuristics algorithm for function optimization. In order to improve the efficiency and reliability it was hybridized with real coded ...
详细信息
The focus of this paper is gravitational search algorithm which is a relatively new heuristics algorithm for function optimization. In order to improve the efficiency and reliability it was hybridized with real coded genetic algorithm and extensively applied to solve benchmarks problems available in literature. In the present paper, these hybridized variants are used to solve three constrained engineering design problem. The obtained results are compared with an extensively available results in literature. It is proved that the performance of one of the hybridized version outperform the remaining hybridized version as well as original gravitational search algorithm, in term of quality of solution and computation effort.
Volatility of load demand and electricity price has set a challenging task in the operational planning and controls of modern power system. Therefore, power utilities face challenge to serve the load demand at minimum...
详细信息
ISBN:
(纸本)9781509040001
Volatility of load demand and electricity price has set a challenging task in the operational planning and controls of modern power system. Therefore, power utilities face challenge to serve the load demand at minimum operating cost by pedorming a proper scheduling of the generating units. To perform such a task, unit commitment plays a vital role and significant amount cost is saved per year. In this paper, gravitational search algorithm (GSA) is utilized for Unit Commitment problem. Optimum scheduling of the generating units is obtained using GSA and Holomorphic Embedded Load Flow method for handling load ftow operation, generators real power output and network losses for each time period. IEEE 30-bus systems is considered to check the pedormance of the proposed method and simulation results are compared to other techniques available in the literature.
This paper presents optimal tuning of the controller parameters of a proportional-integral-derivate (PID) controller for an automatic voltage regulator (AVR) system using a heuristic gravitational search algorithm (GS...
详细信息
This paper presents optimal tuning of the controller parameters of a proportional-integral-derivate (PID) controller for an automatic voltage regulator (AVR) system using a heuristic gravitational search algorithm (GSA) based on mass interactions and Newton's law of gravity. The determination of optimal controller parameters is considered an optimization problem in which different performance indexes and a performance criterion in the time domain have been used as objective functions to test the performance and effectiveness of the GSA. In the determining process of the parameters, the designed PID controller with the proposed approach is simulated under different conditions and the performance of the controller is compared with those reported in the literature. From the numerical simulation results it is clear that the GSA approach is successfully applied to reveal the performance and the feasibility of the proposed controller in the AVR system.
The gravitational search algorithm (GSA) is categorized in swarm intelligence optimization techniques, based on the Newton's law of gravity and motion. In GSA, solution look process relays on the velocity, which i...
详细信息
ISBN:
(纸本)9781509047086
The gravitational search algorithm (GSA) is categorized in swarm intelligence optimization techniques, based on the Newton's law of gravity and motion. In GSA, solution look process relays on the velocity, which is an element of acceleration that decides the step size of the solutions. Due to this component, sometimes the global search process may skip the global optima. So, to avoid this situation, this paper presents a local exploitation based gravitational search algorithm (LEGSA), in which acceleration represented in the form of gravitational field and it reduces iteratively. Due to this, solutions are motivated to exploit more desirable in search space. Further two control parameter, Kbest and gravitational constant are also modified, to give a proper balance amongst exploration and exploitation capabilities. The proposed LEGSA are examined on the 16 different benchmark functions. A Local Exploitation Based GSA (LEGSA) has obvious advantages in comparison with the traditional gravitational search algorithm (GSA) and Biogeography-based optimization (BBO) algorithm.
gravitational search algorithm (GSA) is a simple well known meta-heuristic searchalgorithm based on the law of gravity and the law of motion. In this article, a new variant of GSA is introduced, namely Exploitative G...
详细信息
ISBN:
(数字)9789811033223
ISBN:
(纸本)9789811033223;9789811033216
gravitational search algorithm (GSA) is a simple well known meta-heuristic searchalgorithm based on the law of gravity and the law of motion. In this article, a new variant of GSA is introduced, namely Exploitative gravitational search algorithm (EGSA). In the proposed EGSA, two control parameters (Kbest and gravitational constant) are modified that play an important role in GSA. Gravitation constant G is reduced iteratively to maintain a proper balance between exploration and exploitation of the search space. Further, To enhance the searching speed of algorithm Kbest (best individuals) is exponentially decreased. The performance of proposed algorithm is measured in term of reliability, robustness and accuracy through various statistical analyses over 12 complex test problems. To show the competitiveness of the proposed strategy, the reported results are compared with the results of GSA, Fitness Based gravitational search algorithm (FBGSA) and Biogeography Based Optimization (BBO) algorithms.
Design of wideband and accurate Fractional Order Digital Differentiator (FODD) in terms of infinite impulse response (IIR) filter based on a metaheuristic optimization technique called gravitational search algorithm (...
详细信息
ISBN:
(纸本)9781509046669
Design of wideband and accurate Fractional Order Digital Differentiator (FODD) in terms of infinite impulse response (IIR) filter based on a metaheuristic optimization technique called gravitational search algorithm (GSA) is presented in this work. In comparison with the FODDs based on four other metaheuristics, the GSA based design consistently demonstrates a superior solution quality and an improved convergence rate. The proposed FODD also significantly outperforms the designs based on various classical and non-classical approaches reported in the literature.
The proposed new hybrid approach for data clustering is achieved by initially exploiting spatial fuzzy c-means for clustering the vertex into homogeneous regions. Further to improve the fuzzy c-means with its achievem...
详细信息
ISBN:
(纸本)9789811031564;9789811031557
The proposed new hybrid approach for data clustering is achieved by initially exploiting spatial fuzzy c-means for clustering the vertex into homogeneous regions. Further to improve the fuzzy c-means with its achievement in segmentation, we make use of gravitational search algorithm which is inspired by Newton's rule of gravity. In this paper, a modified modularity measure to optimize the cluster is presented. The technique is evaluated under standard metrics of accuracy, sensitivity, specificity, Map, RMSE and MAD. From the results, we can infer that the proposed technique has obtained good results.
Modern satellite imaging technology has resulted in an increased number of hyperspectral bands acquired by state-of-the-art sensors. It significantly advances the field of remote sensing. Owing to the increasing numbe...
详细信息
ISBN:
(纸本)9781509049516
Modern satellite imaging technology has resulted in an increased number of hyperspectral bands acquired by state-of-the-art sensors. It significantly advances the field of remote sensing. Owing to the increasing number of bands, the huge data quantity causes the curse of dimensionality and leads to the worse accuracy. It also increases the computational complexity exponentially as the problem size increases. It's therefore important to reduce dimensionality in order to prevent the curse of dimensionality. In this paper, a novel dimensionality reduction, named impurity function band prioritization method based on the particle swarm optimization and the gravitational search algorithms, is proposed to reduce the number of hyperspectral bands. The experimental results show that our approach can efficiently reduce dimensionality of hyperspectral data sets and significantly achieve a better classification accuracy compared to other methods.
Data clustering is a kind of data analysis techniques for grouping the set of data objects into clusters. To make use of the advantages of distance measure and nearest neighbor method, we present a hybrid data cluster...
详细信息
ISBN:
(数字)9783319607535
ISBN:
(纸本)9783319607535;9783319607528
Data clustering is a kind of data analysis techniques for grouping the set of data objects into clusters. To make use of the advantages of distance measure and nearest neighbor method, we present a hybrid data clustering algorithm based on GSA and DPC (GSA-DPC) algorithm. The optional clustering center set is selected by DPC algorithm. In turn, we optimize the clustering center set to achieve the best clustering distribution under the fame of GSA. Its performance is compared with four related clustering algorithms. The simulation results demonstrate the effectiveness of the presented algorithm.
In this paper a new approach for parameter adaptation is proposed, where a fuzzy system is implemented to dynamically change some parameters of the gravitational search algorithm (GSA), the idea of dynamically changin...
详细信息
ISBN:
(数字)9783319529417
ISBN:
(纸本)9783319529417;9783319529400
In this paper a new approach for parameter adaptation is proposed, where a fuzzy system is implemented to dynamically change some parameters of the gravitational search algorithm (GSA), the idea of dynamically changing the parameters of GSA come from the necessity of having a method that allows GSA to be implemented on any problem without the need to find the best values for each parameter, because the fuzzy system will do that for us. To properly adjust the parameters the fuzzy system depends on some metrics of GSA, like the percentage of iterations elapsed and the degree of dispersion of the agents from GSA on the search space, which are used in the proposed approach.
暂无评论