This paper presents an effective hybrid evolutionary approach for multi-objective optimisation of reinforced concrete (RC) retainingwalls. The proposed algorithm combines an adaptive gravitational search algorithm (AG...
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This paper presents an effective hybrid evolutionary approach for multi-objective optimisation of reinforced concrete (RC) retainingwalls. The proposed algorithm combines an adaptive gravitational search algorithm (AGSA) with pattern search (PS) called AGSA-PS. In the resulting hybrid approach, the PS algorithm is employed as a local searchalgorithm around the global solution found by AGSA. The proposed algorithm was tested on a set of five well-known benchmark functions and simulation results demonstrate the superiority of the new method compared with the standard algorithm. Thereafter, the proposed AGSA-PS is applied for multi-objective optimisation of RC retaining walls. Two objective functions include total cost and embedded CO2 emissions of retaining wall are considered. The reliability and efficiency of the AGSA-PS for multi-objective optimisation of retaining structures are investigated by considering two design examples of retaining walls. Experimental results demonstrate that the resulting algorithm has high viability, accuracy and significantly outperforms the original algorithm and some other methods in the literature.
A good trade-off between exploration and exploitation to find optimal values in searchalgorithms is very hard to achieve. On the other hand, the combination of search methods may cause computational complexity increa...
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A good trade-off between exploration and exploitation to find optimal values in searchalgorithms is very hard to achieve. On the other hand, the combination of search methods may cause computational complexity increase problems. The gravitational search algorithm (GSA) is a swarm optimization algorithm based on the law of gravity, where the solution search process depends on the velocity of particles. The application of intelligent techniques can improve the search performances of GSA. This paper proposes the design of a Neuro and Fuzzy gravitational search algorithm (NFGSA) to achieve better results than GSA in terms of global optimum search capability and convergence speed, without increasing the computational complexity. Both the algorithms have the same computational complexity O(nd), where n is the number of agents and d is the search space dimension. The main task of the designed intelligent system is to adjust a GSA parameter on a revised version of GSA. NFGSA is compared with GSA, a Plane Surface gravitational search algorithm (PSGSA) and a Modified gravitational search algorithm (MGSA). The results show that NFGSA improves the optimization performances of GSA and PSGSA, without adding computational costs. Moreover, the proposed algorithm is better than MGSA for a benchmark function and achieves similar results for two test functions. The analysis on the computational complexity shows that NFGSA has a better computational complexity than MGSA, because NFGSA has complexity O(nd), whereas MGSA has complexity O((nd)(2)). (C) 2018 Elsevier Ltd. All rights reserved.
Clustering is a key activity in numerous data mining applications such as information retrieval, text mining, image segmentation. Clustering also plays a major role in medical image processing. Manual image segmentati...
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Clustering is a key activity in numerous data mining applications such as information retrieval, text mining, image segmentation. Clustering also plays a major role in medical image processing. Manual image segmentation is very tedious and time consuming task and the results of manual segmentation are subjected to errors due to huge and varying data. Therefore, automated segmentation systems are gaining enormous importance nowadays. This paper presents an automated system for segmentation of brain tissues namely white matter, gray matter and cerebrospinal fluid from brain MRI images. In this work, we propose a novel clustering approach, Fuzzy-gravitational search algorithm(GSA) for MRI brain image segmentation. The proposed approach is based on GSA, and uses fuzzy inference rules for controlling the parameter alpha as search progresses. The results of the system are compared with GSA and recent work on brain image segmentation algorithms for both real and simulated database on the basis of Dice Coefficient values. The performance of the Fuzzy-GSA algorithm is also evaluated against four benchmark datasets from the UC Irvine repository. The results illustrate that the Fuzzy-GSA approach attains the highest quality clustering over the selected datasets when compared with several other clustering algorithms.
A Mixed-Strategy based gravitational search algorithm (MS-GSA) is proposed in this paper, in which three improvement strategies are mixed and integrated in the standard GSA to enhance the optimization ability. The fir...
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A Mixed-Strategy based gravitational search algorithm (MS-GSA) is proposed in this paper, in which three improvement strategies are mixed and integrated in the standard GSA to enhance the optimization ability. The first improvement strategy is introducing elite agent's guidance into movement function to accelerate convergence speed. The second one is designing an adaptive gravitational constant function to keep a balance between the exploration and exploitation in the searching process. And the third improvement strategy is the mutation strategy based on the Cauchy and Gaussian mutations for overcoming the shortages of premature. The MS-GSA has been verified by comparing with 7 popular meta-heuristics algorithms on 23 typical basic benchmark functions and 7 CEC2005 composite benchmark functions. The results on these benchmark functions show that the MS-GSA has achieved significantly better performance than other algorithms. The effectiveness and significance of the results have been verified by Wilcoxon's test. Finally, the MS-GSA is employed to solve the parameter identification problem of Hydraulic turbine governing system (HTGS). It is shown that the MS-GSA is able to identify the parameters of HTGS effectively with higher accuracy compared with existing methods. (C) 2016 Elsevier B.V. All rights reserved.
On the basis of the theoretical analysis of a single-machine infinite-bus (SMIB), using the modified linearized Phil- lips-Heffron model installed with unified power flow controller (UPFC), the potential of the UP...
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On the basis of the theoretical analysis of a single-machine infinite-bus (SMIB), using the modified linearized Phil- lips-Heffron model installed with unified power flow controller (UPFC), the potential of the UPFC supplementary controller to enhance the dynamic stability of a power system is evaluated by measuring the electromechanical controllability through singular value decomposition (SVD) analysis. This controller is tuned to simultaneously shift the undamped electromeehanical modes to a prescribed zone in the s-plane. The problem of robust UPFC based damping controller is formulated as an optimization problem according to the eigenvalue-based multi-objective function comprising the damping factor, and the damping ratio of the undamped electromechanical modes to be solved using gravitational search algorithm (GSA) that has a strong ability to find the most optimistic results. The different loading conditions are simulated on a SMIB system and the rotor speed deviation, internal voltage deviation, DC voltage deviation and electrical power deviation responses are studied with the effect of this flexible AC transmission systems (FACTS) controller. The results reveal that the tuned GSA based UPFC controller using the proposed multi-objective function has an excellent capability in damping power system with low frequency oscillations and greatly enhances the dynamic stability of the power systems.
Wireless sensor networks have a short network lifetime due to limited battery life. External power supplies are utilised to extend the life of sensor nodes. The previous works of charging scheduling lack charging effi...
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Wireless sensor networks have a short network lifetime due to limited battery life. External power supplies are utilised to extend the life of sensor nodes. The previous works of charging scheduling lack charging efficiency, resulting in early node energy exhaustion. There are certain limitations in designing the scheduling path when sensor nodes consume diversified energy. Previous charging schedule approaches or charging path designs aimed to reduce the mobile charger's travel distance as well as the charging time delay. In this paper, we show how to build a charging path that reduces not only the mobile charger's maximum working time, but also the charging time delay or charging latency, as well as the mobile charger's or Mobile Charging Vehicle's (MCV) travel distance . Here first we cluster the sensor nodes based on their location, then we derive a path using gravitational search algorithm inside the clusters. We stimulate the proposed work and compare the working time of the MCV with some existing algorithms to show the efficiency.
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 con...
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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.
In recent years, metaheuristic algorithms have emerged as a promising approach to solve clustering and classification problems. In this paper, gravitational search algorithm (GSA) which is one of the newest swarm base...
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In recent years, metaheuristic algorithms have emerged as a promising approach to solve clustering and classification problems. In this paper, gravitational search algorithm (GSA) which is one of the newest swarm based metaheuristic search techniques, is adapted to generate prototypes for nearest neighbor classification. The proposed method has been tested on several problems and the results are compared with those obtained by several state-of-the-art techniques. The comparison shows that our proposed method can achieve higher classification accuracy than the competing methods and has good performance in the field of prototype generation. (C) 2015 Elsevier B.V. All rights reserved.
In this paper, a novel hybrid population-based meta-heuristic algorithm, called the hybrid Phasor Particle Swarm Optimization and gravitational search algorithm (PPSOGSA), is proposed to solve the problem of optimal p...
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In this paper, a novel hybrid population-based meta-heuristic algorithm, called the hybrid Phasor Particle Swarm Optimization and gravitational search algorithm (PPSOGSA), is proposed to solve the problem of optimal placement and sizing of inverter-based distributed generation (DG) units and shunt capacitors in radial distribution systems with linear and non-linear loads. The objective of the problem is reduction of active power losses considering constraints of the fundamental frequency active and reactive power balance, RMS voltage, and total harmonic distortion of voltage (THDV) at each bus of the network, as well as the branch flow constraints. The performance of the PPSOGSA-based approach is evaluated on the standard IEEE 33- and 69-bus test systems under sinusoidal and non-sinusoidal operating conditions. Compared to the original PPSO and GSA and other algorithms commonly used in the optimal sitting and sizing problem of DG units and shunt capacitors, it is found that the proposed algorithm has yielded better results.
In this study, a multiobjective environmental economic power dispatch problem has been converted into a single-objective optimization problem using the weighted sum method. For the solution of the converted problem, t...
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In this study, a multiobjective environmental economic power dispatch problem has been converted into a single-objective optimization problem using the weighted sum method. For the solution of the converted problem, the gravitational search algorithm (GSA), which is one of the latest algorithms, has been used. In order to increase the performance of the GSA, opposite positioning quality has been added to the structure of the algorithm (OGSA). The obtained results show that the proposed algorithm has obtained better results and has provided a faster convergence. The 30-bus 6-generator test system has been selected for application of the OGSA. The transmission line losses have been added to the problem by using a B loss matrix. Optimum solutions of the problem have been obtained for different weights and the results have been verified.
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