Relevant bibliographies by topics / GRAVITATIONAL SEARCH ALGORITHM (GSA)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'GRAVITATIONAL SEARCH ALGORITHM (GSA)'

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Published: 26 October 2023

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Journal articles on the topic "GRAVITATIONAL SEARCH ALGORITHM (GSA)"

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Lenin, K. "MINIMIZATION OF REAL POWER LOSS BY ENHANCED GRAVITATIONAL SEARCH ALGORITHM." International Journal of Research -GRANTHAALAYAH 5, no. 7 (July 31, 2017): 623–30. http://dx.doi.org/10.29121/granthaalayah.v5.i7.2017.2171.

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In this paper, Enhanced Gravitational Search (EGS) algorithm is proposed to solve the reactive power problem. Gravitational search algorithm (GSA) results are improved by using artificial bee colony algorithm (ABC). In GSA, solutions are fascinated towards each other by applying gravitational forces, which depending on the masses assigned to the solutions, to each other. The heaviest mass will move slower than other masses and pull others. Due to nature of gravitation, GSA may pass global minimum if some solutions stuck to local minimum. ABC updates the positions of the best solutions that have obtained from GSA, preventing the GSA from sticking to the local minimum by its strong penetrating capability. The proposed algorithm improves the performance of GSA in greater level. In order to evaluate the performance of the proposed EGS algorithm, it has been tested on IEEE 57,118 bus systems and compared to other standard algorithms.
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Lenin, K. "A NOVEL HYBRIDIZED ALGORITHM FOR REDUCTION OF REAL POWER LOSS." International Journal of Research -GRANTHAALAYAH 5, no. 11 (November 30, 2017): 316–24. http://dx.doi.org/10.29121/granthaalayah.v5.i11.2017.2358.

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This paper proposes Hybridization of Gravitational Search algorithm with Simulated Annealing algorithm (HGS) for solving optimal reactive power problem. Individual position modernize strategy in Gravitational Search Algorithm (GSA) may cause damage to the individual position and also the local search capability of GSA is very weak. The new HGS algorithm introduced the idea of Simulated Annealing (SA) into Gravitational Search Algorithm (GSA), which took the Metropolis-principle-based individual position modernize strategy to perk up the particle moves, & after the operation of gravitation, Simulated Annealing operation has been applied to the optimal individual. In order to evaluate the efficiency of the proposed Hybridization of Gravitational Search algorithm with Simulated Annealing algorithm (HGS), it has been tested on standard IEEE 118 & practical 191 bus test systems and compared to the standard reported algorithms. Simulation results show that HGS is superior to other algorithms in reducing the real power loss and voltage profiles also within the limits.
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Rashedi, Esmat, Hossein Nezamabadi-pour, and Saeid Saryazdi. "GSA: A Gravitational Search Algorithm." Information Sciences 179, no. 13 (June 2009): 2232–48. http://dx.doi.org/10.1016/j.ins.2009.03.004.

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Shankar, Rajendran, Narayanan Ganesh, Robert Čep, Rama Chandran Narayanan, Subham Pal, and Kanak Kalita. "Hybridized Particle Swarm—Gravitational Search Algorithm for Process Optimization." Processes 10, no. 3 (March 21, 2022): 616. http://dx.doi.org/10.3390/pr10030616.

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The optimization of industrial processes is a critical task for leveraging profitability and sustainability. To ensure the selection of optimum process parameter levels in any industrial process, numerous metaheuristic algorithms have been proposed so far. However, many algorithms are either computationally too expensive or become trapped in the pit of local optima. To counter these challenges, in this paper, a hybrid metaheuristic called PSO-GSA is employed that works by combining the iterative improvement capability of particle swarm optimization (PSO) and gravitational search algorithm (GSA). A binary PSO is also fused with GSA to develop a BPSO-GSA algorithm. Both the hybrid algorithms i.e., PSO-GSA and BPSO-GSA, are compared against traditional algorithms, such as tabu search (TS), genetic algorithm (GA), differential evolution (DE), GSA and PSO algorithms. Moreover, another popular hybrid algorithm DE-GA is also used for comparison. Since earlier works have already studied the performance of these algorithms on mathematical benchmark functions, in this paper, two real-world-applicable independent case studies on biodiesel production are considered. Based on the extensive comparisons, significantly better solutions are observed in the PSO-GSA algorithm as compared to the traditional algorithms. The outcomes of this work will be beneficial to similar studies that rely on polynomial models.
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Kamaruzaman, Anis Farhan, Azlan Mohd Zain, Suhaila Mohamed Yusuf, and Noordin Mohd Yusof. "Gravitational Search Algorithm for Engineering: A Review." Applied Mechanics and Materials 815 (November 2015): 417–20. http://dx.doi.org/10.4028/www.scientific.net/amm.815.417.

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This paper presents a review on gravitational search algorithm (GSA). Nowadays, GSA has been used in various engineering studies such as production cost, production time, power consumption and emission. The GSA also mainly focuses to solve the problem related to optimization, modeling, scheduling and clustering. This paper also highlights the current researches using improved GSA.
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Siddique, Nazmul, and Hojjat Adeli. "Gravitational Search Algorithm and Its Variants." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 08 (July 17, 2016): 1639001. http://dx.doi.org/10.1142/s0218001416390018.

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Gravitational search algorithm (GSA) is a nature-inspired conceptual framework with roots in gravitational kinematics, a branch of physics that models the motion of masses moving under the influence of gravity. In GSA, a collection of objects interacts with each other under the Newtonian gravity and the laws of motion. The performances of objects are measured by masses. All these objects attract each other by the gravity force, while this force causes a global movement of all objects toward the objects with heavier masses. The position of the object corresponds to a solution of the problem. The positions of the objects are updated every iteration and the best fitness along with its corresponding object is stored. Heavier masses move slowly than lighter ones. The algorithm terminates after a specified number of iterations after which the best fitness becomes the global fitness for a particular problem and the positions of the corresponding object becomes the global solution of that problem. This paper presents a review of GSA and its variants.
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Kherabadi, Hossein Azadi, Sepehr Ebrahimi Mood, and Mohammad Masoud Javidi. "Mutation: A New Operator in Gravitational Search Algorithm Using Fuzzy Controller." Cybernetics and Information Technologies 17, no. 1 (March 1, 2017): 72–86. http://dx.doi.org/10.1515/cait-2017-0006.

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Abstract Gravitational Search Algorithm (GSA) isanovel meta-heuristic algorithm. Despite it has high exploring ability, this algorithm faces premature convergence and gets trapped in some problems, therefore it has difficulty in finding the optimum solution for problems, which is considered as one of the disadvantages of GSA. In this paper, this problem has been solved through definingamutation function which uses fuzzy controller to control mutation parameter. The proposed method has been evaluated on standard benchmark functions including unimodal and multimodal functions; the obtained results have been compared with Standard Gravitational Search Algorithm (SGSA), Gravitational Particle Swarm algorithm (GPS), Particle Swarm Optimization algorithm (PSO), Clustered Gravitational Search Algorithm (CGSA) and Real Genetic Algorithm (RGA). The observed experiments indicate that the proposed approach yields better results than other algorithms compared with it.
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Ali, Ahmed F., and Mohamed A. Tawhid. "Direct Gravitational Search Algorithm for Global Optimisation Problems." East Asian Journal on Applied Mathematics 6, no. 3 (July 20, 2016): 290–313. http://dx.doi.org/10.4208/eajam.030915.210416a.

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AbstractA gravitational search algorithm (GSA) is a meta-heuristic development that is modelled on the Newtonian law of gravity and mass interaction. Here we propose a new hybrid algorithm called the Direct Gravitational Search Algorithm (DGSA), which combines a GSA that can perform a wide exploration and deep exploitation with the Nelder-Mead method, as a promising direct method capable of an intensification search. The main drawback of a meta-heuristic algorithm is slow convergence, but in our DGSA the standard GSA is run for a number of iterations before the best solution obtained is passed to the Nelder-Mead method to refine it and avoid running iterations that provide negligible further improvement. We test the DGSA on 7 benchmark integer functions and 10 benchmark minimax functions to compare the performance against 9 other algorithms, and the numerical results show the optimal or near optimal solution is obtained faster.
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SIDDIQUE, Nazmul, and Hojjat ADELI. "APPLICATIONS OF GRAVITATIONAL SEARCH ALGORITHM IN ENGINEERING." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 22, no. 8 (November 25, 2016): 981–90. http://dx.doi.org/10.3846/13923730.2016.1232306.

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Gravitational search algorithm (GSA) is a nature-inspired conceptual framework with roots in gravitational kinematics, a branch of physics that models the motion of masses moving under the influence of gravity. In a recent article the authors reviewed the principles of GSA. This article presents a review of applications of GSA in engineering including combinatorial optimization problems, economic load dispatch problem, economic and emission dispatch problem, optimal power flow problem, optimal reactive power dispatch problem, energy management system problem, clustering and classification problem, feature subset selection problem, parameter identification, training neural networks, traveling salesman problem, filter design and communication systems, unit commitment problem and multiobjective optimization problems.
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Santra, D., A. Mukherjee, K. Sarker, and S. Mondal. "Hybrid Genetic Algorithm-Gravitational Search Algorithm to Optimize Multi-Scale Load Dispatch." International Journal of Applied Metaheuristic Computing 12, no. 3 (July 2021): 28–53. http://dx.doi.org/10.4018/ijamc.2021070102.

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Genetic algorithm (GA) and gravitational search algorithm (GSA) both have successfully been applied in solving ELD problems of electrical power generation systems. Each of these algorithms has their limitations and advantage. GA's global search and GSA's local search capability are their strong points while long execution period of GA and premature of convergence of GSA hinders the possibility of optimum result when applied separately in ELD problems. To mitigate these limitations, experiment is done for the first time by combining GA and GSA suitably and applying the hybrid in non-linear ELD problems of 6, 15, and 40 unit test systems. The paper reports the details of this study including comparative analysis considering similar hybrid algorithms. The result strongly attests the quality, consistency, and overall effectiveness of the GA-GSA hybrid in ELD problems.
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Dissertations / Theses on the topic "GRAVITATIONAL SEARCH ALGORITHM (GSA)"

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KUMAR, NEERAJ. "DESIGNING OF MARKET MODEL, EFFECTIVE PRICE FORECASTING TOOL AND BIDDING STRATEGY FOR INDIAN ELECTRICITY MARKET." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18910.

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The Development scenario for renewable energy across the globe is changing rapidly in terms of capacity addition and grid interconnection. Penetration of renewable energy resources into grid is necessary to meet the elevated demand of electricity. In view of this penetration of solar and wind power growing enormously across the globe. Solar energy is widely escalating in terms of generation and capacity addition due its better predictability over wind energy. Electricity pricing is one of the important aspects for power system planning and it felicitates information for the electricity bidder for exact electricity generation and resource allocation. The important task is to forecast the electricity price accurately in grid interactive environment. This task is tedious in renewable integrated market due to intermittency issue. As renewable energy penetration into the grid is enhancing swiftly. An appropriate market model addressing the issues of related to renewable energy specially wind and solar is necessary. A novel solar energy-based market model is proposed for state level market along with the operating mechanism. The different component associated with grid and their functionality in the operation of grid is discussed. Challenges and possible solutions are addressed to implement the market model. Energy trading plays a crucial role in the economic growth of country. Renewable energy trading opens a new avenue for the economic growth. India is blessed with a rich solar energy resources, the solar power producers tapped the potential of solar up to appreciable extent, but due to lack of trading models and specific regulatory mechanism in context of renewable energy generation is main hurdle in competition among generators. Various market model developed for solar energy trading at state level electricity along with their trading mechanism is presented. Also features of the models are also addressed. xiii A Rigorous literature review on price forecasting is conducted with focus on impact of solar and wind energy on electricity price. The data of Australia electricity market is collected for price forecasting. The correlation among the inputs for price is calculated using correlation coefficient formula and selected the highly corelated input with price. Artificial Neural Network (ANN) is implemented to forecast the price by using historical data. The price is predicted for January to June month and weekly forecast of price for the same month is executed. The minimum MAPE is 1.94 for April month and 1.03 for third week of January. The research work is continued to investigate the impact of solar and wind energy on electricity price. The Long short-term memory (LSTM) is designed to forecast the electricity price considering the solar power penetration. The raw data of Austria market consists of actual day ahead load, forecasted day ahead load, actual day ahead price and actual solar generation is used. The reliability of forecasting model is analyzed by computation of confidence interval on MAPE. The research work is extended to investigate the impact of wind energy on electricity price. The Austria electricity market data is used for investigating the potential impact of wind energy on rice. The statistical analysis of the data is conducted for finding the suitability of the model. Decision tree model is designed and implemented and significant reduction in the forecasting accuracy of 5.802 is achieved for the data set using wind energy as input parameter. The future of solar energy in India is positive. The growth of solar energy in terms of capacity addition and grid interconnection programme is expanding day by day. To promote the solar energy trading in open market a suitable bidding mechanism must be designed for solar power producers. It becomes pertinent to design the bidding strategy for solar power producers to maximize their profit considering the uncertainty in the energy output. Hybrid Particle Swarm Optimization – Gravitational Search Algorithm (HPSO - GSA) is proposed for designing the optimal bidding strategy for solar PV power producer for designed solar energy xiv based Indian electricity market. The objective function is designed considering the constraint of uncertainty and energy imbalance in price. The proposed algorithm shows highest profit when compared with Real Coded Genetic Algorithm (RCGA), Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). In the light of continual renewable energy growth and grid interconnection, a novel solar energy-based electricity market model addressing the issues of solar energy is proposed to make the system effective and reliable. This novel market may fill the promise of providing electricity at competitive cost for all in India. The various market models are proposed for trading the solar energy in competitive market for maximum utilization of untapped potential of solar energy. The various trading models may be implemented based on the application and suitability. The electricity price forecasting is an important aspects of power system planning and for renewable energy interactive grid price forecasting is crucial task due its intermittent nature. ANN model is proposed for price forecasting and significant improvement in MAPE is reported for Australia electricity market data. Further the investigation has been done on the impact of solar energy generation on electricity price using machine learning techniques (DT, RF, LASSO, XGBOOST and LSTM). The LSTM model accuracy is good in price forecasting with consideration of solar energy as input parameter. The investigation is extended for impact of wind energy on electricity price and Decision tree model accuracy is superior as compared to RF, LASSO, LR, SVR and DNN model. The bidding strategy for the designed solar based electricity model is proposed using HPSO-GSA method and profit calculation has been done for solar PV producers on real time data. The maximized profit has been obtained through HSPO-GSA method for two different sets of datasets.
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Drago, Marco. "Search for transient gravitational wave signals with unknown waveform in the LIGO-Virgo network of interferometric detectos using a fully coherent algorithm." Doctoral thesis, Università degli studi di Padova, 2010. http://hdl.handle.net/11577/3422378.

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Gravitational waves are space-time perturbations that are predicted by the General Relativity. Their weak interaction with the matter has neglect a direct detection, however, this has bring to a great effort from the scientific community to construct detectors with high sensitivity and to implement complicated algorithms of signal extraction from noise. For the goal of a detection, the community has realized that the use of more detectors make more realiable on the detection and allows to reconstruct better the waveform characteristics. In this thesis, we have studied a coherent algorithm on the LIGO-Virgo detectors, with the aim to improve the reconstruction performances and use it on the data referring to 2007 to estimate the presence of an interesting candidate as a gravitational wave
Le onde gravitazionali sono perturbazioni dello spazio tempo previste dalla relatività generale. La loro interazione debolissima con la materia ha impedito ad oggi una rilevazione, tuttavia ha causato un grande sforzo dalla comunità scientifica per la costruzione di rivelatori sofisticatissimi in grado di raggiungere sensibilità elevate e l'implementazione di algoritmi per estrarre il segnale gravitazionale dal rumore di fondo. Per questo obiettivo si è realizzato che l'uso di più rivelatori fornisce una stima più credibile sulla rivelazione e permette di ricostruire meglio le caratteristiche dell'onda. In questa tesi si è studiato un algoritmo di analisi coerente sulla rete di rivelatori LIGO-Virgo, allo scopo di migliorarne le prestazioni di ricostruzione e di utilizzarlo sul periodo di presa dati 2007 per estimare la presenza di un eventuale candidato nel suddetto periodo.
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KUMAR, ARVIND. "FUZZY CLUSTERING FOR COLOR IMAGE SEGMENTATION USING GRAVITATIONAL SEARCH ALGORITHM." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15400.

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Clustering is a key activity in numerous data mining applications such as information retrieval, text mining, image segmentation, etc. This research work proposes a clustering approach, Fuzzy-GSA, based on gravitational search algorithm (GSA). In the proposed Fuzzy-GSA approach, a fuzzy inference system is developed to effectively control the parameters of GSA. The performance of the Fuzzy-GSA algorithm is 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 namely, k-means, particle swarm optimization (PSO), gravitational search algorithm (GSA) and, combined gravitational search algorithm and k-means approach (GSA-KM) In this paper, we propose a new hybrid approach for image segmentation. The proposed approach exploits fuzzy GSA for clustering image pixels into homogeneous regions. In order to improve the performance of fuzzy clustering to cope with segmentation problems, we employ gravitational search algorithm which is inspired by Newton’s rule of gravity. Gravitational search algorithm is incorporated into fuzzy GSA to take advantage of its ability to find an optimum cluster center which minimizes the fitness function of fuzzy GSA. Experimental results show effectiveness of the proposed method in segmentation different types of images as compared to classical fuzzy Algorithm
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Chien-HungKuo and 郭建宏. "Fuzzy Gravitational Search Algorithm Based Image Zooming Interpolation Scheme." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/34a493.

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碩士
國立成功大學
電機工程學系
103
This thesis aims to apply fuzzy gravitation search algorithms to decrease the image zooming inconsistent condition. The image interpolation method distinguishes between the two categories: single frame and multi-frame. The latter is often used in visually, due to the continuous access to live images, so a real-time zooming effect can be achieved. The former is mostly used in repair, reconstruction and local pictures to enlarge the view. In the image scaling process, the hardest part is to increase or maintain the sharpness and smoothness of the image and to reduce the blurring. The proposed method is to modify the traditional linear interpolation method, and make use of the fuzzy gravitational search algorithm in order to achieve optimal compensation rate of pixel. Even if in high-magnification scaling, we still have a clear image. Simulation results demonstrate that the proposed scheme gives a higher peak-signal-to-noise ratio (PSNR) and shows a better images results in comparison with traditional method.
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Huan-JungChiu and 邱煥榮. "Gait Optimization of Biped Robot Based on Gravitational Search Algorithm." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/07858482778849153385.

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碩士
國立成功大學
電機工程學系專班
101
This thesis mainly applies the gravitational search algorithm (GSA) to optimize gait trajectory of a biped robot. In order to reduce the motor assembly costs and allow the motion training to being more convenient, a robotic simulator for biped robots is developed in the thesis, where MATLAB software is adopted. First, the gravitational search algorithm is compared with the particle swarm optimization (PSO) by testing three standard functions to confirm whether it performs better than PSO or not. Then, for a simplified two-dimensional five-link biped robot, the GSA can autonomously learn the gait trajectory suppose the fitness function is described by a quadratic summation of the differences between the sole trajectory of the biped robot and its desired one. Finally, for a three-dimensional eight-link biped robot, if the fitness function is defined by the specified center of mass of the robot, then GSA can successfully obtain a stable moving gait. All the simulation results illustrate that the GSA based gait generator can make biped robots walk smoothly and stably.
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Juvvala, Rambabu. "Economic Design of X-bar Control Chart Using Gravitational Search Algorithm." Thesis, 2015. http://ethesis.nitrkl.ac.in/7003/1/Economic_Juvvala_2015.pdf.

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Control chart is a major and one of most widely used statistical process control (SPC) tools. It is used to statistically monitor the process through sampling inspection. Control chart tells us when to allow the process to continue or avoid unnecessary adjustments with machine and when to take the corrective action. On to same problem either on the material side or from the operator side it is quite possible that either targeted value X-bar has changed or process dispersion has changed. These changes must be reflected on the control chart so that the corrective action can be taken. The use of control chart requires selection of three parameters namely sample size n, sampling interval h, and width of control limits k for the chart. Duncan developed a loss cost function for X-bar control chart with single assignable cause. The function has to be optimized using metaheuristic optimization technique. In the present project, the economic design of the X-bar control chart using Gravitational Search Algorithm (GSA) has been developed MATLAB software to determine the three parameters i.e. n , h and k such that the expected total cost per hour is minimized. The results obtained are found to be better than that reported in literature.
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Mandal, Abhijeet. "Gravitational Search Algorithm: A Novel Optimization for Economic Design in Discontinuous Model." Thesis, 2015. http://ethesis.nitrkl.ac.in/7812/1/2015_Gravitational_Mandal.pdf.

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Control charts are generally utilized to monitor and maintain the statistical control of a process. Designing a control chart means selection of three parameters such as sample size n, sampling interval h and width of control limits k. To maintain a control chart we have to incur various types of cost such as prevention costs, appraisal costs, internal failure costs, external failure costs and total cost. In economic design the objective is to minimize the total cost associated with control chart. Thus, the economic design is one type of unconstrained optimization problem. Economic designs of X-bar control chart for two types of manufacturing process models namely continuous and discontinuous is provided in the literature. In this project, gravitational search optimization has been utilized for the economic design of X-bar chart for discontinuous process. The results were observed to be comparable to that reported by the literature.
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Tsai, Ming-Yu, and 蔡明佑. "Estimation of Carrier Frequency Offsets for Uplink OFDMA Using Hybrid Particle Swarm Optimization and Gravitational Search Algorithm." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/69y3dw.

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碩士
國立臺北科技大學
電機工程系研究所
100
In orthogonal frequency division multiplexing (OFDM) system, carrier frequency offset (CFO) is an important factor that destroys orthogonality among subcarriers. This will lead to inter-carrier interference (ICI) and therefore significantly degrades system performance. Moreover, in the uplink orthogonal frequency division multiple access (OFDMA) system, the base station will receive multiple CFOs from multi-user which causes multiple access interference (MAI), further degrading system performance. Hence the research on CFO estimation has received much attention in recent years. This study develops an evolutionary algorithm by taking the advantages of both the particle swarm optimization (PSO) and gravitational search algorithm (GSA) for CFOs estimation in two different systems, which are null subcarrier-based system (System-1) and [21,22] system (System-2). A series of experiments is conducted over Rayleigh fading channel with various users and the results indicate that performance of the proposed hybrid scheme is superior to other methods. In addition, System-1 and System-2 have comparable performances, but System-1 has much higher subcarrier utilization efficiency.
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Tsai, Yen-Chuan, and 蔡嬿娟. "Using a revised gravitational search algorithm to find the optimal parameters for a nano-particle milling process." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/96617741639835419431.

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碩士
國立雲林科技大學
工業工程與管理系
102
In the early 21st century, the nanotechnology were gradually mature, many industries began using nanotechnology in their product. Thus, nanotechnology becomes an indispensable technology in many industries. In general, many researches commonly used Taguchi methods and algorithms to make the optimal decision. However, there would have errors between actual results and predictions which are calculated by Taguchi method. Therefore, many heuristic algorithms have been developed and used in industry. In all manufacturing process, the producers expect to offer the best quality products to customers and they would get good reputation. The aim of this study is to explore the optimal parameters of the manufacturing process. Due to heuristic algorithms still have some problems included the convergence rate and falling into local optimal solution, the problem of convergence into a local optimum need to be improved. Therefore, this study uses Gravitational Search Algorithm whch through the slow convergence and escape local optimum to find the optimal solution. Additionally, this study also uses the fuzzy and mutation methods to improve the gravitational search algorithm. We expected to get the optimal parameters to minimize the mean and variances of grain size in the nano-particle milling process. Finally, this study also compared the results of the Revised Gravitational Search Algorithm and other algorithms. The results of this study could provide researchers or engineers to make parameter settings as reference. These results also could help the industries to improve their manufacturing process.
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CHOU, YUEH-CHING, and 周岳慶. "Combining Particle Swarm Optimization, Gravitational Search Algorithm and Fuzzy Rule to Improve the Classification Performance for Feed-forward Neural Network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/28zh58.

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碩士
國立勤益科技大學
工業工程與管理系
107
The Feed-forward Neural Network (FNN) is a kind of artificial neural network and has been widely used in medical diagnosis, data mining, securities market analysis and other fields. In FNN, during the learning process, the goal is to find the best combination of connection weights and biases in order to achieve the minimum error. However, in many cases, FNNs converge to local optimum but not global optimum. How to optimize the connection weights and deviations to achieve the minimum error is one of the purposes of this study. This study is to use the University of California Irvine (UCI) mechanical learning database of chronic kidney disease (CKD) and mesothelioma (MES) disease as the research object of this study. The research method firstly preprocesses the database and normalizes the data so that the data is between -1 and 1. Using FNN to learn the feature　of each data, and using particle swarm optimization (PSO) and gravitational search algorithm (GSA) to optimize the weights and biases of FNN classifiers based on the algorithms inspired by observation of natural phenomena. In addition, referring to the FuzzyGSA proposed by González et al., the fuzzy rules is used to optimize the parameters of the GSA algorithm to improve the performance of the algorithm in the classifier. PSOGSA, proposed by Mirjalili et al., combines the social thinking ability (Gbest) in PSO with the local search ability of GSA. In this study, fuzzy rules are used to optimize the parameters of the PSOGSA algorithm, and FuzzyPSOGSA is proposed to accelerate the convergence of the algorithm in the later stage. In the part of the results, the learning errors generated by each algorithm in optimizing the classifier are discussed, and the proposed method is evaluated by using the confusion matrix. Our proposed method can provide doctors with a more accurate support decision-making system in the diagnosis of patients with CKD and MES cutaneous disease, to help doctors more accurately determine whether patients have diseases. In order to reduce the delay caused by mistakes in medical diagnosis and medical resources, doctors can reduce the waste of medical treatment and medical resources.
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Book chapters on the topic "GRAVITATIONAL SEARCH ALGORITHM (GSA)"

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Kunche, Prajna, and K. V. V. S. Reddy. "Speech Enhancement Approach Based on Gravitational Search Algorithm (GSA)." In Metaheuristic Applications to Speech Enhancement, 61–75. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31683-3_6.

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González-Álvarez, David L., Miguel A. Vega-Rodríguez, Juan A. Gómez-Pulido, and Juan M. Sánchez-Pérez. "Applying a Multiobjective Gravitational Search Algorithm (MO-GSA) to Discover Motifs." In Advances in Computational Intelligence, 372–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21498-1_47.

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Lalwani, Praveen, Haider Banka, and Chiranjeev Kumar. "GSA-CHSR: Gravitational Search Algorithm for Cluster Head Selection and Routing in Wireless Sensor Networks." In Applications of Soft Computing for the Web, 225–52. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7098-3_13.

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Xing, Bo, and Wen-Jing Gao. "Gravitational Search Algorithm." In Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms, 355–64. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03404-1_22.

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Siddique, Nazmul, and Hojjat Adeli. "Gravitational Search Algorithm." In Nature-Inspired Computing, 51–118. Boca Raton : CRC Press, 2017.: Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315118628-2.

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Hashemi, Amin, Mohammad Bagher Dowlatshahi, and Hossein Nezamabadi-Pour. "Gravitational Search Algorithm." In Handbook of AI-based Metaheuristics, 119–50. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003162841-7.

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Zolghadr-Asli, Babak. "Gravitational Search Algorithm." In Computational Intelligence-based Optimization Algorithms, 236–50. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003424765-15.

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Gupta, Aditi, Nirmala Sharma, and Harish Sharma. "Exploitative Gravitational Search Algorithm." In Advances in Intelligent Systems and Computing, 163–73. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3322-3_15.

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Rawal, Pragya, Harish Sharma, and Nirmala Sharma. "Fast Convergent Gravitational Search Algorithm." In Algorithms for Intelligent Systems, 1–12. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0426-6_1.

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de Moura Oliveira, P. B., Josenalde Oliveira, and José Boaventura Cunha. "Trends in Gravitational Search Algorithm." In Distributed Computing and Artificial Intelligence, 14th International Conference, 270–77. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62410-5_33.

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Conference papers on the topic "GRAVITATIONAL SEARCH ALGORITHM (GSA)"

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"A Brief Analysis of Gravitational Search Algorithm (GSA) Publication from 2009 to May 2013." In International Conference Recent treads in Engineering & Technology. International Institute of Engineers, 2014. http://dx.doi.org/10.15242/iie.e0214511.

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Rohmah, Dewi Syifaur, S. Dewi Retno Sari, and K. Vika Yugi. "Clustering human development index data with gravitational search algorithm-fuzzy 4-means (GSA-F4M)." In THE THIRD INTERNATIONAL CONFERENCE ON MATHEMATICS: Education, Theory and Application. AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0039661.

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Pervez, Imran, Adil Sarwar, Mohammad Tayyab, and Mohammad Sarfraz. "Gravitational Search Algorithm (GSA) based Maximum Power Point Tracking in a Solar PV based Generation System." In 2019 Innovations in Power and Advanced Computing Technologies (i-PACT). IEEE, 2019. http://dx.doi.org/10.1109/i-pact44901.2019.8960130.

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Venkat, R., and K. Satyanarayan Reddy. "Dealing Big Data using Fuzzy C-Means (FCM) Clustering and Optimizing with Gravitational Search Algorithm (GSA)." In 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2019. http://dx.doi.org/10.1109/icoei.2019.8862673.

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Meziane, Rachid, Seddik Boufala, Amar Hamzi, and Mohamed Amara. "Preventive maintenance optimization in hybrid solar gas power system using gravitational search algorithm." In 2015 3rd International Renewable and Sustainable Energy Conference (IRSEC). IEEE, 2015. http://dx.doi.org/10.1109/irsec.2015.7455130.

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Debnath, Manoj Kumar, Ranjan Kumar Mallick, Sadhana Das, and Aditya Aman. "Gravitational search algorithm (GSA) optimized fuzzy-PID controller design for load frequency control of an interconnected multi-area power system." In 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT). IEEE, 2016. http://dx.doi.org/10.1109/iccpct.2016.7530205.

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A. Chagas, Elton, Anselmo B. Rodrigues, and Maria G. Silva. "Minimization of Risks of Voltage and Frequency Violations in Islanded Microgrids using Robust Probabilistic Optimal Power Flow." In Simpósio Brasileiro de Sistemas Elétricos - SBSE2020. sbabra, 2020. http://dx.doi.org/10.48011/sbse.v1i1.2386.

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The main aim of this paper is to propose a robust probabilistic optimal power flow model to determine the droop control parameters for the Distributed Generators (DG) of a islanded microgrid. The term robust is related to the droop control parameters being immune to uncertainties associated with: load forecast errors, DG outages and variability of power output in renewable DG. This optimization problem is solved by an improved gravitational search algorithm (GSA). The test results demonstrated that the proposed method can achieve significant reductions in the load curtailments due to frequency and voltage violations. In addition, a comparison between GSA and the Particle Swarm Optimization (PSO) demonstrated that GSA is more suitable for evaluating the droop control parameters than PSO in relation to the computational cost and the optimal quality of the solution.
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Katooli, Javad, Mohammad Ameri, and Mohammad Hadi Katooli. "The Application of a Microgrid Considering Wind Generation." In ASME 2015 9th International Conference on Energy Sustainability collocated with the ASME 2015 Power Conference, the ASME 2015 13th International Conference on Fuel Cell Science, Engineering and Technology, and the ASME 2015 Nuclear Forum. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/es2015-49787.

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Conventional power networks have experienced a gradual evolution from a centralized nature to distributed and localized structures. The upgrading of power system toward a smart grid is being developed to improve reliability and facilitate the integration of different types of renewable energies and improve load management. Due to different uncertainties linked to electricity supply in renewable MGs, probabilistic energy management techniques are going to be necessary to analyze the system. In this study, the short-term operation planning of a typical microgrid (MG) with diverse units for achieving the maximum profit, considering technical and economical constraints, for the next 24 hours, using gravitational search algorithm (GSA) with SPSS software is presented and the effect of wind generation in the planning is investigated. The MG consists of a diverse variety of power system components such as wind turbine, microturbine, photovoltaic, fuel cell, Hydrogen storage tank, reformer, a boiler, and electrical and thermal loads. Moreover, MG is connected to an electrical grid for exchange of power. The MG is managed and controlled through a central controller. The system costs include the operational cost, thermal recovery, power trade with the local grid, and hydrogen production costs. The system costs include the operational cost, thermal recovery, power trade with the local grid, and hydrogen production costs. Total obtained profit from the MG, considering with US electricity and natural gas prices is $5.312902×103.
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Saeidi-Khabisi, Fatemeh-sadat, and Esmat Rashedi. "Fuzzy gravitational search algorithm." In 2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE). IEEE, 2012. http://dx.doi.org/10.1109/iccke.2012.6395370.

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Gupta, Aditi, Nirmala Sharma, and Harish Sharma. "Accelerative gravitational search algorithm." In 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2016. http://dx.doi.org/10.1109/icacci.2016.7732328.

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