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Comparative analysis of three soft computing techniques for optimizing reservoir rule curves

WATER PRACTICE AND TECHNOLOGY 2025年第3期20卷 714-731页

作者： Anvari, Sedigheh Moghaddasi, Mahnoosh Rashedi, Esmat El-Shafie, Ahmed Grad Univ Adv Technol Inst Sci & High Technol & Environm Sci Dept Ecol Kerman Iran Arak Univ Fac Agr & Environm Dept Water Sci & Engn Arak Iran Arak Univ Res Inst Water Sci & Engn Arak Iran Grad Univ Adv Technol Fac Elect & Comp Engn Kerman Iran United Arab Emirates Univ Natl Water & Energy Ctr POB 15551 Al Ain U Arab Emirates

Reservoir rule curves (RCs) are crucial for guiding operators on the optimal water release based on the available water at the start of each month. In the absence of RCs, simulation and optimization techniques can be effectively employed to develop these curves. This study evaluates the performance of various optimization techniques for deriving optimal reservoir RCs for the Zarrineh Rud reservoir using soft computing (SC) algorithms. The algorithms investigated include the genetic algorithm (GA), particle swarm optimization (PSO), and gravitational search algorithm (GSA). To this end, monthly demand and discharge data from 1987 to 2018 were collected. Historical RCs were first simulated using the sequent peak algorithm (SPA), and optimal RCs were subsequently derived through the GA-SPA, PSO-SPA, and GSA-SPA algorithms to minimize water shortages. The results indicated that the GSA-SPA generally improved the time-based (alpha t) and volume-based (alpha v) reliability indices by 3 and 2%, respectively, compared to the historical SPA (SPA-Hist). Additionally, simulations with the GSA-SPA significantly reduced the mean annual shortage and total shortage by approximately 8% compared to SPA-Hist. The PSO-SPA ranked second, with a 7.4 and 6.8% reduction in mean annual shortage and total shortage, respectively.

关键词： gravitational search algorithm particle swarm optimization rule curves Zarrineh Rud

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MODIFIED gravitational search algorithm WITH PARTICLE MEMORY ABILITY AND ITS APPLICATION

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2013年第11期9卷 4531-4544页

作者： Gu, Binjie Pan, Feng Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ 1800 Lihu Rd Wuxi 214122 Peoples R China

gravitational search algorithm (GSA) is a type of optimization algorithm based on the law of gravity and mass interactions, which is lacking of memory ability. To enhance particle memory ability and search accuracy of GSA, a modified GSA (MGSA) is developed. MGSA adopts the idea of local optimum solution and global optimum solution from particle swarm optimization (PSO) algorithm into GSA. Furthermore, the convergence property of MGSA is analyzed. The performance of MGSA has been evaluated on 12 standard benchmark functions, and the results were compared with GSA. The obtained experimental results verified the effectiveness of MGSA in solving high dimensional benchmark functions. Additionally, to test MGSA performance in practical issue, MGSA is applied into support vector machine (SVM) parameter settings, the results showed that suitable SVM parameters could be effectively found by MGSA.

关键词： gravitational search algorithm Particle swarm optimization Particle memory ability Benchmark function Support vector machine classification

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LEARNING WEIGHTS OF FUZZY RULES BY USING gravitational search algorithm

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2013年第4期9卷 1593-1601页

作者： Kaya, Ersin Kocer, Baris Arslan, Ahmet Selcuk Univ Fac Engn & Architecture Dept Comp Engn TR-42075 Konya Turkey

The fuzzy rules are the most important factor which affects the success in fuzzy rule-based systems. Performance of a fuzzy rule-based classifier can be improved by weighting fuzzy rules. There are different methods for weighting fuzzy rules. In this study, weights of fuzzy rules which are generated from datasets in fuzzy rule-based classifiers are determined by using gravitational search algorithm. gravitational search algorithm is a new algorithm for optimization problems. The aim of the GSA in the proposed method is to find rule weight values which maximize classification accuracy. The proposed method was tested with six different data sets. The simulation results are compared by the other method. The proposed method mostly provided better results than the other method compared.

关键词： Learning Fuzzy rule-based classification gravitational search algorithm Weighting fuzzy rules

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gravitational search algorithm and Lyapunov Theory based Stable Adaptive Fuzzy Logic Controller

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Procedia Technology 2013年 10卷 581-586页

作者： Ananya Roy Kaushik Das Sharma Department of Electrical Engineering Bengal Engineering and Science University Shibpur India Department of Applied Physics University of Calcutta 92 APC Road Kolkata-700009 India

In this paper a new hybrid design methodology for stable adaptive fuzzy controllers for a non-linear system is proposed. The proposed design strategy utilizes the gravitational search algorithm (GSA) based heuristic global search technique and Lyapunov theory based local adaptation. The objective is to design a self-adaptive fuzzy controller, optimizing the free parameters of the fuzzy controller, so that the designed controller can guarantee desired stability. The GSA based design and hybrid GSA- Lyapunov concurrent design methodologies are implemented for a nonlinear plant with different reference signals and the results demonstrate the superiority of proposed hybrid design methodology.

关键词： gravitational search algorithm Lyapunov Theory Adaptive Fuzzy Logic Controller

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Advanced Image Processing Techniques for Medical Image Retrieval Using Visual Features and Distance Measures

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2024年第10期33卷 2450103-2450103页

作者： Krishnamoorthy, Sujatha Lakshmi, S. Venkata Janet, J. Sundararaj, Vinu Sri Krishna Coll Engn & Technol Coimbatore 641008 Tamil Nadu India Revire Intelligence LLP Eraviputhoorkadai Tamil Nadu India

Recently, there has been a rapid increase in the volume of medical image repositories because of the maximized utilization of digitized image data in hospitals. As a result, there is a complexity in querying and handling such large databases, which has led to the development of a novel Content-Based Medical Image Retrieval (CBMIR) technique. In this work, the CBMIR technique is developed using three types of visual features (i.e., color, texture and shape) along with 12 distance measures, and is optimized with the Crossover-gravitational search algorithm (CRO-GSA). For ease, we named this technique Content-Based Medical Image Retrieval using Visual features with CRO-GSA (CBMIRVC). The initial step of our CBMIRVC technique is to extract three types of visual features from the images. Then, each type of feature is employed with a suitable distance measure, which is used for the computation of image similarities between the medical images in the database and a provided query image. The optimization of our proposed CBMIRVC technique is done by the CRO-GSA algorithm. This algorithm optimizes the CBMIRVC technique by identifying the optimal combinations of visual features and similarity measures. Additionally, optimal weights are computed in accordance with the three types of features for the three similarities. Experimental validations were done using retrieved cancer-related and endoscopic color images

关键词： Content-based medical image retrieval weights similarities visual features crossover gravitational search algorithm

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Constrained Optimization Using gravitational search algorithm

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NATIONAL ACADEMY SCIENCE LETTERS-INDIA 2013年第5期36卷 527-534页

作者： Yadav, Anupam Deep, Kusum Indian Inst Technol Roorkee Dept Mathemt Roorkee Uttar Pradesh India

gravitational search algorithm is a newly born metaheuristic which is inspired by the working force of attraction between two masses. In this article gravitational search algorithm is employed to solve the constrained optimization problems. A variety of state-of-the-art benchmark problems are taken into account to justify the performance of the gravitational search algorithm. The results of gravitational search algorithms are compared with two state-of-the-art Particle Swarm Optimization algorithms in various aspects. Pairwise one tailed t test is applied to justify the statistical significance of the results and time complexity analysis is also performed. Finally the conclusions are drawn based on the experimental results.

关键词： gravitational search algorithm Particle swarm optimization Constrained optimization

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Development of an integrated machine learning model for rheological behaviours and compressive strength prediction of self-compacting concrete incorporating environmental-friendly materials

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STRUCTURAL ENGINEERING AND MECHANICS 2023年第2期86卷 181-195页

作者： Hadi, Pouryan Ashkan, KhodaBandehLou Peyman, Hamidi Fedra, Ashrafzadeh Islamic Azad Univ Dept Civil Engn Urmia Branch Orumiyeh Iran

To predict the rheological behaviours along with the compressive strength of self-compacting concrete that incorporates environmentally friendly ingredients as cement substitutes, a comparative evaluation of machine learning methods is conducted. To model four parameters, slump flow diameter, L-box ratio, V-funnel time, as well as compressive strength at 28 days-a complete mix design dataset from available pieces of literature is gathered and used to construct the suggested machine learning standards, SVM, MARS, and Mp5-MT. Six input variables-the amount of binder, the percentage of SCMs, the proportion of water to the binder, the amount of fine and coarse aggregates, and the amount of superplasticizer are grouped in a particular pattern. For optimizing the hyper-parameters of the MARS model with the lowest possible prediction error, a gravitational search algorithm (GSA) is required. In terms of the correlation coefficient for modelling slump flow diameter, L -box ratio, V-funnel duration, and compressive strength, the prediction results showed that MARS combined with GSA could improve the accuracy of the solo MARS model with 1.35%, 11.1%, 2.3%, as well as 1.07%. By contrast, Mp5-MT often demonstrates greater identification capability and more accurate prediction in comparison to MARS-GSA, and it may be regarded as an efficient approach to forecasting the rheological behaviors and compressive strength of SCC in infrastructure practice.

关键词： compressive strength gravitational search algorithm machine learning rheological properties self-compacting concrete

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Numerical investigation and optimisation of flat plate solar collectors using two swarm-based metaheuristic algorithms

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ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS 2023年第1期156卷 78-89页

作者： Maji, Ambarish Deshamukhya, Tuhin Choubey, Gautam GMR Inst Technol Dept Mech Engn Rajam 532127 Andhra Pradesh India Barak Valley Engn Coll Dept Mech Engn Karimganj 788701 Assam India Inst Infrastructure Technol Res & Management Dept Mech & Aerosp Engn Ahmadabad 380026 Gujarat India

In the present work, four flat-plate solar collectors of similar dimensions have been simulated using the CFD approach. Model 1 has been considered as circular with a U-shaped zigzag channel, while model 2 is a squareshaped plate. In the 3rd model, the number of turns in the channel has been increased by keeping the geometry of the pipe constant, while in the 4th model, multiple straight channels have been incorporated instead of a single U-shaped zigzag channel. Three-dimensional simulations have been primarily done to understand the effect of channel geometry area and turnings on the collector's performance. A steady-state incompressible forced convective heat transfer analysis has been performed here. Water is used as a fluid in the channel to carry heat. The results of the numerical analysis led to the conclusion that model 3 is the best flat plate solar collector where turnings are maximum. With a maximum collector performance of 41.57, model 3 performs 36.7% better than model 1. Additionally, when water flows at a rate of 8 m/s inside the collector's channel, the rate of heat transfer in model 3 is 2.5% faster than in model 1. Moreover, a global optimization study has been performed using two swarm-based metaheuristic algorithms, gravitational search algorithm (GSA) and Firefly algorithm (FA), with the aim of determining good optimum values of the design variables that will maximize collector efficiency for model 3 at different flow velocities. For model 3, the percentage variation in collector efficiency between CFD and optimised findings using metaheuristic algorithms is found to be (a) 41.08% for v = 2 m/s;(b) 28.41% for v = 4 m/s;(c) 21.71% for v = 6 m/s;and (d) 13.09% for v = 8 m/s. Therefore, it is possible to anticipate that as flow velocity increases, there will be a steady decrease in the variation between computed and optimized results.

关键词： Flat plate solar collector CFD Collector efficiency gravitational search algorithm Firefly algorithm

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Optimal coordination of directional overcurrent relays using hybrid fractional computing with gravitational search strategy

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ENERGY REPORTS 2021年 7卷 7504-7519页

作者： Muhammad, Yasir Raja, Muhammad Asif Zahoor Shah, Muhammad Abid Ali Awan, Saeed Ehsan Ullah, Farman Chaudhary, Naveed Ishtiaq Cheema, Khalid Mehmood Milyani, Ahmad H. Shu, Chi-Min COMSATS Univ Islamabad Dept Elect & Comp Engn Attock Campus Islamabad Pakistan Natl Yunlin Univ Sci & Technol Future Technol Res Ctr 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China King Abdulaziz Univ Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia Natl Yunlin Univ Sci & Technol Dept Safety Hlth & Environm Engn 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan

The protection system has a vital role in subsystems of the power network including the generation, transmission and distribution system. This system combines several types of protection relays including directional over current relay, differential relay, distance relay, earth faults relay, inverse definite minimum time (IDMT) relay for the protection of line, generators bus bars, transformers, line to ground faults and feeder lines of the power system. In this research paper, the overall performance of the protection system is improved by reducing the overall operational time of directional over current relays (DOCRs) through the optimal coordination between the primary and backup over current protective relays while keeping the time multiplier setting (TMS), pickup tap setting (PTS) and coordination time of interval (CTI) within the allowable limits. A new optimization strategy, namely the FPSOGSA, is developed by integrating the concept of fractional calculus inside the mathematical model of canonical particle swarm optimization and combining with traditional gravitational search algorithm to enhance the optimizer characteristics and tested on coordination problem of DOCRs in standard power systems including the IEEE 3, 8 and 15 bus networks. The results, yielded by proposed algorithm and their comparison with other well-known recently established counterparts, reveal that the synergy of FC, PSO and GSA enhance the performance of the optimizer by combining the individual strengths of these tools and finding the best global solution. The consistency, stability and reliability of FPSOGSA is endorsed by comprehensive statistical analysis based on the empirical cumulative distribution function, histograms, boxplot illustrations, quantile-quantile plots, minimum fitness evolution in each independent simulation. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： Computational intelligence Directional overcurrent relay Fractional calculus gravitational search algorithm Particle swarm optimization

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Analysis of optimized phase locked loop for grid synchronization of solar PV system under grid disturbances

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INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS 2023年第12期51卷 5840-5858页

作者： Jalan, Subham Kumar Babu, B. Chitti Indian Inst Informat Technol Design & Mfg PhotoVolta PV Res Lab Chennai India Indian Inst Informat Technol Design & Mfg PhotoVolta PV Res Lab Chennai 600127 India

In this work, an optimal phase locked loop (PLL) is employed to estimate the fundamental grid frequency and phase angle under grid disturbances, which largely reduces the frequency and phase error. Conventional synchronous reference frame (SRF) offers considerable results for balanced grid conditions but has poor performance under grid disturbances with improper phase detection. In this regard, gravitational search algorithm (GSA)-based optimization is utilized to optimally tune the proportional and integral (PI) gain values of conventional SRF PLL, which ensures the proper tracking of fundamental phase angle and frequency even during abnormal grid conditions, that is, voltage unbalance, frequency variations, high-frequency harmonics, and phase variations. Further, integral square error (ISE) criteria, settling time, and overshoot are considered as objective functions to obtain the optimal value of current gain, present in the control loop of a grid-tied solar photovoltaic (PV) system. The supremacy of the proposed work, compared to SRF and optimal SRF PLL, is tested with MATLAB & REG;/Simulink studies and verified by dSPACE-1104-based hardware-in-loop (HIL) results for real-time validation.

关键词： dSPACE-1104 gravitational search algorithm grid synchronization optimization phase locked loop solar PV system

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