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Neural network and fuzzy system for the tuning of gravitational search algorithm parameters

EXPERT SYSTEMS WITH APPLICATIONS 2018年 102卷 234-244页

作者： Pelusi, Danilo Mascella, Raffaele Tallini, Luca Nayak, Janmenjoy Naik, Bighnaraj Abraham, Ajith Univ Teramo Fac Commun Sci Teramo Italy Sri Sivani Coll Engn Dept Comp Sc Engn Srikakulam India Veer Surendra Sai Univ Technol Dept Comp Applicat Burla Odisha India Machine Intelligence Res Labs MIR Labs Sci Networ Washington DC USA

A good trade-off between exploration and exploitation to find optimal values in search algorithms 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.

关键词： Computational intelligence Fuzzy systems gravitational search algorithm Neural networks search algorithms

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Fuzzy clustering using gravitational search algorithm for brain image segmentation

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第20期81卷 29633-29652页

作者： Hooda, Heena Verma, Om Prakash Delhi Technol Univ Dept IT Delhi India Delhi Technol Univ Dept ECE Delhi India

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.

关键词： Magnetic resonance imaging Brain image segmentation gravitational search algorithm Fuzzy clustering Fuzzy inference rules

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A mixed-strategy based gravitational search algorithm for parameter identification of hydraulic turbine governing system

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KNOWLEDGE-BASED SYSTEMS 2016年第0期109卷 218-237页

作者： Zhang, Nan Li, Chaoshun Li, Ruhai Lai, Xijie Zhang, Yuanchuan. Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China

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.

关键词： gravitational search algorithm Elite agent's guidance Adaptive gravitational constant function Hydraulic turbine governing system Parameter identification

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A multi-objective gravitational search algorithm based approach of power system stability enhancement with UPFC

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Journal of Central South University 2013年第6期20卷 1536-1544页

作者： Ajami Ali Armaghan Mehdi Electrical Engineering Department Azarbaijan Shahid Madani University Young Researchers Club Ardabil BranchIslamic Azad University

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.

关键词： unified power flow controller gravitational search algorithm power system stability

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A cluster based charging schedule for wireless rechargeable sensor networks using gravitational search algorithm

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WIRELESS NETWORKS 2022年第7期28卷 3323-3336页

作者： Rahaman, Sk Md Abidar Azharuddin, Md Aliah Univ Dept Comp Sci & Engn Kolkata 700156 India

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.

关键词： Wireless sensor networks Charging scheduling Clustering Lifetime Mobile charging vehicle gravitational search algorithm

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A hierarchical gravitational search algorithm with an effective gravitational constant

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SWARM AND EVOLUTIONARY COMPUTATION 2019年 46卷 118-139页

作者： Wang, Yirui Yu, Yang Gao, Shangce Pan, Haiyu Yang, Gang Univ Toyama Fac Engn Toyama 9308555 Japan Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin 541004 Peoples R China Renmin Univ China Sch Informat Multimedia Comp Lab Beijing Peoples R China

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.

关键词： Hierarchical structure gravitational search algorithm Population topology gravitational constant Function optimization

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Using gravitational search algorithm in prototype generation for nearest neighbor classification

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NEUROCOMPUTING 2015年 157卷 256-263页

作者： Rezaei, Mohadese Nezamabadi-pour, Hossein Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

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.

关键词： Classification K-nearest neighbor Prototype generation gravitational search algorithm

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Optimal Placement and Sizing of Inverter-Based Distributed Generation Units and Shunt Capacitors in Distorted Distribution Systems Using a Hybrid Phasor Particle Swarm Optimization and gravitational search algorithm

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2020年第6-7期48卷 543-557页

作者： Milovanovic, Milos Tasic, Dragan Radosavljevic, Jordan Perovic, Bojan Univ Pristina Kosovska Mitrovica Fac Tech Sci Kosovska Mitrovica Serbia Univ Nis Fac Elect Engn Nish Serbia

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.

关键词： distribution system inverter-based distributed generation shunt capacitor optimal placement harmonic distortion hybrid optimization algorithm phasor particle swarm optimization gravitational search algorithm

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Opposition-based gravitational search algorithm applied to economic power dispatch problems consisting of thermal units with emission constraints

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2015年第Sup-1期23卷 2278-2288页

作者： Ozyon, Serdar Yasar, Celal Durmus, Burhanettin Temurtas, Hasan Dumlupinar Univ Dept Elect & Elect Engn Kutahya Turkey Dumlupinar Univ Dept Comp Engn Kutahya Turkey

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.

关键词： Environmental economic power dispatch gravitational search algorithm opposition-based learning weighted sum method

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A novel opposition-based gravitational search algorithm for combined economic and emission dispatch problems of power systems

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2012年第1期35卷 21-33页

作者： Shaw, Binod Mukherjee, V. Ghoshal, S. P. Asansol Engn Coll Dept Elect Engn Asansol W Bengal India Indian Sch Mines Dept Elect Engn Dhanbad 826004 Jharkhand India Natl Inst Technol Dept Elect Engn Durgapur W Bengal India

gravitational search algorithm (GSA) is based on the law of gravity and interaction between masses. In GSA, the searcher agents are a collection of masses, and their interactions are based on the Newtonian laws of gravity and motion. This paper proposes a novel algorithm to accelerate the performance of the GSA. The proposed opposition-based GSA (OGSA) of the present work employs opposition-based learning for population initialization and also for generation jumping. In the present work, opposite numbers have been utilized to improve the convergence rate of the GSA. For the experimental verification of the proposed algorithm, a comprehensive set of 23 complex benchmark test functions including a wide range of dimensions is employed. Additionally, four standard power systems problems of combined economic and emission dispatch (CEED) are solved by the OGSA to establish the optimizing efficacy of the proposed algorithm. The results obtained confirm the potential and effectiveness of the proposed algorithm compared to some other algorithms surfaced in the recent state-of-the art literatures. Both the near-optimality of the solution and the convergence speed of the proposed algorithm are promising. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Benchmark test function Combined economic emission dispatch gravitational search algorithm Opposite numbers Optimization

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