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An Improved Hybrid Method Combining gravitational search algorithm With Dynamic Multi Swarm Particle Swarm Optimization

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IEEE ACCESS 2019年 7卷 50388-50399页

作者： Nagra, Arfan Ali Han, Fei Ling, Qing-Hua Mehta, Sumet Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Jiangsu Peoples R China Jiangsu Key Lab Secur Technol Ind Cyberspace C-212013 Zhenjiang Jiangsu Peoples R China Jiangsu Univ Sci & Technol Sch Comp Sci & Engn Zhenjiang 212003 Jiangsu Peoples R China

GSA is badly suffering from a slow convergence rate and poor local search ability when solving complex optimization problems. To solve this problem, a new hybrid population-based algorithm is proposed with the combination of dynamic multi swarm particle swarm optimization and gravitational search algorithm (GSADMSPSO). The proposed algorithm has divided the main population of masses into smaller sub-swarms and also stabilizing them by presenting a new neighborhood strategy. Then, by adopting the global search ability of the proposed algorithm, each agent (particle) improves the position and velocity. The main idea is to integrate the ability of GSA with the DMSPSO to enhance the performance of exploration and exploitation of a proposed algorithm. In order to evaluate the competences of the proposed algorithm, benchmark functions are employed. The experimental results have been confirmed a better performance of GSADMSPSO as compared with the other gravitational and PSO variants in terms of fitness rate.

关键词： gravitational search algorithm dynamic multi swarm optimization neighborhood strategy benchmark optimization problems

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GSP: an automatic programming technique with gravitational search algorithm

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APPLIED INTELLIGENCE 2019年第4期49卷 1502-1516页

作者： Mahanipour, Afsaneh Nezamabadi-pour, Hossein Shahid Bahonar Univ Kerman Dept Elect Engn IDPL Kerman Iran Shahid Bahonar Univ Kerman Mahani Math Res Ctr Kerman Iran

Recently, automatic programming approaches have attracted great deal of interest aiming to utilize search techniques to find out optimal programs in various problems. Genetic programming is the most commonly explored automatic programming technique which uses genetic algorithm to evolve and discover programs with the tree structure. Herein, we focus on a new gravitational search algorithm (GSA)-based technique to create computer programs, automatically. This method is called gravitational search programming (GSP). Using GSA, the approach of generating the tree structure and insertion of internal nodes has been explained in detail. The GSP has been employed to the symbolic regression (SR) and the problem of feature construction (FC) that are widely used as a mathematical expression fitting to a given set of data points, and a data preprocessing technique for classification, respectively. The proficiency of the proposed algorithm has been evaluated and compared with the well-known automatic programming algorithms as well as C4.5 decision tree classifier. The results have been obtained over ten typical functions and 13 diverse datasets. The obtained results prove the effectiveness of the proposed method in achieving improved accuracy values in comparison to those of competing algorithms.

关键词： Automatic programming gravitational search algorithm Genetic programming Feature construction Symbolic regression

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Batch and column studies on the removal of methyl orange by Acalypha indica biomass using gravitational search algorithm as an optimization tool

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DESALINATION AND WATER TREATMENT 2019年第0期147卷 385-397页

作者： Janani, V. Aruna Saravanan, S. Rajesh, S. Sivakumar, P. Anirbid, Sircar Kalasalingam Acad Res & Educ Dept Chem Engn Krishnankoil 626126 India Kalasalingam Acad Res & Educ Dept Mech Engn Krishnankoil 626126 India Pandit Deendayal Petr Univ Sch Petr Technol Gandhinagar 382007 India

The biosorption of methyl orange dye from an aqueous solution onto Acalypha indica (A. indica) was examined using batch and continuous processes. The equilibrium dye uptake limit of A. indica was determined by analyzing the impact of different working parameters like adsorbent dose, pH, and time. The biosorbent was exemplified by scanning electron microscopy, Fourier transform infrared spectroscopy, surface area analyzer, point of zero charge, and Boehm titration. In batch experiments, the maximum uptake capacity of sorbent (82 mg g(-1)) was achieved with the biosorbent dosage of 0.03 g 100 mL(-1) and at pH 3.0. Henry's isotherm model was (R-2 = 0.997) proved to be a better fit than Langmuir and Freundlich isotherms. The data acquired from kinetic studies were found to fit well with the Elovich's model (R-2 > 0.99) when compared with pseudo-first-order, pseudo-second-order equation, and intraparticle diffusion model. Optimized parameters from batch studies were further employed for column studies. Maximum uptake capacity of biosorbent (244 mg g(-1 )) was obtained at the bed height of 3 cm and at the flow rate of 5 mL min(-1) . Breakthrough curves showed that the chosen biosorbent reduced the concentration of dye from 30 mg L-1 to appreciable level. gravitational search algorithm (GSA) was employed to predict the optimal combination of the process parameters. It was observed from the GSA results that the equilibrium uptake capacity of the biosorbent should be maintained at the concentration level of 11.3921 mg L-1 and at the time of 47.35 min.

关键词： Methyl orange dye Acalypha indica Isotherm Breakthrough curve gravitational search algorithm

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Inverse calculation of demolition robot based on gravitational search algorithm and differential evolution neural network

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2020年第3期17卷

作者： Huang, Jianzhong Cen, Yuwan Xie, Nenggang Ye, Xiaohua Hefei Univ Technol Sch Mech Engn Hefei Anhui Peoples R China Anhui Univ Technol Engn Res Ctr Hydraul Vibrat & Control Minist Educ Maanshan Anhui Peoples R China

For the inverse calculation of laser-guided demolition robot, its global nonlinear mapping model from laser measuring point to joint cylinder stroke has been set up with an artificial neural network. Due to the contradiction between population diversity and convergence rate in the optimization of complex neural networks by using differential evolution, a gravitational search algorithm and differential evolution is proposed to accelerate the convergence rate of differential evolution population driven by gravity. gravitational search algorithm and differential evolution is applied to optimize the inverse calculation neural network mapping model of demolition robot, and the algorithm simulation shows that gravity can effectively regulate the convergence process of differential evolution population. Compared with the standard differential evolution, the convergence speed and accuracy of gravitational search algorithm and differential evolution are significantly improved, which has better optimization stability. The calculation results show that the output accuracy of this gravitational and differential evolution neural network can meet the calculation requirements of the positioning control of demolition robot's manipulator. The optimization using gravitational search algorithm and differential evolution is done with the connection weights of a neural network in this article, and as similar techniques can be applied to the other hyperparameter optimization problem. Moreover, such an inverse calculation method can provide a reference for the autonomous positioning of large hydraulic series manipulator, so as to improve the robotization level of construction machinery.

关键词： Demolition robot differential evolution gravitational search algorithm artificial neural network inverse calculation

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Coverage-All Targets algorithm for 3D Wireless Multimedia Sensor Networks Based on the gravitational search algorithm

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2019年第5期53卷 429-440页

作者： Wang, Yanjiao Chen, Ye Northeast Elect Power Univ Coll Elect Engn Jilin 132000 Jilin Peoples R China

Aiming at the actual targets coverage scene of targets and sensors in the three-dimensional physical world, in order to use the minimal sensors to cover all the targets, a new coverage-all targets algorithm based on gravitational search algorithm (GSA-CT) is proposed. Firstly, from the practical point of view, a 3D coverage-all targets model of WMSNs which based on the spatial position relationship of sensors and targets is established in three-dimensional space. Secondly, in order to avoid randomness of the current order method to determine the minimal number of sensors to cover all the targets, a new fitness calculation method has been proposed. Thirdly, in order to improve solution accuracy, GSA is used as the optimization method of coverage-all targets method. Experimental results show that compared with the other 7 coverage methods for the 9 actual coverage scenarios, the number of sensors required for GSA-CT proposed in this paper is the least, and the method is very stable.

关键词： Wireless multimedia sensor network coverage-all targets three-dimensional directional perception model gravitational search algorithm

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gravitational search algorithm–based fuzzy control for a nonlinear ball and beam system

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Journal of Control and Decision 2018年第3期5卷 229-240页

作者： M.J.Mahmoodabadi N.Danesh Department of Mechanical Engineering Sirjan University of TechnologySirjanIran

In recent decades,fuzzy logic and its application for stabilising nonlinear systems have had a great *** this paper,a novel optimal fuzzy controller is provided to control a ball and beam *** fuzzy control force is calculated via a fuzzy system based on the singleton fuzzifier,the centre average defuzzifier and the product inference *** further improve the control performance,the gravitational search algorithm is applied to optimise the controller *** obtained simulation results indicate that the proposed scheme can provide a better performance in the case of convergence rate and accuracy in comparison with those of other recently published works.

关键词： Fuzzy controller gravitational search algorithm optimal control nonlinear system ball and beam system

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SCGSA: A sine chaotic gravitational search algorithm for continuous optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2020年第0期144卷 113118-000页

作者： Jiang, Jianhua Jiang, Ran Meng, Xianqiu Li, Keqin Jilin Univ Finance & Econ Dept Data Sci Changchun 130117 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

gravitational search algorithm (GSA), as one of the novel meta-heuristic optimization algorithms inspired by the law of gravity and mass interactions, is however prone to local optima stagnation due to heavier gravity. Hence, an enhanced version, chaotic gravitational constants for the gravitational search algorithm (CGSA), was proposed to improve the exploration ability through various chaotic maps. In this paper, with insightful utilization of sine cosine algorithm, we put forward sine chaotic gravitational search algorithm (SCGSA) as a further step of CGSA to escape from its local optima stagnation. The experiments show remarkable results in both the speed of convergence and the ability of finding global optima in 30 benchmark functions (CEC 2014), thus proving a better balance between exploration and exploitation in SCGSA compared with CGSA. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： gravitational search algorithm Chaotic maps Sine cosine algorithm Continuous optimization problem

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An aggregative learning gravitational search algorithm with self-adaptive gravitational constants

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 152卷 113396-113396页

作者： Lei, Zhenyu Gao, Shangce Gupta, Shubham Cheng, Jiujun Yang, Gang Univ Toyama Fac Engn Toyama 9308555 Japan Tongji Univ Key Lab Embedded Syst & Serv Comp Minist Educ Shanghai 200092 Peoples R China Renmin Univ China Sch Informat Beijing Peoples R China Korea Univ Res Inst Mega Construct Seoul 02841 South Korea

The gravitational search algorithm (GSA) is a meta-heuristic algorithm based on the theory of Newtonian gravity. This algorithm uses the gravitational forces among individuals to move their positions in order to find a solution to optimization problems. Many studies indicate that the GSA is an effective algorithm, but in some cases, it still suffers from low search performance and premature convergence. To alleviate these issues of the GSA, an aggregative learning GSA called the ALGSA is proposed with a self-adaptive gravitational constant in which each individual possesses its own gravitational constant to improve the search performance. The proposed algorithm is compared with some existing variants of the GSA on the IEEE CEC2017 benchmark test functions to validate its search performance. Moreover, the ALGSA is also tested on neural network optimization to further verify its effectiveness. Finally, the time complexity of the ALGSA is analyzed to clarify its search performance. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： gravitational search algorithm gravitational constant Elite individuals Exploration and exploitation Aggregative learning Neural network learning

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A Comprehensive Survey on Solving Clustering and Classification Problems Using gravitational search algorithm 9

A Comprehensive Survey on Solving Clustering and Classificat...

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IEEE 9th International Conference on Advanced Computing (IACC)

作者： Rather, Sajad Ahmad Shahid, Mohammad Bala, P. Shanthi Pondicherry Univ Dept Comp Sci Pondicherry India

ISBN: (纸本)9781728143927

gravitational search algorithm is a physics based optimization algorithm inspired by Newton's law of gravitation and laws of motion. Both clustering and classification are two important steps in machine learning and getting expertise in them is the need of today's artificial intelligence era. Inventing new methods for mastering clustering and classification for reducing the complexity of the data is always welcome. This paper presents a review of applications of gravitational search algorithm and its variants for clustering and classification problems. In clustering, the GSA is used with various traditional clustering algorithms to find the interesting patterns in data and to divide the data set into different clusters. For solving classification problems, the GSA is hybridized with other swarm optimization algorithms to increase the classification accuracy and finding optimal classification rules.

关键词： Clustering Classification Feature Subset Selection gravitational search algorithm Hybridization Optimization

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An Improved gravitational search algorithm for Optimization Problems 31

An Improved Gravitational Search Algorithm for Optimization ...

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31st Chinese Control And Decision Conference (CCDC)

作者： Li, Wei Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Shaanxi Key Lab Network Comp & Secur Technol Xian 710048 Peoples R China

ISBN: (纸本)9781728101057

Although GSA is an effective optimization algorithm, the best fitness found by GSA cannot be improved in every generation. In order to improve the performance of GSA, this paper proposed an improved gravitational search algorithm (IGSA). In IGSA, a crossover operation is introduced in IGSA so that each solution can inherit some useful information from the global best solution. The exploitation capability of the algorithm can be greatly enhanced. The linearly decreasing weight is used to balance the global and local search abilities. To verify the effectiveness of IGSA, numerical experiments are carried on ten benchmark problems from CEC2014. The experimental results show that IGSA is competitive with respect to other compared algorithms for solving optimization problems.

关键词： gravitational search algorithm Crossover Weight Optimization algorithm

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