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Graph Planarization Problem Optimization Based on Triple-Valued gravitational search algorithm

IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2014年第1期9卷 39-48页

作者： Gao, Shangce Todo, Yuki Gong, Tao Yang, Gang Tang, Zheng Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Kanazawa Univ Fac Elect & Comp Engn Brain Like Informat Proc Lab Kanazawa Ishikawa 9201192 Japan Renmin Univ China Sch Informat Beijing 100872 Peoples R China Toyama Univ Grad Sch Innovat Life Sci Toyama 9308555 Japan

This article presents a triple-valued gravitational search algorithm (TGSA) to tackle the graph planarization problem (GPP). GPP is one of the most important tasks in graph theory, and has proved to be an NP-hard problem. To solve it, TGSA uses a triple-valued encoding scheme and models the search space into a triangular hypercube quantitatively based on the well-known single-row routing representation method. The agents in TGSA, whose interactions are driven by the gravity law, move toward the global optimal position gradually. The position updating rule for each agent is based on two indices: one is a velocity index which is a function of the current velocity of the agent, and the other is a population index based on the cumulative information in the whole population. To verify the performance of the algorithm, 21 benchmark instances are tested. Experimental results indicate that TGSA can solve the GPP by finding its maximum planar subgraph and embedding the resulting edges into a plane simultaneously. Compared with traditional algorithms, a novelty of TGSA is that it can find multiple optimal solutions for the GPP. Comparative results also demonstrate that TGSA outperforms the traditional meta-heuristics in terms of the solution qualities within reasonable computational times. Published by John Wiley & Sons, Inc.

关键词： gravitational search algorithm triple-valued encoding graph planarization

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Path planning of unmanned aerial vehicle based on improved gravitational search algorithm

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Science China(Technological Sciences) 2012年第10期55卷 2712-2719页

作者： LI Pei DUAN HaiBin Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical EngineeringBeihang UniversityBeijing 100191China State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China

Path planning of Uninhabited Aerial Vehicle(UAV) is a complicated global optimum *** the paper,an improved gravitational search algorithm(GSA) was proposed to solve the path planning *** search algorithm(GSA) is a newly presented under the inspiration of the Newtonian gravity,and it is easy to fall local *** the basis of introducing the idea of memory and social information of Particle Swarm Optimization(PSO),a novel moving strategy in the searching space was designed,which can improve the quality of the optimal ***,a weighted value was assigned to inertia mass of every agent in each iteration process to accelerate the convergence speed of the *** position was updated according to the selection rules of survival of the *** this way,the population is always moving in the direction of the optimal *** feasibility and effectiveness of our improved GSA approach was verified by comparative experimental results with PSO,basic GSA and two other GSA models.

关键词： uninhabited aerial vehicle path planning gravitational search algorithm social information weighted value selection rules

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Feature Selection Using an Improved gravitational search algorithm

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IEEE ACCESS 2019年 7卷 114440-114448页

作者： Zhu, Lei He, Shoushuai Wang, Lei Zeng, Weijun Yang, Jian Army Engn Univ PLA Coll Commun Engn Nanjing 210007 Jiangsu Peoples R China

Feature selection is an important issue in the field of machine learning, which can reduce misleading computations and improve classification performance. Generally, feature selection can be considered as a binary optimization problem. gravitational search algorithm (GSA) is a population-based heuristic algorithm inspired by Newton's laws of gravity and motion. Although GSA shows good performance in solving optimization problems, it has a shortcoming of premature convergence. In this paper, the concept of global memory is introduced and the definition of exponential Kbest is used in an improved version of GSA called IGSA. In this algorithm, the position of the optimal solution obtained so far is memorized, which can effectively prevent particles from gathering together and moving slowly. In this way, the exploitation ability of the algorithm gets improved, and a proper balance between exploration and exploitation gets established. Besides, the exponential Kbest can significantly decrease the running time. In order to solve feature selection problem, a binary IGSA (BIGSA) is further introduced. The proposed algorithm is tested on a set of standard datasets and compared with other algorithms. The experimental results confirm the high efficiency of BIGSA for feature selection.

关键词： Feature selection gravitational search algorithm classification

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Multi-strategy gravitational search algorithm for constrained global optimization in coordinative operation of multiple hydropower reservoirs and solar photovoltaic power plants

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APPLIED SOFT COMPUTING 2021年 107卷 107315-107315页

作者： Niu, Wen-jing Feng, Zhong-kai Liu, Shuai ChangJiang Water Resources Commiss Bur Hydrol Wuhan 430010 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China China Water Resources Beifang Invest Design & Res Tianjin 300222 Peoples R China

Recently, the solar photovoltaic power, a promising renewable energy, is witnessing a rapid development period. However, it is often difficult to perfectly capture the generation of solar photovoltaic plants because of various factors (like weather condition, solar radiation and human activities), increasing the operational risk and cost of power system. Hybrid energy system proves to be an effective measure to address this problem. Motivated by this practical necessity, this paper develops a novel hybrid gravitational search algorithm to solve the coordinative operation model of multiple hydropower reservoirs and solar photovoltaic power plants. In the proposed method, the gravitational search algorithm is set as the unified framework;the neighborhood search strategy is used to improve the convergence rate by considering the social information and individual experience;the adaptive mutation strategy is used to improve the population diversity by elite conservation and mutation operator;the modified elastic-ball strategy and constraint handling technique are used to enhance the solution feasibility. The simulation results of numerical functions demonstrate the superiority of the developed method in convergence rate and global search ability. The hydro-solar operation results in different cases show that compared with the traditional methods, the proposed method can yield high-quality scheduling schemes to alleviate the peak shaving pressure of power system. Thus, the novelty of this paper is to provide an effective HGSA method for solving the complex engineering optimization problem. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Engineering optimization Hybrid energy system Multiple hydropower reservoirs Solar photovoltaic power plants gravitational search algorithm

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Automatic Unsupervised Feature Selection using gravitational search algorithm

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IETE JOURNAL OF REsearch 2015年第1期61卷 22-31页

作者： Kumar, Vijay Chhabra, Jitender Kumar Kumar, Dinesh Manipal Univ CSE Dept Jaipur Rajasthan India NIT Comp Engn Dept Kurukshetra Haryana India GJUS & T Comp Sci & Engn Dept Hisar Haryana India

Feature selection is an optimization problem that selects the features that have minimum redundancy and maximum relevance to improve the efficiency of algorithms. In this paper, we have proposed a novel automatic unsupervised feature selection method based on gravitational search algorithm, called AFSGSA (automatic feature selection using gravitational search algorithm). In contrast to most of existing unsupervised feature selection techniques, the proposed AFSGSA requires no prior knowledge of the data to be classified and number of features to be selected. AFSGSA determines the optimal number of features of the data-set on the run. Statistical property of data-set is used to develop a novel threshold setting concept to refine the features. A novel fitness function is also proposed to make the search more efficient. The performance of AFSGSA has been compared with recently developed well-known feature selection techniques. The experimental results reveal the efficiency and efficacy of the proposed feature selection technique over other existing techniques.

关键词： Clustering Feature selection gravitational search algorithm Meta-heuristics Variance

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Optimization of cutting conditions for end milling of Ti6Al4V Alloy by using a gravitational search algorithm (GSA)

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MECCANICA 2013年第7期48卷 1701-1715页

作者： Al-Zubaidi, Salah Ghani, Jaharah A. Haron, Che Hassan Che Univ Kebangsaan Malaysia Dept Mech & Mat Engn Fac Engn & Built Environm Bangi 43600 Selangor Malaysia

Surface roughness is commonly used to indicate the quality of machine parts. Optimizing cutting parameters throughout the machining process is an important aspect for manufacturers, as it allows them to achieve a minimum surface value. During this study, a new optimization technique known as the gravitational search algorithm (GSA) was employed in order to achieve minimum surface roughness when end milling a Ti6Al4V alloy under dry cutting conditions, with both PVD coated and uncoated cutting tools. Regression models have been created based on the results of real experimental data. Through use of SPSS software, it was possible to formulate the objective (fitness) functions which were used in the GSA optimization for each cutting tool. A MATLAB code was then created to instigate the optimization process. The results indicated that high cutting speed and low feed rate and depth of cut could result in a minimum surface roughness value of (0.6255 mu m), based on the objective function for the PVD cutting tool. Alternatively, surface roughness of around (0.4165 mu m) could be achieved by using an uncoated tool on a lower feed rate, depth of cut and cutting speed. The same GSA technique was used in another case study optimized by Genetic algorithm (GA). The GSA achieved the same results, and proved that it is faster than GA: GSA could reach the optimum solution in the third iteration;GA could only reach it in the 67th.

关键词： Optimization Ti6Al4V alloy gravitational search algorithm Surface roughness

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An improved gravitational search algorithm for green partner selection in virtual enterprises

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NEUROCOMPUTING 2016年 217卷 103-109页

作者： Xiao, Jianhua Niu, Yunyun Chen, Ping Leung, Stephen C. H. Xing, Fei Nankai Univ Res Ctr Logist Tianjin 300071 Peoples R China China Univ Geosci Sch Informat Engn Beijing 100083 Peoples R China Nankai Univ Sch Business Tianjin 300071 Peoples R China Univ Hong Kong Fac Engn Hong Kong Hong Kong Peoples R China Xiamen Univ Sch Aerosp Engn Xiamen 361005 Peoples R China

With the increasing public consciousness in environmental protection, the green partner selection problem (G-PSP) is an important issue in virtual enterprises. In this paper, the green criterion is introduced to partner selection problem (PSP), and a green partner selection model based on six criteria is proposed. As PSP has been proven to be an NP problem, and G-PSP cannot be solved in reasonable time by traditional methods. In this paper, an improved algorithm I-GSA/PSO that combines gravitational search algorithm and particle swarm optimization is developed to solve G-PSP in virtual enterprises. Experimental results show that I-GSA/PSO is effective and outperforms other evolutionary algorithms in solving G-PSP. (C) 2016 Published by Elsevier B.V.

关键词： gravitational search algorithm Particle swarm optimization Green partner selection Virtual enterprise

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A novel hybrid system for feature selection based on an improved gravitational search algorithm and k-NN method

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APPLIED SOFT COMPUTING 2015年 31卷 293-307页

作者： Xiang, Jie Han, XiaoHong Duan, Fu Qiang, Yan Xiong, XiaoYan Lan, Yuan Chai, Haishui Taiyuan Univ Technol Minist Educ China Key Lab Adv Transducers & Intelligent Control Sys Taiyuan Shanxi Peoples R China Taiyuan Univ Technol Coll Comp Sci & Technol Taiyuan Shanxi Peoples R China

Feature selection is an important pre-processing step for solving classification problems. This problem is often solved by applying evolutionary algorithms in order to decrease the dimensional number of features involved. In this paper, we propose a novel hybrid system to improve classification accuracy with an appropriate feature subset in binary problems based on an improved gravitational search algorithm. This algorithm makes the best of ergodicity of piecewise linear chaotic map to explore the global search and utilizes the sequential quadratic programming to accelerate the local search. We evaluate the proposed hybrid system on several UCI machine learning benchmark examples, comparing our approaches with feature selection techniques and obtained better predictions with consistently fewer relevant features. Furthermore, the improved gravitational search algorithm is tested on 23 nonlinear benchmark functions and compared with 5 other heuristic algorithms. The obtained results confirm the high performance of the improved gravitational search algorithm in solving function optimization problems. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Feature selection Binary classification gravitational search algorithm k-nearest neighbor Leave-one-out cross validation Function optimizationa

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A Multi-Layered gravitational search algorithm for Function Optimization and Real-World Problems

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 94-109页

作者： Yirui Wang Shangce Gao Mengchu Zhou Yang Yu the Faculty of Engineering University of ToyamaToyama 930-8555Japan the Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

A gravitational search algorithm(GSA)uses gravitational force among individuals to evolve *** GSA is an effective population-based algorithm,it exhibits low search performance and premature *** ameliorate these issues,this work proposes a multi-layered GSA called *** by the two-layered structure of GSA,four layers consisting of population,iteration-best,personal-best and global-best layers are *** interactions among four layers are dynamically implemented in different search stages to greatly improve both exploration and exploitation abilities of *** comparison between MLGSA and nine existing GSA variants on twenty-nine CEC2017 test functions with low,medium and high dimensions demonstrates that MLGSA is the most competitive *** is also compared with four particle swarm optimization variants to verify its excellent ***,the analysis of hierarchical interactions is discussed to illustrate the influence of a complete hierarchy on its *** relationship between its population diversity and fitness diversity is analyzed to clarify its search *** computational complexity is given to show its ***,it is applied to twenty-two CEC2011 real-world optimization problems to show its practicality.

关键词： Artificial intelligence exploration and exploitation gravitational search algorithm hierarchical interaction hierarchy machine learning population structure

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Hypersonic reentry trajectory planning by using hybrid fractional-order particle swarm optimization and gravitational search algorithm

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Chinese Journal of Aeronautics 2021年第1期34卷 50-67页

作者： Khurram SHAHZAD SANA Weiduo HU School of Astronautics Beihang UniversityBeijing 102206China

This paper proposes a novel hybrid algorithm called Fractional-order Particle Swarm optimization gravitational search algorithm(FPSOGSA)and applies it to the trajectory planning of the hypersonic lifting reentry flight *** proposed method is used to calculate the control profiles to achieve the two objectives,namely a smoother trajectory and enforcement of the path constraints with terminal *** smoothness of the trajectory is achieved by scheduling the bank angle with the aid of a modified scheme known as a Quasi-Equilibrium Glide(QEG)*** aerodynamic load factor and the dynamic pressure path constraints are enforced by further planning of the bank angle with the help of a constraint enforcement *** maximum heating rate path constraint is enforced through the angle of attack *** Common Aero Vehicle(CAV)flight vehicle is used for the simulation purpose to test and compare the proposed method with that of the standard Particle Swarm Optimization(PSO)method and the standard gravitational search algorithm(GSA).The simulation results confirm the efficiency of the proposed FPSOGSA method over the standard PSO and the GSA methods by showing its better convergence and computation efficiency.

关键词： Fractional-order gravitational search algorithm Particle swarm optimization Reentry gliding vehicle Trajectory optimization

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