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A hybrid approach of gravitational search algorithm and ant miner plus for fingerprint recognition

INTERNATIONAL JOURNAL OF MODERN PHYSICS C 2023年第4期34卷

作者： Kumar, Mahesh Kumar, Devender Baba Mastnath Univ Dept Comp Sci & Engn Sect 29 Rohtak Haryana India

The gravitational search algorithm (GSA) is an eminent heuristic algorithm inspired by the laws of gravity and motion. It possesses an independent physical model in which the mass agents are guided by gravitational force to quickly achieve the convergence. Although the GSA is proven to be efficient for science and engineering problems, the mass agents can be trapped in premature convergence due to the heaviness of masses in the later iterations. The occurrence of premature convergence impedes the agents' further exploration of the search space for a better solution. Here, the ant miner plus (AMP) variant of the ant colony optimization (ACO) algorithm is utilized to avoid the trapping of agents in local optima. The AMP algorithm extends the exploration ability of the GSA algorithm by using the attributes of pheromone updating rules generated by best ants and a problem-dependent heuristic function. The AMP variant adheres to the attributes of the ACO algorithm and is also a decision-making variant which determines the problem solution more efficiently by constructing a directed acyclic graph, considering class-specific heuristic values, and including weight parameters for the pheromone and heuristic values. In this research, this hybridization of GSA and AMP (GSAMP) algorithms is presented, and it is utilized for the decision-making application of fingerprint recognition. Here, fingerprint recognition is conducted for complete as well as latent fingerprints, which are poor quality partial fingerprints, mostly acquired from crime scenes by law enforcement agencies. The experiments are performed for the complete fingerprint dataset of FVC2004 and the latent fingerprint dataset of NIST SD27, using the proposed GSAMP approach and the individual algorithms of Ant Miner (AM) and AMP. The experimental evaluation indicates the superiority of the proposed approach compared to other methods.

关键词： gravitational search algorithm ant colony optimization ant miner ant miner plus heuristic algorithm fingerprint recognition latent fingerprints

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A Novel Feature Selection Method on Mutual Information and Improved gravitational search algorithm for High Dimensional Biomedical Data 13

A Novel Feature Selection Method on Mutual Information and I...

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13th International Conference on Computer and Automation Engineering (ICCAE)

作者： Yan, Chaokun Kang, Xi Li, Mengyuan Wang, Jianlin Henan Univ Sch Comp & Informat Engn Kaifeng Peoples R China

ISBN: (纸本)9781665412957

In the past few decades, the field of bioinformatics has accumulated a large amount of gene expression data which provided important support for the diagnosis of disease. However, high dimensionality, small sample sizes, and redundant features often adversely affect the accuracy and the speed of prediction. Existing feature selection models cannot obtain the information of these datasets accurately. Filter and wrapper are two commonly used feature selection methods. Combining the advantages of the fast calculation speed of the filter and the high accuracy of the wrapper, a new hybrid algorithm called MI-IBGSA, is proposed, which hybridizes mutual information and improved gravitational search algorithm (GSA). First, mutual information is used to rank and select important features, these features are further chosen into the population of the wrapper method. Then, due to the effectiveness of the GSA algorithm, GSA is adopted to further seek an optimal feature subset. However, GSA also has the disadvantages of slow search speed and premature convergence, which limit its optimization ability. In our work, a scale function is added to the speed update to enhance its searchability, and an adaptive kappa(best) particle update formula is proposed to improve its convergence accuracy and propose a fitness sharing strategy to enhance the randomness of particle populations and searchability through the niche algorithm of fitness sharing. We used 10- fold-CV method with the KNN classifier to evaluate the classification accuracy. Experimental results on five publicly available high-dimensional biomedical data sets show that the proposed MI-IBGSA has superior performance than other algorithms.

关键词： Feature selection gravitational search algorithm Mutual Information scale function adaptive kappa(best) particle update fitness sharing strategy Niche algorithm

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Feature Selection in Multi-label Classification based on Binary Quantum gravitational search algorithm 26

Feature Selection in Multi-label Classification based on Bin...

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26th International Computer Conference of the Computer-Society-of-Iran

作者： Noormohammadi, Hojat Dowlatshahi, Mohammad Bagher Lorestan Univ Dept Engn Khorramabad Iran

ISBN: (纸本)9781665412414

Unlike a single-label supervisor dataset where each instance is assigned to one class label, in multi-label datasets, several class labels are assigned to each instance, which makes it difficult to build an accurate and comprehensive model from this dataset. In this study, a memetic algorithm for feature selection in a multi-label dataset is proposed. The principal innovation of this study is the offer of a novel local search algorithm which, in collaboration with binary quantum-inspired gravitational search algorithm (BQIGSA), forms the main framework of the proposed memetic algorithm. The main invention of the proposed local search algorithm is to build a number of neighbors for a solution using the prior knowledge vector and the posterior knowledge vector to select effective features and remove useless and irrelevant features. The results of implementing the proposed algorithm and comparing these results with similar works show that the proposed method in most cases leads to better results.

关键词： Multi-label feature selection Memetic algorithm Local search algorithm Prior knowledge vector Posterior knowledge vector gravitational search algorithm

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gravitational search algorithm for Microseismic Source Location in Tunneling: Performance Analysis and Engineering Case Study

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ROCK MECHANICS AND ROCK ENGINEERING 2019年第10期52卷 3999-4016页

作者： Ma, Chunchi Jiang, Yupeng Li, Tianbin Chengdu Univ Technol State Key Lab Geohazard Prevent & Geoenvironm Pro Chengdu 610059 Sichuan Peoples R China Univ Sydney Sch Civil Engn Sydney NSW 2006 Australia

Microseismic source location (MSL) provides crucial information for the interpretation of rock mass stability and early warning of rock mass hazards. The accuracy of MSL mainly depends on the formation of the sensor array, the multi-velocity model, and the locating algorithm. Especially, the choice of algorithm plays a decisive role, which requires both optimal accuracy and efficiency for searching the global optimal solution. In this paper, an advanced heuristic algorithm, gravitational search algorithm (GSA), is applied for MSL in tunnel engineering. A standard framework of the GSA-based searching process is first built. Its accuracy, stability, and speed of convergence are rigorously compared and analyzed with particle swarm optimization and simplex algorithm using synthetic and real microseismic data. Four types of equivalent velocity models are combined with the searching algorithm to further discuss the applicability and performance of GSA in different situations. The studies show that for all the cases, GSA has the highest speed of convergence with the best accuracy. Locating errors are controlled within 10 m, which fulfills the requirement of engineering accuracy. A representative case study is conducted using microseismic data prior to a major rockburst in a twin-tube highway tunnel. The calculated cluster of seismic events using GSA-based algorithm well matches the actual unstable areas. This work indicates that GSA is an optimal algorithm for microseismic source location and rockburst warning in tunneling.

关键词： Microseismic source location gravitational search algorithm Velocity models Rockburst warning Tunnel engineering

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gravitational search algorithm and K-means for simultaneous feature selection and data clustering: a multi-objective approach

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SOFT COMPUTING 2019年第6期23卷 2083-2100页

作者： Prakash, Jay Singh, Pramod Kumar ABV Indian Inst Informat Technol & Management Gwa Computat Intelligence & Data Min Res Lab Gwalior MP India

Clustering is an unsupervised classification method used to group the objects of an unlabeled data set. The high dimensional data sets generally comprise of irrelevant and redundant features also along with the relevant features which deteriorate the clustering result. Therefore, feature selection is necessary to select a subset of relevant features as it improves discrimination ability of the original set of features which helps in improving the clustering result. Though many metaheuristics have been suggested to select subset of the relevant features in wrapper framework based on some criteria, most of them are marred by the three key issues. First, they require objects class information a priori which is unknown in unsupervised feature selection. Second, feature subset selection is devised on a single validity measure;hence, it produces a single best solution biased toward the cardinality of the feature subset. Third, they find difficulty in avoiding local optima owing to lack of balancing in exploration and exploitation in the feature search space. To deal with the first issue, we use unsupervised feature selection method where no class information is required. To address the second issue, we follow pareto-based approach to obtain diverse trade-off solutions by optimizing conceptually contradicting validity measures silhouette index (Sil) and feature cardinality (d). For the third issue, we introduce genetic crossover operator to improve diversity in a recent Newtonian law of gravity-based metaheuristic binary gravitational search algorithm (BGSA) in multi-objective optimization scenario;it is named as improved multi-objective BGSA for feature selection (IMBGSAFS). We use ten real-world data sets for comparison of the IMBGSAFS results with three multi-objective methods MBGSA, MOPSO, and NSGA-II in wrapper framework and the Pearson's linear correlation coefficient (FM-CC) as a multi-objective filter method. We employ four multi-objective quality measures converge

关键词： Feature selection Data clustering Multi-objective optimization 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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gravitational search algorithm with both attractive and repulsive forces

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SOFT COMPUTING 2019年第3期23卷 783-825页

作者： Zandevakili, Hamed Rashedi, Esmat Mahani, Ali Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran Grad Univ Adv Technol Dept Elect Engn Kerman Iran

The gravitational search algorithm (GSA) is a meta-heuristic optimization algorithm which is inspired by the gravity force. This algorithm uses Newton's gravity and motion laws to calculate the masses interactions and shows high performance in solving optimization problems. The premature convergence is the common drawback of heuristic search algorithms in high-dimensional problems, and GSA is not an exception. In this paper, a new version of GSA is proposed to improve the power of GSA in exploration and exploitation. The proposed algorithm has both attractive and repulsive forces. In this algorithm, the heavy particles attract some particles and repulse some others, in which the forces are inversely proportional to their distances. For better evaluation, the GSA with both attractive and repulsive forces (AR-GSA) is tested using CEC 2013 benchmark functions and the results are compared with some well-known meta-heuristic algorithms. The simulation results show that AR-GSA can improve the convergence rate, the exploration, and the exploitation capabilities of GSA.

关键词： Optimization Heuristic search algorithms gravitational search algorithm Centripetal force

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Optimized economic operation of energy storage integration using improved gravitational search algorithm and dual stage optimization

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JOURNAL OF ENERGY STORAGE 2022年 50卷

作者： Nazir, Muhammad Shahzad Abdalla, Ahmad N. Zhao, Huanyu Chu, Zhang Nazir, Hafiz M. Jamsheed Bhutta, Muhammad Shoaib Javed, Muhammad Sufyan Sanjeevikumar, Padmanaban Huaiyin Inst Technol Fac Automat Huaian 223003 Peoples R China Huaiyin Inst Technol Fac Informat & Elect Engn Huaian 223003 Peoples R China Yuan Ze Univ Dept Comp Sci & Engn Taoyuan Taiwan Wuxi Univ Sch Elect & Informat Engn Wuxi 214105 Jiangsu Peoples R China Lanzhou Univ Sch Phys Sci & Technol Lanzhou 730000 Peoples R China Aarhus Univ Dept Business Dev & Technol CTiF Global Capsule CGC Herning Campus DK-7400 Herning Denmark

This paper presents optimized energy storage and distribution network model under uncertainty of the power generation problem, based on an improved gravitational search algorithm (IGSA). Throughout the search phase, the IGSA uses a trial-and-error strategy to update the best agent. It also modifies the orbit of the poor agent in the late stages of the search and uses the coordinate descent approach to search for the optimal agent's position. A dual-stage optimization (DSO) model is employed for energy storage and uncertainty of energy generation. The hybrid IGSA-DSO used an adoptive speed inertia coefficient which ensures global and local searchability. The features of the proposed model were validated using IEEE-RTS 24 bus test system with various cases to realize the coordinated planning of the power grid and energy storage. Finally, the simulation shows interesting results of the proposed approach (IGSA-DSO), which reduces the investment cost by 0.248% comparatively.

关键词： Renewable energy Energy storage Energy economics Sustainable system gravitational search algorithm Dual-stage optimization Wind energy

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gravitational search algorithm based data scheduling for peer to peer video on demand system

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第19期78卷 27291-27307页

作者： AjithaGladis, K. P. CSI Inst Technol Dept IT Thovalai India

In peer to peer (P2P) video streaming systems, peers in network assist to forward the data to other peers without the interference of central servers. Video on Demand (VoD) is widely using internet service, which offers video to users with effective control when they need it. The major significant problem in developing a P2P VoD system is data scheduling, which concentrates on dealing with transmitting and dispatching data segments within a system efficiently. So a gravitational search algorithm based data Scheduling (GSAS) is presented in this paper. Initially, the network is developed in the form of hierarchical topology. Video file is cached as data segments in each peer in the top layer of the network. Using the priority function, these data segments are sorted or prioritized. Using the proposed GSA algorithm, optimal or suitable peers which cache the requested data segments in the top layer are selected. Then the prioritized data segments from the selected peers are scheduled to the peer which has requested for video sequences. The video file "Grandmother" with the size 53 MB is examined in this approach. This proposed approach is simulated in the network simulator. Simulation results show that the performance of this proposed approach is superior to that of the existing work in terms of throughput and scheduling time.

关键词： Peer to peer video streaming systems Video on demand (VoD) gravitational search algorithm Data scheduling Priority function

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Monitoring tool wear using wavelet package decomposition and a novel gravitational search algorithm-least square support vector machine model

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2020年第3期234卷 822-836页

作者： Kong, Dongdong Chen, Yongjie Li, Ning Huazhong Univ Sci & Technol Sch Mech Sci & Engn 1037 Luoyu Rd Wuhan Hubei Peoples R China

Monitoring tool wear has drawn much attention recently since tool failure will make it hard to guarantee the surface integrity of workpieces and the stability of manufacturing process. In this paper, the integrated approach that combines wavelet package decomposition, least square support vector machine, and the gravitational search algorithm is proposed for monitoring the tool wear in turning process. Firstly, the wavelet package decomposition is utilized to decompose the original cutting force signals into multiple sub-bands. Root mean square of the wavelet packet coefficients in each sub-band are extracted as the monitoring features. Then, the gravitational search algorithm-least square support vector machine model is constructed by using the extracted wavelet-domain features so as to identify the tool wear states. Eight sets of cutting experiments are conducted to prove the superiority of the proposed integrated approach. The experimental results show that the wavelet-domain features can help to ameliorate the performance of the gravitational search algorithm-least square support vector machine model. Besides, gravitational search algorithm-least square support vector machine performs better than gravitational search algorithm-support vector machine in prediction accuracy of tool wear states even in the case of small-sized training data set and the time consumption of parameters optimization in gravitational search algorithm-least square support vector machine is less than that of gravitational search algorithm-support vector machine under large-sized training data set. What's more, the gravitational search algorithm-least square support vector machine model outperforms some other related methods for tool wear estimation, such as k-NN, feedforward neural network, classification and regression tree, and linear discriminant analysis.

关键词： Tool wear estimation cutting forces wavelet package decomposition least square support vector machine gravitational search algorithm

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