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Feature selection by recursive binary gravitational search algorithm optimization for cancer classification

SOFT COMPUTING 2020年第6期24卷 4407-4425页

作者： Han, Xiaohong Li, Dengao Liu, Ping Wang, Li Taiyuan Univ Technol 79 West St Yingze Taiyuan 030024 Shanxi Peoples R China

DNA microarray technology has become a prospective tool for cancer classification. However, DNA microarray datasets typically have very large number of genes (usually more than tens of thousands) and less number of samples (often less than one hundred). This raises the issue of getting the most relevant genes prior to cancer classification. In this paper, we have proposed a two-phase feature selection method for cancer classification. This method selects a low-dimensional set of genes to classify biological samples of binary and multi-class cancers by integrating ReliefF with recursive binary gravitational search algorithm (RBGSA). The proposed RBGSA refines the gene space from a very coarse level to a fine-grained one at each recursive step of the algorithm without degrading the accuracy. We evaluate our method by comparing it with state-of-the-art methods on 11 benchmark microarray datasets of different cancer types. Comparison results show that our method selects only a small number of genes while yielding substantial improvements in accuracy over other methods. In particular, it achieved up to 100% classification accuracy for 7 out of 11 datasets with a very small size of gene subset (up to < 1.5%) for all 11 datasets.

关键词： Gene selection Cancer classification gravitational search algorithm

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A gravitational search algorithm With Chaotic Neural Oscillators

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IEEE ACCESS 2020年 8卷 25938-25948页

作者： Wang, Yirui Gao, Shangce Yu, Yang Wang, Ziqian Cheng, Jiujun Yuki, Todo Toyama Univ Fac Engn Toyama 9308555 Japan Tongji Univ Dept Comp Sci & Technol Key Lab Embedded Syst & Serv Comp Minist Educ Shanghai 200092 Peoples R China Kanazawa Univ Fac Elect & Informat & Comp Engn Kanazawa Ishikawa 9201192 Japan

gravitational search algorithm (GSA) inspired from physics emulates gravitational forces to guide particles' search. It has been successfully applied to diverse optimization problems. However, its search performance is limited by its inherent mechanism where gravitational constant plays an important role in gravitational forces among particles. To improve it, this paper uses chaotic neural oscillators to adjust its gravitational constant, named GSA-CNO. Chaotic neural oscillators can generate various chaotic states according to their parameter settings. Thus, we select four kinds of chaotic neural oscillators to form distinctive chaotic characteristics. Experimental results show that chaotic neural oscillators effectively tune the gravitational constant such that GSA-CNO has good performance and stability against four GSA variants on functions. Three real-world optimization problems demonstrate the promising practicality of GSA-CNO.

关键词： Chaotic neural oscillator chaotic state gravitational constant gravitational search algorithm

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Implementing novel hybrid models to improve indirect measurement of the daily soil temperature: Elman neural network coupled with gravitational search algorithm and ant colony optimization

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MEASUREMENT 2020年 165卷 108127-108127页

作者： Mehdizadeh, Saeid Mohammadi, Babak Pham, Quoc Bao Khoi, Dao Nguyen Linh, Nguyen Thi Thuy Urmia Univ Water Engn Dept Orumiyeh Iran Hohai Univ Coll Hydrol & Water Resources Nanjing 210098 Peoples R China Duy Tan Univ Inst Res & Dev Danang 550000 Vietnam Duy Tan Univ Fac Environm & Chem Engn Danang 550000 Vietnam Vietnam Natl Univ Ho Chi Minh City Univ Sci Fac Environm Ho Chi Minh City Vietnam

Soil temperature (ST) as a vital variable of soil plays a key role in agriculture products, surface energy transactions, soil moisture balance, etc. In developing countries like Iran, access to the ST data may be limited. Hence, estimating this parameter by an appropriate alternative approach is of great importance. Two novel hybrid models are developed in this study based on Elman neural network (ENN) coupled with gravitational search algorithm (GSA) and ant colony optimization (ACO) for improving the daily ST estimation at various soil depths (i.e., ENN-GSA and ENN-ACO). In fact, both the optimization algorithms including the GSA and ACO were applied to train the parameters of ENN. To achieve this, the daily data from two stations, namely the Isfahan and Rasht located in Iran were employed during 1998-2017. The classical ENN and hybrid ENN-GSA and ENN-ACO models are developed using the other meteorological parameters under eleven different scenarios. The results illustrated that the proposed hybrid models outperformed the classical ENN for estimating the daily ST of the studied locations at different depths;however, the hybrid ENN-GSA was the best-performing model at the studied stations and whole the soil depths. In addition, all the standalone and hybrid models illustrated the highest accuracy under full input pattern. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Soil temperature Estimation Elman neural networks gravitational search algorithm Ant colony optimization

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Annual Streamflow Time Series Prediction Using Extreme Learning Machine Based on gravitational search algorithm and Variational Mode Decomposition

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JOURNAL OF HYDROLOGIC ENGINEERING 2020年第5期25卷

作者： Niu, Wen-jing Feng, Zhong-kai Chen, Yu-bin Zhang, Hai-rong Cheng, Chun-tian ChangJiang Water Resources Commiss Bur Hydrol Jiefang Ave 1863 Wuhan 430010 Peoples R China Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Hubei Peoples R China China Yangtze Power Co Ltd Dept Water Resources Management Jianshe Rd 1 Yichang 443000 Peoples R China Dalian Univ Technol Dept Inst Hydropower & Hydroinformat Key Lab Ocean Energy Utilizat & Energy Conservat Minist Educ Dalian 116024 Peoples R China

Accurate annual runoff prediction plays an important role in modern water resources planning and management. Here, a hybrid model using evolutionary extreme learning machine and variational mode decomposition (VMD) is developed to forecast annual runoff time series. In the proposed method, the VMD method is first used to decompose the original streamflow into a series of disjoint subcomponents;second, each subcomponent is forecasted by constructing an appropriate extreme learning machine model while the gravitational search algorithm is adopted to tune the model parameters;finally, the aggregated output generated by the forecasting results of all the models is treated as the final simulated output. The annual runoff data series of three huge hydropower reservoirs in China are chosen to testify the performance of the proposed forecasting model. The results show that the developed model can outperform several traditional methods with respect to the employed statistical indexes. Thus, the decomposition-ensemble idea is helpful to yield accurate and stable forecasting results, while the proposed forecasting method can provide strong technical support for operators in water resources and power systems.

关键词： Hydrological time series forecasting Variational mode decomposition Evolutionary extreme learning machine gravitational search algorithm Decomposition and ensemble

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Energy-efficient clustering method for wireless sensor networks using modified gravitational search algorithm

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EVOLVING SYSTEMS 2020年第4期11卷 575-587页

作者： Ebrahimi Mood, Sepehr Javidi, Mohammad Masoud Shahid Bahonar Univ Kerman Dept Comp Sci Kerman Iran

Past decades have witnessed the advancement of wireless sensor networks (WSNs) in both academic and industrial communities. Clustering is one of the most popular methods to increase the lifespan of WSNs. The optimal number of cluster heads and how to organize the clusters are the most important issues to be addressed in the clustering methods. In this paper, we proposed a novel user-independent and dynamical method to calculate the optimal number of clusters, organize the clusters, and determine the best cluster heads in each round. In this method, efficient energy consumption and link quality were considered to compute the optimal number of clusters. Then, the algorithm began to organize the compact clusters with high energy level cluster heads. We investigated a new fitness function in order to achieve these objectives. A new version of gravitational search algorithm (GSA) was used to solve this optimization problem. In this algorithm, the power distance sums scaling method was applied to calculate the mass values. Then, a fuzzy logic controller is employed to identify the parameter of this algorithm to control the exploitation and exploration abilities of the method during the computational process of the algorithm. Then, the novel version of GSA was applied to reach an appropriate solution for the fitness function, find the optimal number of clusters, and properly organize these clusters. To evaluate the effectiveness of the proposed method, several experiments were performed and the obtained results were compared with the results of other popular clustering methods. The simulation results revealed that the performance of the modified GSA was better than other state-of-the-art meta-heuristic optimization algorithms. Moreover, the proposed method for the clustering problem in WSNs outperformed other popular clustering methods and increased the lifetime of WSNs.

关键词： Wireless sensor network Clustering methods Energy consumption gravitational search algorithm Fuzzy logic controller

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Enhance chaotic gravitational search algorithm (CGSA) by balance adjustment mechanism and sine randomness function for continuous optimization problems

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2020年 537卷 122621-000页

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

The gravitational search algorithm (GSA) is a population-based meta-heuristic optimization algorithm which finds the optimal solution by the law of gravity and attraction between objects. However, as the number of iterations increases, the increase of the quality of the agents makes GSA fall into the local optimal solution more easily, which greatly reduces the exploration capability of the algorithm. Although the chaotic gravitational search algorithm (CGSA) uses chaotic maps for improving diversity to solve this problem, it still has problems with the balance of exploration and exploitation. This paper proposes the balance adjustment based chaotic gravitational search algorithm (BA-CGSA), which introduces the sine randomness function and the balance mechanism to solve the above problem. 30 benchmark functions of IEEE CEC 2014 are adopted to evaluate the performance of the proposed algorithm in terms of exploration and exploitation. Meanwhile, a real engineering design problem is used to illustrate the ability of the algorithm to solve practical application problems. The experimental results demonstrate its good performance in continuous optimization problems. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Optimization algorithm gravitational search algorithm Sine cosine algorithm Balance adjustment mechanism

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Ecological operation of cascade hydropower reservoirs by elite-guide gravitational search algorithm with Levy flight local search and mutation

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JOURNAL OF HYDROLOGY 2020年第0期581卷 124425-000页

作者： Feng, Zhong-kai Liu, Shuai Niu, Wen-jing Li, Shu-shan Wu, Hui-jun Wang, Jia-yang Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China Bur Hydrol Changjiang Water Resources Commiss Wuhan 430010 Peoples R China Power Dispatching Control Ctr China Southern Power Grid Guangzhou 510663 Peoples R China Power Res Inst China Southern Power Grid Guangzhou 510080 Peoples R China

gravitational search algorithm (GSA) is an evolutionary algorithm developed to solve the global optimization problems, but still suffers from the premature convergence problem due to the loss of swarm diversity. In order to improve the GSA performance, this paper develops a novel multi-strategy gravitational search algorithm (MGSA) where the Levy flight strategy is adopted to increase the local search ability of the global best-known agent;and then the mutation strategy is used to improve the swarm diversity in the evolutionary process;finally, the elitism selection strategy is used to enhance the exploration ability and convergence speed of the swarm. The MGSA method is compared with several methods in 24 famous benchmark functions, and the results demonstrate the superiority of the MGSA method in both search ability and convergence rate. Next, the MGSA method is used to solve the ecological operation problem of the Wu hydropower system. The results indicate that compared with several existing methods, MGSA can obtain better scheduling schemes to make obvious reductions in the inappropriate ecological water volume in different scenarios. Thus, this paper provides a new effective tool for the complex engineering optimization problems.

关键词： Cascade hydropower reservoirs Ecological operation gravitational search algorithm Levy flight local search strategy Mutation operation Elitism selection strategy

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Improving exploration and exploitation via a Hyperbolic gravitational search algorithm

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KNOWLEDGE-BASED SYSTEMS 2020年 193卷 105404-105404页

作者： Pelusi, Danilo Mascella, Raffaele Tallini, Luca Nayak, Janmenjoy Naik, Bighnaraj Deng, Yong Univ Teramo Fac Commun Sci Teramo Italy Aditya Inst Technol & Management Dept Comp Sci & Engn Tekkali K Kotturu India Veer Surendra Sai Univ Technol Dept Comp Applicat Burla Odisha India Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu Peoples R China

Finding the best compromise between exploration and exploitation phases of a search algorithm is a hard task. The gravitational search algorithm (GSA) is an evolutionary algorithm based on the Newton's universal law of gravitation. Many studies show that GSA suffers from slow exploitation which generates premature convergence. This paper proposes a Hyperbolic gravitational search algorithm (HGSA) able to find an optimal balance between exploration and exploitation. The main contributions of this work are: the definition of suitable hyperbolic acceleration coefficients, the dynamic regulation of the gravitational constant coefficient through hyperbolic function and the definition of a decreasing hyperbolic function for the best agents which attract the other agents. The proposed algorithm is compared with well-known search algorithms on classical benchmark functions, CEC-06 2019 benchmark functions and three-bar truss design problem. The results show that HGSA is better than other algorithms in terms of optimization and convergence performances. (c) 2019 Elsevier B.V. All rights reserved.

关键词： Evolutionary algorithms gravitational search algorithm Optimization algorithms Exploration and exploitation Hyperbolic functions

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An integrated feedforward-feedback control structure utilizing a simplified global gravitational search algorithm to control nonlinear systems

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2020年第1期45卷 1-16页

作者： Lutfy, Omar Farouq Univ Technol Baghdad Control & Syst Engn Dept Baghdad Iraq

This paper presents an integrated feedforward-feedback control structure to control nonlinear dynamical systems. This intelligent control system exploits a modified recurrent wavelet neural network (MRWNN) in the feedforward (FF) and the feedback (FB) loops of the control structure. Specifically, the MRWNN is proposed to boost the approximation performance of a previously reported network by employing two amendments to the original structure. To optimize the parameters of both the FF and the FB controllers, an enhanced version of the gravitational search algorithm (GSA) is developed to improve the searching capability of the original algorithm. In particular, two modifications were adopted, including the removal of two control parameters related to the gravitational constant in the original algorithm and the utilization of the global best solution to constitute the next generation of agents. Hence, the proposed algorithm is called the simplified global gravitational search algorithm (SGGSA), which has demonstrated better optimization performance compared to those of other techniques, including the original GSA. By conducting several evaluation tests using different nonlinear time-variant dynamical systems, the effectiveness of the proposed control structure was confirmed in terms of control precision and robustness against external disturbances. In addition, the MRWNN has exhibited a superior control performance compared with other related controllers.

关键词： Integrated feedforward-feedback control structure wavelet neural network recurrent wavelet neural network gravitational search algorithm genetic algorithm

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Optimal object detection and tracking in occluded video using DNN and gravitational search algorithm

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SOFT COMPUTING 2020年第24期24卷 18301-18320页

作者： Mahalingam, T. Subramoniam, M. Sathyabama Univ Chennai Tamil Nadu India

Moving object tracking is an effective optimization procedure based on the impermanent relevant information associated with the original frames. Suggesting a method with efficient accuracy in convoluted atmospheres is a difficulty for scientists in the area of research study. In this research, powerful object detection and movement tracking videos are proposed. Here, we are considering the input video sequence PETS and Hall monitor videos. Initially, the background and foreground separations are done by modified kernel fuzzy c-means algorithm. The object detection and tracking are done by gravitational search algorithm-based deep belief neural network. The implementation will be in MATLAB. The effectiveness of the recommended strategy is assessed with means of precision, recall, F-measure, FPR, FNR, PWC, FAR, similarity, specificity, and accuracy. From the experimental results, the proposed work outperforms the state of artwork. Here, the proposed method attains maximum precision and recall value for both PETS and Hall monitor video when compared to the existing algorithm.

关键词： Kernel fuzzy c-means algorithm Background and foreground separation Deep belief neural network gravitational search algorithm

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