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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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Multi-Objective Operation of Cascade Hydropower Reservoirs Using TOPSIS and gravitational search algorithm with Opposition Learning and Mutation

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WATER 2019年第10期11卷 2040-2040页

作者： Feng, Zhong-kai Liu, Shuai Niu, Wen-jing Jiang, Zhi-qiang Luo, Bin Miao, Shu-min Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Hubei Peoples R China ChangJiang Water Resources Commiss Bur Hydrol Wuhan 430010 Hubei Peoples R China Tsinghua Sichuan Energy Internet Res Inst Chengdu 610200 Sichuan Peoples R China State Grid Sichuan Elect Power Res Inst Chengdu 610072 Sichuan Peoples R China

In this research, a novel enhanced gravitational search algorithm (EGSA) is proposed to resolve the multi-objective optimization model, considering the power generation of a hydropower enterprise and the peak operation requirement of a power system. In the proposed method, the standard gravity search algorithm (GSA) was chosen as the fundamental execution framework;the opposition learning strategy was adopted to increase the convergence speed of the swarm;the mutation search strategy was chosen to enhance the individual diversity;the elastic-ball modification strategy was used to promote the solution feasibility. Additionally, a practical constraint handling technique was introduced to improve the quality of the obtained agents, while the technique for order preference by similarity to an ideal solution method (TOPSIS) was used for the multi-objective decision. The numerical tests of twelve benchmark functions showed that the EGSA method could produce better results than several existing evolutionary algorithms. Then, the hydropower system located on the Wu River of China was chosen to test the engineering practicality of the proposed method. The results showed that the EGSA method could obtain satisfying scheduling schemes in different cases. Hence, an effective optimization method was provided for the multi-objective operation of hydropower system.

关键词： cascade hydropower reservoirs multi-objective optimization TOPSIS gravitational search algorithm opposition learning partial mutation elastic-ball modification

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Performance analysis and optimization for maximum exergy efficiency of a geothermal power plant using gravitational search algorithm

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ENERGY CONVERSION AND MANAGEMENT 2019年第Apr.期185卷 155-168页

作者： Ozkaraca, Osman Kecebas, Ali Mugla Sitki Kocman Univ Technol Fac Dept Informat Syst Engn TR-48000 Mugla Turkey Mugla Sitki Kocman Univ Technol Fac Dept Energy Syst Engn TR-48000 Mugla Turkey

The limited energy resources globally, low efficiency of renewable energies, complicated and costly energy conversion systems and environmental pollution have significantly increased scholar's interest in innovative and efficient systems and their improvement studies. Therefore, it is necessary to increase the efficiency of power generation systems used in geothermal sources of medium or low enthalpy. This study aims to improve the thermodynamic performance of an existing binary geothermal system with organic Rankine cycle and its system components while trying to comprehend the physical events/changes during these improvement processes. A model has been developed that simulates the system completely and accurately. Seventeen system parameters which were considered as crucial to maximize the exergy efficiency of the system like turbine inlet, condenser temperature and so on, are optimized using a gravitational search algorithm. The results of the study show that the exergy efficiency of the system is 14% and thus it can be maximized to 31% with optimization. During the optimization process, the pressure of work fluid on the evaporator line is increased and thus 2.1 MW more power is produced compared to normal power production. The condenser, with the highest exergy destruction in the system, has performance improvements of 75%. As a result, with the optimization process, a more compatible operating strategy between system components is ensured. This will allow the system and its components to run for longer and without failures.

关键词： Geothermal power plant Exergy efficiency Optimization Improvement strategy gravitational search algorithm

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Channel assignment in multi-radio wireless mesh networks using an improved gravitational search algorithm

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2014年第0期38卷 163-171页

作者： Doraghinejad, Mohammad Nezamabadi-pour, Hossein Mahani, Ali Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

Wireless mesh networks have emerged recently to improve networking performance and other networking services. The combination of multi-radio nodes with multi-hop mesh architectures leads researcher to overcome some constraints of single-radio networks such as incapability of effective scale to exploit the increasing available system bandwidth. Therefore, a good channel assignment (CA) in multi-radio mesh networks can reduce the number of interference co-channels and improve the network throughput. In this paper, an improved version of gravitational search algorithm (IGSA) is proposed to solve CA problems;a local operator is combined with the gravitational search algorithm to find the best solution. In this study, the main goal is to minimize the overall interference and to increase the network throughput with ensuring network connectivity. The obtained results confirm the high performance of our algorithm in comparison with other related works. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Wireless mesh networks Channel assignment Multi-radios Discrete local search 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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Binary optimization using hybrid particle swarm optimization and gravitational search algorithm

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NEURAL COMPUTING & APPLICATIONS 2014年第6期25卷 1423-1435页

作者： Mirjalili, Seyedali Wang, Gai-Ge Coelho, Leandro dos S. Griffith Univ Sch Informat & Commun Technol Brisbane Qld 4111 Australia Jiangsu Normal Univ Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Pontifical Catholic Univ Parana PUCPR Ind & Syst Engn Grad Program PPGEPS Curitiba Parana Brazil Fed Univ Parana UFPR Dept Elect Engn Elect Engn Grad Program PPGEE Polytech Ctr Curitiba Parana Brazil

The PSOGSA is a novel hybrid optimization algorithm, combining strengths of both particle swarm optimization (PSO) and gravitational search algorithm (GSA). It has been proven that this algorithm outperforms both PSO and GSA in terms of improved exploration and exploitation. The original version of this algorithm is well suited for problems with continuous search space. Some problems, however, have binary parameters. This paper proposes a binary version of hybrid PSOGSA called BPSOGSA to solve these kinds of optimization problems. The paper also considers integration of adaptive values to further balance exploration and exploitation of BPSOGSA. In order to evaluate the efficiencies of the proposed binary algorithm, 22 benchmark functions are employed and divided into three groups: unimodal, multimodal, and composite. The experimental results confirm better performance of BPSOGSA compared with binary gravitational search algorithm (BGSA), binary particle swarm optimization (BPSO), and genetic algorithm in terms of avoiding local minima and convergence rate.

关键词： Binary optimization Binary algorithms PSOGSA Particle swarm optimization gravitational search algorithm

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Incremental gravitational search algorithm for high-dimensional benchmark functions

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NEURAL COMPUTING & APPLICATIONS 2019年第8期31卷 3779-3803页

作者： Ozyon, Serdar Yasar, Celal Temurtas, Hasan Dumlupinar Univ Elect & Elect Engn Dept TR-43100 Kutahya Turkey Dumlupinar Univ Comp Engn Dept TR-43100 Kutahya Turkey

In this study, gravitational search algorithm (GSA), which is a powerful optimization algorithm developed in recent years and based on physics, is improved by integrating the incremental social learning structure. In this improvement, new agents have been added to the GSA starting from the first population at certain steps, agent insertion on the maximum population number has been terminated, and the search has been continued until the desired function call is accomplished. This improved algorithm, which is a recent version of the GSA, is named as the incremental gravitational search algorithm (IGSA). The process of adding agent to the population has been performed with three different approaches. Results of the 30-dimensional test functions, which are solved by the GSA in the literature, are compared with the obtained results of IGSA, developed for each approach. Thereafter, the dimensions of the same test functions have been increased (50 and 100 dimensions) and resolved with IGSA, and the results are discussed.

关键词： Optimization Meta-heuristic algorithms gravitational search algorithm Incremental social learnings (ISL) High-dimension benchmark functions

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An automatic nuclei segmentation method using intelligent gravitational search algorithm based superpixel clustering

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SWARM AND EVOLUTIONARY COMPUTATION 2019年 45卷 15-32页

作者： Mittal, Himanshu Saraswat, Mukesh Jaypee Inst Informat Technol Noida India

A reliable nuclei segmentation is still an open-ended problem, especially in the breast cancer histology images. For the same, this paper proposes an intelligent gravitational search algorithm based superpixel clustering method for automatic nuclei segmentation. In the proposed method, a novel variant of gravitational search algorithm, intelligent gravitational search algorithm, is employed to obtain the optimal cluster centroids. The experimental and statistical results evince that the proposed variant surpasses existing meta-heuristic algorithms on 47 benchmark functions belonging to different problem categories i.e., unimodal, multimodal, and real-parameter single objective optimization problems of CEC, 2013. Further, the segmentation accuracy of the proposed method is examined on H&E stained estrogen receptor positive (ER+) breast cancer images. Experiments affirm that the proposed method is comparatively an efficacious and accurate method for segmenting the nuclei within breast cancer histology images.

关键词： Nuclei segmentation Breast cancer histology images Superpixel clustering gravitational search algorithm

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Memetic algorithms for training feedforward neural networks: an approach based on gravitational search algorithm

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NEURAL COMPUTING & APPLICATIONS 2021年第7期33卷 2561-2588页

作者： Garcia-Rodenas, Ricardo Jimenez Linares, Luis Alberto Lopez-Gomez, Julio Univ Castilla La Mancha Dept Math Ciudad Real Spain Univ Castilla La Mancha Dept Informat Technol & Syst Ciudad Real Spain

The backpropagation (BP) algorithm is a gradient-based algorithm used for training a feedforward neural network (FNN). Despite the fact that BP is still used today when FNNs are trained, it has some disadvantages, including the following: (i) it fails when non-differentiable functions are addressed, (ii) it can become trapped in local minima, and (iii) it has slow convergence. In order to solve some of these problems, metaheuristic algorithms have been used to train FNN. Although they have good exploration skills, they are not as good as gradient-based algorithms at exploitation tasks. The main contribution of this article lies in its application of novel memetic approaches based on the gravitational search algorithm (GSA) and Chaotic gravitational search algorithm (CGSA) algorithms, called respectively Memetic gravitational search algorithm (MGSA) and Memetic Chaotic gravitational search algorithm (MCGSA), to train FNNs in three classical benchmark problems: the XOR problem, the approximation of a continuous function, and classification tasks. The results show that both approaches constitute suitable alternatives for training FNNs, even improving on the performance of other state-of-the-art metaheuristic algorithms such as ParticleSwarm Optimization (PSO), the Genetic algorithm (GA), the Adaptive Differential Evolution algorithm with Repaired crossover rate (Rcr-JADE), and the Covariance matrix learning and Bimodal distribution parameter setting Differential Evolution (COBIDE) algorithm. Swarm optimization, the genetic algorithm, the adaptive differential evolution algorithm with repaired crossover rate, and the covariance matrix learning and bimodal distribution parameter setting differential evolution algorithm.

关键词： Feedforward neural networks Memetic algorithms gravitational search algorithm Quasi-Newton methods

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Chaotic secure communication based on a gravitational search algorithm filter

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2012年第4期25卷 766-774页

作者： Han, XiaoHong Chang, XiaoMing Taiyuan Univ Technol Coll Comp Sci & Technol Taiyuan 030024 Shanxi Peoples R China Taiyuan Univ Technol Coll Software Taiyuan 030024 Shanxi Peoples R China

A new chaotic secure communication scheme based on a gravitational search algorithm (GSA) filter is proposed. In this scheme, useful signals are delivered via an encoder, a chaotic transmitter, a GSA-based filter, a chaotic receiver, and a decoder. The security of such a communication system is promoted due to the unpredictable features of the chaotic map and the unknown encoding-modulation scheme. By using a GSA filter technique the resistance of the system to noise is enhanced. To verify the effectiveness of the proposed scheme, it is compared with the current state-of-the-art schemes in simulations. At the same time, comparisons with a genetic algorithm (GA) filter and a particle swarm optimization (PSO) filter are made. Numerical simulations confirm that the proposed method is better in estimating the states and information symbols, and has a lower bit error rate than other schemes. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Secure communication Message estimation gravitational search algorithm Chaos

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