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A discrete gravitational search algorithm for the blocking flow shop problem with total flow time minimization

APPLIED INTELLIGENCE 2019年第9期49卷 3362-3382页

作者： Zhao, Fuqing Xue, Feilong Zhang, Yi Ma, Weimin Zhang, Chuck Song, Houbin Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou 730050 Gansu Peoples R China Xijin Univ Sch Mechn Engn Xian 710123 Shaanxi Peoples R China Tongji Univ Sch Econ & Management Shanghai 200092 Peoples R China Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA

The blocking flow shop problem (BFSP) is one of the key models in the flow shop scheduling problem in the manufacturing systems. gravitational search algorithm (GSA) is an algorithm based on the population for solving various optimization problems. However, GSA is scarcely applied to solve the BFSP as it is designed to solve the continuous problems. In this paper, a Discrete gravitational search algorithm (DGSA) is presented for solving the BFSP with the total flow time minimization. A new variable profile fitting (VPF) combined with NEH heuristic, named VPF _ NEH(n), is introduced for balancing the quality and the diversity of the initial population to configure the DGSA. The three operators including the variable neighborhood operators (VNO), the path relinking and the plus operator are implemented during the location updating of the candidates. The objective of the operation is to prevent the premature convergence of the population and to balance the exploration and exploitation in the process of optimization. The expected runtime of the DGSA is analyzed by the level-based theorem. The simulated results indicate that the effectiveness and superiority of the DGSA.

关键词： gravitational search algorithm Blocking flow shop problem Total flow time Constructive heuristic Variable neighborhood search

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A novel hybrid K-harmonic means and gravitational search algorithm approach for clustering

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第8期38卷 9319-9324页

作者： Yin, Minghao Hu, Yanmei Yang, Fengqin Li, Xiangtao Gu, Wenxiang NE Normal Univ Coll Comp Sci Changchun 130117 Peoples R China

Clustering is used to group data objects into sets of disjoint classes called clusters so that objects within the same class are highly similar to each other and dissimilar from the objects in other classes. K-harmonic means (KHM) is one of the most popular clustering techniques, and has been applied widely and works well in many fields. But this method usually runs into local optima easily. A hybrid data clustering algorithm based on an improved version of gravitational search algorithm and KHM, called IGSAKHM, is proposed in this research. With merits of both algorithms, IGSAKHM not only helps the KHM clustering escape from local optima but also overcomes the slow convergence speed of the IGSA. The proposed method is compared with some existing algorithm on seven data sets, and the obtained results indicate that IGSAKHM is superior to KHM and PSOKHM in most cases. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Clustering K-harmonic means gravitational search algorithm IGSAKHM algorithm

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Optimal static state estimation using improved particle swarm optimization and gravitational search algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2013年第1期52卷 254-265页

作者： Mallick, Sourav Ghoshal, S. P. Acharjee, P. Thakur, S. S. Natl Inst Technol Dept Elect Engn Durgapur 713209 India

In this paper, two novel evolutionary search techniques based on Improved Particle Swarm Optimization (IPSO) algorithm and gravitational search algorithm (GSA), have been proposed to solve the static State Estimation (SE) problem as an optimization problem. The proposed methods are tested on five IEEE standard test systems along with two ill-conditioned test systems under different simulated conditions and the results are compared with the same of standard Weighted Least Square State Estimation (WLS-SE) technique, Particle Swarm Optimization (PSO) based SE and Hybrid Particle Swarm Optimization gravitational search algorithm (PSOGSA) based SE technique. The optimization performance and the statistical error analysis show the superiority of the proposed GSA based SE technique over the other two techniques. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Static state estimation Improved particle swarm optimization gravitational search algorithm Ill-conditioned system

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A memory-based gravitational search algorithm for enhancing minimum variance distortionless response beamforming

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APPLIED SOFT COMPUTING 2016年第0期47卷 103-118页

作者： Darzi, Soodabeh Kiong, Tiong Sieh Islam, Mohammad Tariqul Soleymanpour, Hassan Rezai Kibria, Salehin Univ Kebangsaan Malaysia Ctr Space Sci Bangi 43600 Malaysia Univ Tenaga Nas Coll Engn Ctr Syst & Machine Intelligence Selangor Bandar Baru Ban Malaysia Univ Kebangsaan Malaysia Dept Elect Elect & Syst Engn Bangi 43600 Malaysia Semnan Univ Dept Elect Engn Semnan Iran

This paper introduces a memory-based version of gravitational search algorithm (MBGSA) to improve the beamforming performance by preventing loss of optimal trajectory. The conventional gravitational search algorithm (GSA) is a memory-less heuristic optimization algorithm based on Newton's laws of gravitation. Therefore, the positions of agents only depend on the optimal solutions of previous iteration. In GSA, there is always a chance to lose optimal trajectory because of not utilizing the best solution from previous iterations of the optimization process. This drawback reduces the performance of GSA when dealing with complicated optimization problems. However, the MBGSA uses the overall best solution of the agents from previous iterations in the calculation of agents' positions. Consequently, the agents try to improve their positions by always searching around overall best solutions. The performance of the MBGSA is evaluated by solving fourteen standard benchmark optimization problems and the results are compared with GSA and modified GSA (MGSA). It is also applied to adaptive beamforming problems to improve the weight vectors computed by Minimum Variance Distortionless Response (MVDR) algorithm as a real world optimization problem. The proposed algorithm demonstrates high performance of convergence compared to GSA and Particle Swarm Optimization (PSO). (C) 2016 Elsevier B.V. All rights reserved.

关键词： gravitational search algorithm Minimum variance distortionless response Adaptive beamforming Particle Swarm Optimization Heuristic algorithm Artificial intelligence

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Design of Posicast PID control systems using a gravitational search algorithm

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NEUROCOMPUTING 2015年 167卷 18-23页

作者： de Moura Oliveira, P. B. Solteiro Pires, E. J. Novais, Paulo UTAD Univ Dept Engn Sch Sci & Technol INESC TEC UTAD Pole P-5001801 Vila Real Portugal Univ Minho Dept Informat ALGORITMI Ctr Braga Portugal

In this paper we propose the gravitational search algorithm to design PID control structures. The controller design is performed considering the objectives of set-point tracking and disturbance rejection, minimizing the integral of the absolute error criterion. A two-degrees-of-freedom control configuration with a feedforward pre-filter inserted outside the PID feedback loop is used to improve system performance for both design criteria. The pre-filter used is a Posicast controller designed simultaneously with a PID controller. Simulation results are presented which show the proposed technique merit. (C) 2015 Elsevier B.V. All rights reserved.

关键词： gravitational search algorithm PID control Posicast control

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GGSA: A Grouping gravitational search algorithm for data clustering

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2014年第0期36卷 114-121页

作者： Dowlatshahi, Mohammad Bagher Nezamabadi-pour, Hossein Lorestan Univ Dept Comp Engn Khorramabad Iran Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

gravitational search algorithm (GSA) is a stochastic population-based metaheuristic designed for solving continuous optimization problems. It has a flexible and well-balanced mechanism for enhancing exploration and exploitation abilities. In this paper, we adapt the structure of GSA for solving the data clustering problem, the problem of grouping data into clusters such that the data in each cluster share a high degree of similarity while being very dissimilar to data from other clusters. The proposed algorithm, which is called Grouping GSA (GGSA), differs from the standard GSA in two important aspects. First, a special encoding scheme, called grouping encoding, is used in order to make the relevant structures of clustering problems become parts of solutions. Second, given the encoding, special GSA updating equations suitable for the solutions with grouping encoding are used. The performance of the proposed algorithm is evaluated through several benchmark datasets from the well-known UCI Machine Learning Repository. Its performance is compared with the standard GSA, the Artificial Bee Colony (ABC), the Particle Swarm Optimization (PSO), the Firefly algorithm (FA), and nine other well-known classical classification techniques from the literature. The simulation results indicate that GGSA can effectively be used for multivariate data clustering. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： gravitational search algorithm Grouping encoding Data clustering Nature-inspired algorithm Swarm Intelligence

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A Novel Distributed gravitational search algorithm With Multi-Layered Information Interaction

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IEEE ACCESS 2021年 9卷 166552-166565页

作者： Li, Xiaosi Yang, Haichuan Li, Jiayi Wang, Yirui Gao, Shangce Wesoft Co Ltd Dept Engn Kawasaki Kanagawa 2100024 Japan Univ Toyama Fac Engn Toyama 9308555 Japan Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Zhejiang Peoples R China

Population structures play a crucial role in individuals' evolution. gravitational search algorithm (GSA) inspired by physical laws is a population-based algorithm. Its population structure is able to influence the individuals' search behavior. In this paper, we propose a distributed GSA with multi-layered information interaction, termed as MGSA, to offer a good balance between exploitation and exploration. A historical information layer and an elite top layer are designed to improve individuals' interaction. A distributed structure maintains the population diversity. Experimental results on CEC2017 benchmark functions and a real-world static economic dispatch problem confirm the effectiveness of MGSA in comparison with several state-of-the-art algorithms. In addition, landscape search trajectory and population diversity analyses verify the excellent convergence of MGSA.

关键词： Statistics Sociology Heuristic algorithms Optimization Convergence Chaotic communication Licenses Information interaction population structure hierarchical structure gravitational search algorithm meta-heuristic algorithms

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Case-based reasoning adaptation based on fuzzy gravitational search algorithm for disaster emergency plan

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第6期40卷 11007-11022页

作者： Yu, Xiaobing Yu, Xianrui Zhang, Xueying Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing Peoples R China Nanjing Univ Informat Sci & Technol Collaborat Innovat Ctr Forecast & Evaluat Meteoro Nanjing Peoples R China

Disasters can result in substantial destructive damages to the world. Emergency plan is vital to deal with these disasters. It is still difficult for the traditional CBR to generate emergency plans to meet requirements of rapid responses. An integrated system including Case-based reasoning (CBR) and gravitational search algorithm (GSA) is proposed to generate the disaster emergency plan. Fuzzy GSA (FGSA) is developed to enhance the convergence ability and accomplish the case adaptation in CBR. The proposed algorithm dynamically updates the main parameters of GSA by introducing a fuzzy system. The FGSA-CBR system is proposed, in which fitness function is defined based on the effectiveness of disaster emergency management. The comparison results have revealed that the proposed algorithm has good performances compared with the original GSA and other algorithms. A gas leakage accident is taken as an empirical study. The results have demonstrated that the FGSA-CBR has good performances when generating the disaster emergency plan. The combination of CBR and FGSA can realize the case adaptation, which provides a useful approach to the real applications.

关键词： Emergency plan case-based reasoning adaptation gravitational search algorithm

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

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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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