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An enhanced quantum-inspired gravitational search algorithm for color prediction based on the absorption spectrum

TEXTILE REsearch JOURNAL 2021年第11-12期91卷 1211-1226页

作者： Gao, Zehai Zhang, Yan Zhou, Shisheng Lyu, Wei Xian Univ Technol Jinhua South Rd 5 Xian 710048 Shaanxi Peoples R China Shenzhen Jinjia Grp Co Ltd Shenzhen Peoples R China

Spot color is widely applied to printing and packing in modern industry, which can satisfy the individualization requirements and express the emotion of products. Color prediction is the core technique for spot color restoration. In this paper, a method that combines the least squares method and gravitation search algorithm is proposed to address the color prediction by using the absorption spectrum. Firstly, the spectral transmittance of the thin film with high transmission and low reflectance characteristics is researched to find the absorbance. Secondly, the least squares method is used to ascertain the primary colors of the spot color. Thirdly, an enhanced quantum gravitation search algorithm is designed to predict the spot color. The predicted results on the 30 spot colors show that the proposed method has higher accuracy in comparison with the three existed methods. The color differences between the prepared spot colors and the reproduced spot colors are all less than 3, in which 75% of the color differences are less 1 and 35% of the color differences are less 0.1. All the results confirm that the proposed method can predict the spot color accurately.

关键词： color prediction gravitational search algorithm absorption spectrum color difference

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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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Breast tissue density classification based on gravitational search algorithm and deep learning: a novel approach

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International Journal of Information Technology (Singapore) 2022年第7期14卷 3481-3493页

作者： Kate, Vandana Shukla, Pragya Acropolis Institute of Technology and Research Indore India Institute of Technology & Research Devi Ahilya Vishwavidyalaya Indore India

This work presents the automatic classification of mammographic breast tissue density as it plays a crucial role in morphological analysis for abnormality detection. The proposed work consists of pre-processing, mammogram enhancement, and classification. Image pre-processing is used to extract the foreground image from the original image. Image extraction is a well known optimization problem. In order to capture the aforementioned problem, gravitation search algorithm is adopted with the consideration of kapur’s entropy as a fitness function. Afterwards, mammogram enhancement and noise removal is performed by utilizing unsharp masking and Anisotrophic filtering technique. Finally, VGG16 and InceptionV3 were utilised to train the convolution neural network (CNN) classification model using deep transfer learning. For a four class classification of breast tissue density, the suggested method is evaluated on the dataset from Digital Image Database for Screening Mammography (DDSM). It obtains classification accuracy of 97.98% for InceptionV3 based model and 91.92% for VGG16 based model on DDSM dataset. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Deep transfer learning gravitational search algorithm InceptionV3 Mammograms VGG16

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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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MAR-GSA: Mixed attraction and repulsion based gravitational search algorithm

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INFORMATION SCIENCES 2024年 662卷

作者： Qian, Zhiqiang Xie, Yongfang Xie, Shiwen Cent South Univ Sch Automat Changsha 410083 Peoples R China

As a population-based stochastic optimization algorithm, gravitational search algorithm (GSA) has attracted numerous interests and has been applied in various applications. However, GSA has drawbacks such as uneven search and premature convergence in practical applications. This paper specifically explains the inherent characteristic of GSA in prioritizing the center position. Correspondingly, an improvement strategy of fitness normalization with mass shift is proposed, creating a situation where gravity and repulsion are mixed. Then, the global best mechanism with weights is incorporated into the particle's velocity update formula, which compensates for the difficulties in the later exploitation stage. Finally, an empirical formula for the initial gravitational constant related to the size of the solution space is proposed, which enhances the global search ability together with the former strategy. 12 shifted benchmark functions are used to construct 20 optimization problems ranging from 2 to 120 dimensions. The average performance of the proposed algorithm, other GSA and well-known algorithms are compared under the same budget. The results demonstrate that the proposed GSA not only effectively addresses the drawbacks of GSA and maintains good performance, but also exhibits strong competitiveness compared to various similar algorithms.

关键词： Center-biased search characteristic Mixed attraction and repulsion Shift transformation Population-based algorithm gravitational search algorithm

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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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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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Levy flight incorporated hybrid learning model for gravitational search algorithm

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KNOWLEDGE-BASED SYSTEMS 2023年第1期265卷

作者： Joshi, Susheel Kumar Indian Inst Informat Technol Kottayam Dept Computat Sci & Humanities Kottayam 686635 Kerala India

gravitational search algorithm (GSA) is a widely used meta-heuristic algorithm for global optimization. Its strong social interaction abilities and easy to implement nature make it more applicable than its contemporaries. However, multi-modality always remains a challenging task for GSA search mechanism due to its incapabilities towards premature convergence. This paper proposes a novel GSA variant called 'Levy flight incorporated gravitational search algorithm with an adaptive spiral strategy (LevyGSA)' to address the shortcomings of GSA with the following developments: First, a levy flight associated position update strategy for elite agents of the swarm is proposed for a better interior search. Secondly, an adaptive spiral update strategy is introduced for the rest swarm to balance the trade-off between exploration and exploitation for a robust search. Finally, a dimensional reduction based strategy for enhancing the local search around the known global optimal region is introduced. The proposed algorithm is tested over 23 classical test problems and 30 CEC 2014 test problems. The numerical results demonstrate the outstanding performance of the proposed algorithm through which it outperforms the well-known existing meta-heuristics along with recent GSA variants. Furthermore, finding more accurate solutions for five engineering design problems validates its applicability in real-world scenarios.(c) 2023 Elsevier B.V. All rights reserved.

关键词： gravitational search algorithm Elite levy flight update strategy Spiral adaptive strategy Meta -heuristics Stochastic optimization

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