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Automatic fabric defect detection using optimal Gabor filter based on hybrid beetle antennae search-gravitational search algorithm

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JOURNAL OF OPTICS-INDIA 2023年第4期52卷 1667-1675页

作者： Kim, Jongchol Jo, Hyok Ri, Jinmyong Han, Kyongwon Univ Sci Dept Control Sci Pyongyang 999093 North Korea

In this paper, a method for detecting defects in fabric surface is presented using an optimal Gabor filter based on hybrid BAS-GSA (Beetle Antennae search algorithm-gravitational search algorithm). The method can be specifically divided into a training stage and a detection stage. Firstly, in training stage, a two-dimensional Gabor filter is constructed for defect-free fabric images. And then, the parameters of the Gabor filter are optimized using a combination model of BAS (Beetle Antennae search algorithm) and GSA (gravitational search algorithm) with strong global and local search function. In the detection stage, a Gabor filter is constructed from the optimal parameters obtained in training stage, and the filter is used to convolve the fabric image to be detected;and then, the obtained convolution image is binarized to finally identify whether the fabric to be tested has defects. Experimental results show that the detection rate of this method can reach 98%, which has good stability and robustness, and is suitable for industrial production.

关键词： Gabor filters Beetle antennae search algorithm gravitational search algorithm Fabric defect detection BAS-GSA

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Elite-of-the-Elites Driven Five-Layered gravitational search algorithm for Optimization

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2023年第12期18卷 1958-1960页

作者： Zhong, Lin Sui, Qingya Yu, Jiatianyi Wang, Rong-Long Lei, Zhenyu Gao, Shangce Univ Toyama Fac Engn Toyama 9308555 Japan Univ Fukui Fac Engn Fukui 9108507 Japan

The gravitational search algorithm (GSA) is a popular optimization method inspired by celestial interactions. And the MLGSA is an outstanding variant of GSA. To improve its performance, we propose the elite-of-the-elites driven five-layered gravitational search algorithm (EFGSA). EFGSA introduces a new population layer comprising elite individuals, improving the balance between exploration and exploitation. Experimental results on the 2017 IEEE Conference on Evolutionary Computation (CEC) benchmark functions demonstrate EFGSA's superiority over the MLGSA algorithm in terms of solution quality and convergence speed. The obtained results underscore the efficacy of EFGSA in addressing optimization problems. (c) 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

关键词： evolutionary algorithm gravitational search algorithm exploration and exploitation population structure

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Error-free and mean value based reversible data hiding using gravitational search algorithm in encrypted images

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第21期81卷 30749-30768页

作者： Raju, K. Upendra Prabha, N. Amutha VIT SENSE Vellore Tamil Nadu India VIT Univ Sch Elect Engn Vellore Tamil Nadu India

In recent years the data hiding on encrypted image is significant topic for data security. Due to its capability for preserving confidentiality, Reversible Data Hiding (RDH) on encrypted domain will helpful on cloud computing as an emerging technology. In this paper, an error free and mean value based RDH process with high capacity on encrypted images is proposed. Here, the image provider (content owner), data hider and data receiver are three parties. Initially, the image provider keeps the averages to obtain the modified image. Encrypt the modified image further through the aid of the encryption key and pass it to data hider. In data hider, gravitational search algorithm (GSA) is utilized to find the best pixel locations in encrypted image for information hiding. Moreover, the similar encrypted image size, an alpha channel is created and then combines the encrypted image and the alpha channel for embedding secret data to make an encrypted image with embedded data. On receiver side, through the aid of encryption key, the receiver recovers the image by decrypting the encrypted image and also the secret bits are removed through the aid of data hiding keys by data extraction. The experimental result demonstrates that proposed system has better performance compared to other state-of- art strategies. The peak signal-to-noise ratio (PSNR) achieved by the proposed system is better than 8.36% and 18.3% compared to other existing works.

关键词： Reversible data hiding Data security Encrypted images Alpha channel Content owner gravitational search algorithm

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A seasonal ARIMA model based on the gravitational search algorithm (GSA) for runoff prediction

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WATER SUPPLY 2022年第8期22卷 6959-6977页

作者： Zhang, Xianqi Wu, Xilong Zhu, Guoyu Lu, Xiaobin Wang, Kai North China Univ Water Resources & Elect Power Sch Water Conservancy Zhengzhou 450046 Peoples R China Prov & Minist Cobuilt Collaborat Innovat Ctr Yellow River Basin Water Resources Efficient Util Zhengzhou 450046 Peoples R China Henan Water Conservancy & Water Transportat Engn Zhengzhou 450046 Peoples R China Sichuan Univ Coll Water Resource & Hydropower State Key Lab Hydraul & Mt River Engn Chengdu Sichuan Peoples R China

The prediction of river runoff is crucial for flood forecasting, agricultural irrigation and hydroelectric power generation. Coupled runoff prediction model based on the gravitational search algorithm (GSA) and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model is proposed to address the non-linear and seasonal features of runoff data. The GSA has a significant local optimisation capability, while the SARIMA model allows for real-time adjustment of the model using historical data and is suitable for analysing time series with seasonal variations. Consequently, the GSA-SARIMA model was developed and applied to the runoff prediction of the Xianyang section of the Wei River. The results suggest that the GSA-SARIMA model achieves a linear correlation coefficient of 0.9351, a Nash efficiency coefficient of 0.91, a mean relative error of 6.57 and a root mean square error of 0.21. All of the evaluation indicators of this model outperform the other models developed, and its application to actual runoff prediction is feasible, which creates a new path for runoff prediction.

关键词： ARIMA model gravitational search algorithm runoff prediction seasonal differencing Wei River

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Reinforcement Learning-based control using Q-learning and gravitational search algorithm with experimental validation on a nonlinear servo system

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INFORMATION SCIENCES 2022年 583卷 99-120页

作者： Zamfirache, Iuliu Alexandru Precup, Radu-Emil Roman, Raul-Cristian Petriu, Emil M. Politehn Univ Timisoara Dept Automat & Appl Informat Bd V Parvan 2 Timisoara 300223 Romania Univ Ottawa Sch Elect Engn & Comp Sci 800 King Edward Ottawa ON K1N 6N5 Canada

This paper presents a novel Reinforcement Learning (RL)-based control approach that uses a combination of a Deep Q-Learning (DQL) algorithm and a metaheuristic gravitational search algorithm (GSA). The GSA is employed to initialize the weights and the biases of the Neural Network (NN) involved in DQL in order to avoid the instability, which is the main drawback of the traditional randomly initialized NNs. The quality of a particular set of weights and biases is measured at each iteration of the GSA-based initialization using a fitness function aiming to achieve the predefined optimal control or learning objective. The data generated during the RL process is used in training a NN-based controller that will be able to autonomously achieve the optimal reference tracking control objective. The proposed approach is compared with other similar techniques which use different algorithms in the initialization step, namely the traditional random algorithm, the Grey Wolf Optimizer algorithm, and the Particle Swarm Optimization algorithm. The NN-based controllers based on each of these techniques are compared using performance indices specific to optimal control as settling time, rise time, peak time, overshoot, and minimum cost function value. Real-time experiments are conducted in order to validate and test the proposed new approach in the framework of the optimal reference tracking control of a nonlinear position servo system. The experimental results show the superiority of this approach versus the other three competing approaches. (c) 2021 Elsevier Inc. All rights reserved.

关键词： gravitational search algorithm NN training Optimal reference tracking control Q-learning Reinforcement learning Servo systems

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Optimal solution of PM synchronous motor obtained by gravitational search algorithm

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INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS 2022年第2期69卷 149-167页

作者： Cvetkovski, Goga Petkovska, Lidija Ss Cyril & Methodius Univ Fac Elect Engn & Informat Technol Rugjer Boskovic 18POB 574 Skopje 1000 North Macedonia

This paper presents a novel approach to the efficiency improvement of permanent magnet synchronous motor using gravitational search algorithm as an optimization tool. The gravitational search algorithm (GSA) is a recently developed meta-heuristic optimization algorithm, which so far has proven to be quite suitable for solving power engineering optimization problems. The aim of this research work is to implement this novel optimization algorithm for the efficiency improvement of permanent magnet synchronous motor, where the objective function in the optimization process is the efficiency of the investigated motor. Comparative analysis of the initial and a number of optimized solutions of the motor model is performed.

关键词： Permanent magnet synchronous motor efficiency optimization optimal design gravitational search algorithm

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Energy-Efficient Transmission in Wireless Networks Using Resnet with gravitational search algorithm 2

Energy-Efficient Transmission in Wireless Networks Using Res...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rajakumari, S. Hc, Kantharaju Ahmad, Shadab Sharma, Rupa Rani Sudha, S. Ramesh, T. R.M.D. Engineering College Department of Mathematics India Vemana Institute of Technology Department of Ai & Ml Karnataka Bengaluru India Samara University Department of Electrical and Computer Engineering Afar region Ethiopia G.L Bajaj Institute of Technology and Management Department of Applied Science Uttar Pradesh Greater Noida India Sri Ranganathar Institute of Engineering and Technology Department of Ece Tamilnadu Coimbatore India R.M.K Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9798350371055

Although wireless networks are extremely important in modern communication, one of the most significant challenges they face is the quantity of energy that they use. Within this study, a new way of sending data over wireless networks is suggested. The gravitational search algorithm (GSA) and residue networks of the ResNet type are used together in this method. By using wireless networks, the main goal of this method is to find answers to problems that come up with using energy more efficiently. The creation and use of a hybrid ResNet-GSA model for wireless communication is one of the most important parts of our method. Because it follows the rules of gravity, GSA can quickly look through the solution space to find the right parameter values. At the same time, the network works better when the deep learning features of ResNet are used. The results of our research show that there are big improvements in how much energy is used compared to using traditional ways. If you want to cut down on the amount of energy used, the ResNet-GSA strategy that was suggested also works well. The findings demonstrate that optimisation techniques and deep learning methodologies are not only compatible with one another but also perform effectively together to make wireless networks more environmentally friendly. © 2024 IEEE.

关键词： Energy Efficiency gravitational search algorithm Residual Networks Transmission Optimization

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gravitational search algorithm-driven missing links prediction in social networks

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第11期34卷 e6901-e6901页

作者： Singh, Ankita Singh, Nanhay Guru Gobind Singh Indraprastha Univ Univ Sch Informat Commun & Technol IT Dept Delhi India Indira Gandhi Delhi Tech Univ Women Delhi India AIACTR CSE Dept NSUT East Campus Delhi India

The increased usage of online social networks in recent days attracts the attention of researchers. That makes analysis of social networks a significant concern. Link prediction is one of the problem of social networks, where links are predicted between users by analyzing the relationships between the nodes. As the size and usability of the social network grows, the accurate prediction of connections with limited public information is a challenge. This article introduces a novel approach for predicting missing links using a nature-inspired method, the gravitational search algorithm. We selected seven real-world networks and five widely used algorithms and also considered the sparsity of networks for the experimental evaluation of the proposed approach. The proposed GSA-driven algorithm has shown a better or comparable AUC result than the other standard algorithms on these networks.

关键词： dense graph gravitational search algorithm missing link prediction social network analysis sparse

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Multi-objective scheduling of priority-based rescue vehicles to extinguish forest fires using a multi-objective discrete gravitational search algorithm

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INFORMATION SCIENCES 2022年 608卷 578-596页

作者： Tian, Guangdong Fathollahi-Fard, Amir M. Ren, Yaping Li, Zhiwu Jiang, Xingyu Beijing Univ Civil Engn & Architecture Sch Mech Elect & Vehicle Engn Beijing 100044 Peoples R China Shenyang Univ Technol Sch Mech Engn Shenyang 110870 Peoples R China Univ Quebec Dept Elect Engn Ecole Technol Super 1100 Notre Dame St W Montreal PQ Canada Jinan Univ Sch Intelligent Syst Sci & Engn Zhuhai 509070 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa 999078 Macau Peoples R China Xidian Univ Sch Electromech Engn Xian 710071 Peoples R China

The implementation of emergency scheduling of priority-based rescue vehicles for extinguishing forest fires is a complex optimization problem facing with many challenges and difficulties to optimize the operational costs and to improve the efficiency to make robust decisions. The main challenges are to minimize the number of fire engines while minimizing firefighting time and firefighting delay time, simultaneously. The main difficulties to make these decisions are to consider the severity of each fire point with regards to the limited resources of vehicles. Hence, this paper motivates to develop a new multi-objective scheduling model for extinguishing the fire of forests considering rescue priority with the limited rescue resources. To make an efficient and robust solution for the proposed problem, another novelty is to propose a hybrid optimization algorithm which is a modified discrete gravitational search algorithm. To confirm the applicability of the proposed problem, an actual forest fire emergency scheduling in Heilongjiang Province, China, is simulated. The proposed hybrid optimization algorithm is tested to check the feasibility the Pareto solutions and it is compared with a set of well-known and recent algorithms to show its efficiency. Finally, after a comprehensive discussion, the results prove that this work provides an accurate and effective tool for performing the emergency scheduling for extinguishing the forest fire. (C) 2022 Elsevier Inc. All rights reserved.

关键词： Forest fire Emergency management Rescue priority gravitational search algorithm Optimization algorithms

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Investigation of performance of fuzzy logic controllers optimized with the hybrid genetic-gravitational search algorithm for PMSM speed control

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AUTOMATIKA 2022年第2期63卷 313-327页

作者： Unsal, Sinan Aliskan, Ibrahim Republ Turkiye Minist Youth & Sports Bolu Turkey Bulent Ecevit Univ Fac Engn Zonguldak Turkey

Fuzzy logic controllers (FLCs) are widely used to control complex systems with model uncertainty, such as alternating current motors. The design process of the FLC is generally based on the designer's adjustments on the controller until the desired performance is achieved. However, doing the controller design in this way makes the design process quite difficult and time-consuming, so it is often impossible to make a suitable and successful design. In this study, the output membership functions of the FLC are optimized with heuristic algorithms to reach the best speed control performance of the permanent magnet synchronous motor (PMSM). This paper proposes a new hybrid algorithm called H-GA-GSA, created by combining the advantages of the Genetic algorithm (GA) and gravitational search algorithm (GSA) to optimize FLC. The paper presents a convenient adjustment and design method for optimizing FLC with heuristic algorithms considered. To evaluate the effectiveness of H-GA-GSA, the proposed hybrid algorithm has been compared with GA and GSA in terms of convergence rate, PMSM speed control performance and electromagnetic torque variations. Optimization performance and results obtained from simulation studies verify that the proposed hybrid H-GA-GSA outperforms GA and GSA.

关键词： Fuzzy logic optimization hybrid algorithm Genetic algorithm gravitational search algorithm

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