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Multi-objective optimal dispatching of combined cooling, heating and power using hybrid gravitational search algorithm and random forest regression: Towards the microgrid orientation

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ENERGY REPORTS 2023年 9卷 1926-1936页

作者： Nazir, Muhammad Shahzad Almasoudi, Fahad M. Abdalla, Ahmad N. Zhu, Chang Alatawi, Khaled Saleem S. Huaiyin Inst Technol Fac Automat Huaian 223003 Peoples R China Univ Tabuk Fac Engn Dept Elect Engn Tabuk 47913 Saudi Arabia Huaiyin Inst Technol Fac Informat & Elect Engn Huaian 223003 Peoples R China

In order to eventually reach the net-zero carbon target, the low carbon transition demands substantial growth in the penetration of renewable generation in energy systems. It is essential to have enough flexible resources to prevent the curtailment of energy systems with high intermittent renewable output to run consistently. Therefore, this study focuses on the multi-objective optimal dispatching model of combined cooling, heating, and power (CCHP) microgrid with power-to-gas (P2G). The struc-ture of the CCHP microgrid and the working principle of P2G are studied and analyzed. First, briefly described the multi-objective optimal dispatch model of the CCHP microgrid with P2G established with the goal of operational and environmental cost. Second, a hybrid gravitational search algorithm and random forest regression (GSA-RFR) are introduced to find out the optimal values. The proposed model verifies that P2G improves the wind abandonment capacity of the CCHP microgrid operation system. Furthermore, it reduces environmental costs and transforms environmental values into economic benefits during system operation. Noting that, the GSA-RFR improves the system economy by 7.13% comparatively.(c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Power-to-gas (P2G) Combined cooling Heating and power (CCHP) Wind power Optimization Microgrid gravitational search algorithm

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Design optimization of the complementary voltage controlled oscillator using a multi-objective gravitational search algorithm

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EVOLVING SYSTEMS 2023年第1期14卷 59-67页

作者： Ebrahimi, Sepehr Mood Hemmati, Mohammad Jafar Yazd Univ Dept Comp Sci Yazd Iran Sirjan Univ Technol Dept Elect Engn Sirjan Iran

Past decade has witnessed the progress of cross-coupled LC voltage controlled oscillator (VCO) in both academic and industrial communities. In this work, a new multi-objective optimization methodology is proposed to introduce an optimal design of a complementary cross-coupled LC-VCO. The design objective is to minimize the phase noise and power consumption of the oscillator at the oscillation frequency of 2.5 GHz and 1.5 V supply voltage. The important characteristics of the complementary LC-VCO which is one of the more popular cross-coupled configurations are described in sufficient details. In addition, the confirmation theorems of the proposed method are proven to show that the new version of Multi-Objective gravitational search algorithm (MOGSA) can control the exploitation and exploration abilities of the algorithm. Hence the improved version of MOGSA has better performance against other popular multi-objective methods. The simulation results obtained from the circuit optimization are summarized to confirm the robustness of the proposed method.

关键词： Complementary LC-VCO Phase noise Power consumption Multi-objective optimization gravitational search algorithm

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Chaos embedded opposition based learning for gravitational search algorithm

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APPLIED INTELLIGENCE 2023年第5期53卷 5567-5586页

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

Due to its robust search mechanism, gravitational search algorithm (GSA) has achieved a lot of popularity in different research communities. However, stagnation reduces its searchability towards global optima for rigid and complex multi-modal problems. This paper proposes a GSA variant that incorporates chaos-embedded opposition-based learning into the basic GSA for the stagnation-free search. Additionally, a sine-cosine based chaotic gravitational constant is introduced to balance the trade-off between exploration and exploitation capabilities more effectively. The proposed variant is tested over 23 classical benchmark problems, 15 test problems of CEC 2015 test suite, and 15 test problems of CEC 2014 test suite. Different graphical, as well as empirical analyses, reveal the superiority of the proposed algorithm over conventional meta-heuristics and most recent GSA variants.

关键词： gravitational search algorithm Chaotic map Opposition based learning Meta-heuristics Stochastic optimization

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Levy flight and chaos theory based gravitational search algorithm for multilayer perceptron training

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EVOLVING SYSTEMS 2023年第3期14卷 365-392页

作者： Rather, Sajad Ahmad Bala, P. Shanthi Pondicherry Univ Sch Engn & Technol Dept Comp Sci Pondicherry 605014 India

In this paper, a modified version of the gravitational search algorithm (GSA) based on levy flight and chaos theory namely LCGSA has been used to train Multilayer Perceptron (MLP) neural network for feature classification and function approximation. In LCGSA, the diversification of the search space is carried out by levy flight while chaotic maps have been utilized for the intensification of the candidate solutions towards the global optimum. Besides, the sigmoid function acts as an objective function for LCGSA based MLP pair in order to minimize the neural bias and error. Moreover, the matrix encoding scheme is used for representing the candidate solutions. To verify the effectiveness of LCGSA, it has been applied to three well-known classification datasets namely XOR, Balloon, and Heart. Besides, two function approximation benchmarks including Sigmoid and Cosine functions are also considered for the performance evaluation. Furthermore, the quantitative and qualitative analysis of the simulation results has been carried out. The quantitative performance metrics include statistical measures, run time, mean square error (MSE), and average test error (ATE). Also, the simulation analysis is benchmarked by utilizing various qualitative measures such as convergence rate, box plot analysis, and approximation curves. The signed Wilcoxon rank-sum test is employed to statistically verify the experimental outcomes. In addition, ten LCGSA versions are compared with various hybrid and recent heuristic optimization techniques. The simulation results indicate that LCGSA provides better performance than standard GSA and most of the competing algorithms.

关键词： Multilayer perceptron Neural network gravitational search algorithm Chaos theory Levy flight Classification Function approximation Feedforward neural network LCGSA

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A grey wolf optimizer-based chaotic gravitational search algorithm for global optimization

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JOURNAL OF SUPERCOMPUTING 2023年第3期79卷 2691-2739页

作者： Yu, Xianrui Zhao, Qiuhong Lin, Qi Wang, Tongyu Beihang Univ Sch Econ & Management Beijing 100191 Peoples R China Beijing Key Lab Emergency Support Simulat Technol Beijing 100191 Peoples R China Beijing Int Sci & Technol Cooperat Base City Safe Beijing 100191 Peoples R China Univ Int Business & Econ Sch Informat Technol & Management Beijing 100029 Peoples R China Ocean Univ China Sch Econ Qingdao 266100 Peoples R China

gravitational search algorithm (GSA) is widely accepted as one of the effective optimization algorithms providing promising results. However, it suffers with local optima and premature convergence. To deal with those problems, chaotic gravitational constants for the gravitational search algorithm (CGSA) are proposed to enhance the efficiency of exploration via embedding chaotic maps, but the enhancement of exploration throughout iterations could somehow degrade the exploitation ability of GSA. Accordingly, this paper introduces a location update strategy inspired by the hunting mechanism of grey wolf optimizer (GWO) for improving the exploitation process of CGSA. In addition, a negative entropy function is designed to effectively control the implementation of the proposed strategy. In order to show the superior performance of the grey wolf chaotic gravitational search algorithm (GWCGSA), we carry out a comparative study of 30 benchmark functions (CEC 2014) with multiple GSAs and state-of-the-art algorithms. The experimental results show that the proposed strategy achieves better performance in most functions (greater than 20/30). Also, the experiments on seven engineering optimization problems indicate that the practicality of the proposed algorithm can be ensured.

关键词： gravitational search algorithm Exploration and exploitation Grey wolf optimizer Chaotic map Numerical optimization

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Development of hybrid optimization algorithm for structures furnished with seismic damper devices using the particle swarm optimization method and gravitational search algorithm

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Earthquake Engineering and Engineering Vibration 2022年第2期21卷 455-474页

作者： Najad Ayyash Farzad Hejazi Civil Engineering University Putra MalaysiaSeri Kembangan 43300Malaysia

Previous studies about optimizing earthquake structural energy dissipation systems indicated that most existing techniques employ merely one or a few parameters as design variables in the optimization process,and thereby are only applicable only to simple,single,or multiple degree-of-freedom *** current approaches to optimization procedures take a specific damper with its properties and observe the effect of applying time history data to the building;however,there are many different dampers and isolators that can be ***,there is a lack of studies regarding the optimum location for various viscous and wall *** main aim of this study is hybridization of the particle swarm optimization(PSO) and gravitational search algorithm(GSA) to optimize the performance of earthquake energy dissipation systems(i.e.,damper devices) simultaneously with optimizing the characteristics of the *** types of structural dampers device are considered in this study:(ⅰ) variable stiffness bracing(VSB) system,(ⅱ) rubber wall damper(RWD),(ⅲ) nonlinear conical spring bracing(NCSB) device,(iv) and multi-action stiffener(MAS) *** many parameters may affect the design of seismic resistant structures,this study proposes a hybrid of PSO and GSA to develop a hybrid,multi-objective optimization method to resolve the aforementioned *** characteristics of the above-mentioned damper devices as well as the section size for structural beams and columns are considered as variables for development of the PSO-GSA optimization algorithm to minimize structural seismic response in terms of nodal displacement(in three directions) as well as plastic hinge formation in structural members simultaneously with the weight of the *** that,the optimization algorithm is implemented to identify the best position of the damper device in the structural frame to have the maximum effect and minimize the seismic structure *** examine the performance

关键词： hybrid optimization algorithm structures earthquake seismic damper devices particle swarm optimization method gravitational search algorithm

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Application of empirical wavelet transform, particle swarm optimization, gravitational search algorithm and long short-term memory neural network to copper price forecasting

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PORTUGUESE ECONOMIC JOURNAL 2025年第1期24卷 151-169页

作者： Kim, Yong-Hyong Ham, Song-Jun Ri, Chong-Sim Kim, Won-Hyok Ri, Wi-Song Kim Il Sung Univ Fac Finance Dept Accounting Pyongyang North Korea Artificial Intelligence Inst Korea Comp Ctr Pyongyang North Korea

Copper is one of the main non-ferrous metals which are closely associated with important industries, such as equipment manufacturing, electrical wiring, and construction;and thus, copper price is becoming an important impact factor on the performance of related economies. This paper aims to develop a hybrid method for forecasting the copper price by combining empirical wavelet transform (EWT), particle swarm optimization (PSO), gravitational search algorithm (GSA) and long short term memory neural network (LSTM), which is denoted as EWT-PSO-GSA-LSTM in this study. The forecasting performance of the proposed hybrid method was verified by time series data of the copper closing price in the London Metal Exchange (LME). The results of this study have shown that the proposed EWT-PSO-GSA-LSTM method outperformed other forecasting methods in terms of several performance criteria, such as the root mean square error (RMSE), the mean absolute error (MAE), the mean absolute percentage error (MAPE), and the Diebold-Mariano (DM) test. For the daily copper price time series, the EWT-PSO-GSA-LSTM method had the smallest RMSE, MAE and MAPE values (0.007, 0.013 and 1.358, respectively) compared to LSTM, EWT-LSTM, PSO-LSTM and EWT-PSO-LSTM methods. Furthermore, all the DM values of our proposed method were below -2.61 and the p\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p$$\end{document} values were smaller than 1%, indicating that the proposed method performed the best in forecasting the copper price at the 99% confidence level. Given the present results, it can be concluded that it is possible to improve the copper price forecasting method by combining the EWT, PSO, GSA and LSTM models.

关键词： Copper price forecasting Long-short term memory neural network Particle swarm optimization gravitational search algorithm Empirical wavelet transform C22 C45 C53 C61 L61

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GSA-UBS: A Novel Medical Hyperspectral Band Selection Based on gravitational search algorithm 13th

GSA-UBS: A Novel Medical Hyperspectral Band Selection Based ...

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International Conference on Brain Inspired Cognitive Systems (BICS)

作者： Zhang, Chenglong Yang, Xiaoli Zhang, Aizhu Yu, Dexin Liu, Nian Ma, Xiaopeng Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China Shandong Univ Radiol Dept Qilu Hosp Jinan 250000 Peoples R China Zhejiang Univ Affiliated Hosp 1 PET Ctr Dept Nucl MedSch Med Hangzhou 310003 Peoples R China

ISBN: (纸本)9789819714162;9789819714179

Medical hyperspectral images (MHSIs) provide the possibility of non-invasive disease diagnosis. However, due to the sparsity of MHSIs data in high-dimensional space, the "curse of dimensionality" arises, which reduces the efficiency and accuracy of data processing. Therefore, spectral dimensionality reduction has become a necessary step for MHSIs data analysis and application. To preserve the inherent properties of spectral bands, an unsupervised band selection algorithm based on gravitational search algorithm, called GSA-UBS, is proposed in this paper to search for the best subset of bands. Considering the amount of information and redundancy of the candidate bands, we define an evaluation criterion consisting of a band distance matrix and an information entropy vector. Additionally, we design a simple discrete search strategy that enables GSA to directly obtain the original serial number of the selected bands instead of weighting the bands 0-1. Extensive experiments are conducted on two publicly available in vivo brain cancer MHSIs datasets, and the results demonstrate that GSA-UBS outperforms several state-of-the-art methods.

关键词： gravitational search algorithm Medical hyperspectral images band selection

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Fuzzy clustering using gravitational search algorithm for brain image segmentation

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第20期81卷 29633-29652页

作者： Hooda, Heena Verma, Om Prakash Delhi Technol Univ Dept IT Delhi India Delhi Technol Univ Dept ECE Delhi India

Clustering is a key activity in numerous data mining applications such as information retrieval, text mining, image segmentation. Clustering also plays a major role in medical image processing. Manual image segmentation is very tedious and time consuming task and the results of manual segmentation are subjected to errors due to huge and varying data. Therefore, automated segmentation systems are gaining enormous importance nowadays. This paper presents an automated system for segmentation of brain tissues namely white matter, gray matter and cerebrospinal fluid from brain MRI images. In this work, we propose a novel clustering approach, Fuzzy-gravitational search algorithm(GSA) for MRI brain image segmentation. The proposed approach is based on GSA, and uses fuzzy inference rules for controlling the parameter alpha as search progresses. The results of the system are compared with GSA and recent work on brain image segmentation algorithms for both real and simulated database on the basis of Dice Coefficient values. The performance of the Fuzzy-GSA algorithm is also evaluated against four benchmark datasets from the UC Irvine repository. The results illustrate that the Fuzzy-GSA approach attains the highest quality clustering over the selected datasets when compared with several other clustering algorithms.

关键词： Magnetic resonance imaging Brain image segmentation gravitational search algorithm Fuzzy clustering Fuzzy inference rules

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A cluster based charging schedule for wireless rechargeable sensor networks using gravitational search algorithm

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WIRELESS NETWORKS 2022年第7期28卷 3323-3336页

作者： Rahaman, Sk Md Abidar Azharuddin, Md Aliah Univ Dept Comp Sci & Engn Kolkata 700156 India

Wireless sensor networks have a short network lifetime due to limited battery life. External power supplies are utilised to extend the life of sensor nodes. The previous works of charging scheduling lack charging efficiency, resulting in early node energy exhaustion. There are certain limitations in designing the scheduling path when sensor nodes consume diversified energy. Previous charging schedule approaches or charging path designs aimed to reduce the mobile charger's travel distance as well as the charging time delay. In this paper, we show how to build a charging path that reduces not only the mobile charger's maximum working time, but also the charging time delay or charging latency, as well as the mobile charger's or Mobile Charging Vehicle's (MCV) travel distance . Here first we cluster the sensor nodes based on their location, then we derive a path using gravitational search algorithm inside the clusters. We stimulate the proposed work and compare the working time of the MCV with some existing algorithms to show the efficiency.

关键词： Wireless sensor networks Charging scheduling Clustering Lifetime Mobile charging vehicle gravitational search algorithm

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