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gravitational search algorithm with both attractive and repulsive forces

SOFT COMPUTING 2019年第3期23卷 783-825页

作者： Zandevakili, Hamed Rashedi, Esmat Mahani, Ali Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran Grad Univ Adv Technol Dept Elect Engn Kerman Iran

The gravitational search algorithm (GSA) is a meta-heuristic optimization algorithm which is inspired by the gravity force. This algorithm uses Newton's gravity and motion laws to calculate the masses interactions and shows high performance in solving optimization problems. The premature convergence is the common drawback of heuristic search algorithms in high-dimensional problems, and GSA is not an exception. In this paper, a new version of GSA is proposed to improve the power of GSA in exploration and exploitation. The proposed algorithm has both attractive and repulsive forces. In this algorithm, the heavy particles attract some particles and repulse some others, in which the forces are inversely proportional to their distances. For better evaluation, the GSA with both attractive and repulsive forces (AR-GSA) is tested using CEC 2013 benchmark functions and the results are compared with some well-known meta-heuristic algorithms. The simulation results show that AR-GSA can improve the convergence rate, the exploration, and the exploitation capabilities of GSA.

关键词： Optimization Heuristic search algorithms gravitational search algorithm Centripetal force

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gravitational search algorithm: a comprehensive analysis of recent variants

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第5期80卷 7581-7608页

作者： Mittal, Himanshu Tripathi, Ashish Pandey, Avinash Chandra Pal, Raju Jaypee Inst Informat Technol Noida India Malaviya Natl Inst Technol Jaipur Rajasthan India

gravitational search algorithm is a nature-inspired algorithm based on the mathematical modelling of the Newton's law of gravity and motion. In a decade, researchers have presented many variants of gravitational search algorithm by modifying its parameters to efficiently solve complex optimization problems. This paper conducts a comparative analysis among ten variants of gravitational search algorithm which modify three parameters, namely Kbest, velocity, and position. Experiments are conducted on two sets of benchmark categories, namely standard functions and CEC2015 functions, including problems belonging to different categories such as unimodal, multimodal, and unconstrained optimization functions. The performance comparison is evaluated and statistically validated in terms of mean fitness value and convergence graph. In experiments, IGSA has achieved better precision with balanced trade-off between exploration and exploitation. Moreover, triple negative breast cancer dataset has been considered to analysis the performance of GSA variants for the nuclei segmentation. The variants performance has been analysed in terms of both qualitative and quantitive with aggregated Jaccard index as performance measure. Experiments affirm that IGSA-based method has outperformed other methods.

关键词： Optimization algorithm Nature-inspired algorithm gravitational search algorithm

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gravitational search algorithm applied to fixed head hydrothermal power system with transmission line security constraints

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ENERGY 2018年 155卷 392-407页

作者： Ozyon, Serdar Yasar, Celal Dumlupmar Univ Elect & Elect Engn Dept TR-43100 Kutahya Turkey

Optimal operation and production planning are important problems for electric power generation systems. Optimal operation of an energy generation system conventionally refers to the minimization of total thermal fuel cost (TTFC). However, the presence of thermal and hydraulic generation units in a power system requires that hydraulic as well as thermal constraints be added to a problem, which is referred to as a short term hydrothermal coordination problem (STHCP). STHCPs assume that the amount of water in the reservoirs of a hydraulic generation unit has no effect on power generation when its net heads are fixed. This study applied a gravitational search algorithm (GSA) for the solution of a STHCP with fixed head. The probable electrical and hydraulic constraints of the problem have been provided by punishment function method. Line losses of the power system were calculated using the Newton-Raphson load flow method. Two different cases, where transfer capacities of transmission lines were neglected and included, were set up, and the problem was solved for both cases. Transfer capacities of transmission lines were included to make the problem more complex and realistic. Solutions for both cases were discussed. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Fixed head Short term hydrothermal coordination problem gravitational search algorithm Newton-Raphson AC load flow Line transfer capacity

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gravitational search algorithm for Calculating Exciton Binding Energy in Monolayer Transition Metal Dichalcogenides

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JOURNAL OF ELECTRONIC MATERIALS 2021年第1期50卷 163-169页

作者： Oloore, Luqman E. Owolabi, Taoreed O. King Fahd Univ Petr & Minerals Phys Dept Laser Res Grp Dhahran 31261 Saudi Arabia Adekunle Ajasin Univ Phys & Elect Dept Akungba Akoko 342111 Ondo State Nigeria

Large excitonic binding energies in monolayers of transition metal dichalcogenides such as molybdenum disulfide (MoS2), molybdenum diselenide (MoSe2), tungsten disulfide (WS2) and tungsten diselenide (WSe2), were calculated using a gravitational search algorithm. The optimized fitness function is based on a two dimensional (2D) effective mass model of excitons, parameterized by first principle calculations, including a suitable treatment of screening. In addition to the ground state, the binding energies of the first few excited states of the exciton were computed, hence the optical transition energies, as a function of principal quantum number n, were obtained for the exciton states. The method was also used to predict the corresponding 2D polarizabilities, and consequently, dielectric constants for the 2D semiconductors. Dependence of the effective dielectric constants on n was also investigated. Our results compare favorably with existing theoretical methods based on density function theory or GW approximation and the Bethe-Salpeter equation. Furthermore, our results are in reasonable agreement with recent experimental measurements.

关键词： Transition metal dichalcogenides gravitational search algorithm binding energy optical transition energy

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gravitational search algorithm based on multiple adaptive constraint strategy

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COMPUTING 2020年第10期102卷 2117-2157页

作者： Liu, Jingsen Xing, Yuhao Ma, Yixiang Li, Yu Henan Univ Inst Intelligent Network Syst Kaifeng 475004 Henan Peoples R China Henan Univ Coll Software Kaifeng 475004 Henan Peoples R China Henan Univ Inst Management Sci & Engn Kaifeng 475004 Henan Peoples R China

In order to improve the convergence speed and optimization accuracy of gravitational search algorithm, the improved gravitational algorithm with dynamically adjusting inertia weight and trend factors of speed and position is proposed. This kind of algorithm with dynamic inertia weight improves the updating way of particle mass. Moreover, the mass change has a nonlinear decreasing trend and improves the algorithm's optimization accuracy and convergence speed. At the same time, the speed trend factor and location adaptive factor is introduced, which can dynamically constrain the moving step of each generation of particles according to the number of iterations of the current population. So the algorithm is multi-adaptive. Through classical test function and the CEC2017 benchmark function, the improved algorithm is compared and tested. The theoretical analysis proves the convergence and time complexity of the improved algorithm. Simulation results show that the improved algorithm has a remarkable improvement in terms of optimal performance, high convergence speed and optimization precision.

关键词： gravitational search algorithm Dynamic inertia weight Velocity trend factor Position adaptive factor Adaptivity

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gravitational search algorithm for economic optimization design of a shell and tube heat exchanger

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APPLIED THERMAL ENGINEERING 2016年第0期107卷 184-193页

作者： Mohanty, Dillip Kumar VIT Univ Sch Mech Engn Vellore Tamil Nadu India

A new design optimization approach using the gravitational search algorithm is developed to obtain optimal configuration of a shell and tube heat exchanger from economic point of view. The objective function considered for the optimization process is the total annual cost including the investment cost and the operating cost. The gravitational search algorithm (GSA) is a heuristic search algorithm based on the law of gravity and mass interactions. Taking into account the importance of shell and tube heat exchangers in industrial applications and the complexity in their geometry, the GSA methodology is adopted to obtain an optimal geometric configuration. The developed algorithm is applied to two case studies and the results are compared with the original design and other optimization methods available in literature such as Genetic algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Biogeography Based Optimization (BBO), Cuckoo search algorithm (CSA) and Firefly algorithm (FFA). The simulation results show that the operating cost can be reduced by 61.5% while the total cost can be reduced by 22.3% as compared to the original design for a shell and tube heat exchanger of heat duty 434 MW. The comparison of the obtained results with other algorithms indicates that the GSA algorithm can be successfully applied for design optimization of a shell and tube heat exchanger from economic point of view. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Shell and tube heat exchanger Overall heat transfer coefficient Optimization gravitational search algorithm Law of gravity

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gravitational search algorithm based optimized deep learning model with diverse set of features for facial expression recognition

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第2期12卷 1631-1646页

作者： Alenazy, Wael Mohammad Alqahtani, Abdullah Saleh King Saud Univ Dept Self Dev Skills Common First Year Deanship Comp Skills Riyadh Saudi Arabia

Facial expression recognition (FER) is an essential part of effective human-computer interaction and serves as a helpful medium for children and patients who have problems with communication. However, most of the previous studies focus on building a FER model based on supervised and unsupervised approaches. This paper is focused on a semi-supervised deep belief network (DBN) approach to predict the facial expressions from the CK+, Oulu CASIA, MMI, and JAFFE datasets. To achieve accurate classification of the facial expressions, a gravitational search algorithm (GSA) is applied to optimize some parameters in the DBN network. The Histogram oriented gradients (HOG) and 2D-Discrete Wavelet Transform (2D-DWT) are used for feature extraction from the lip, cheek, brow, eye, and furrow patches. The unwanted information present in the image is eliminated using a feature selection approach. The feature extraction is done by the Kernel-principal component analysis to obtain higher-order correlations between input variables and detect non-linear components. The HOG features extracted from the lip patch provides the best performance for accurate facial expression classification. Finally, a comparative analysis to compare the proposed model with different machine learning techniques based on the evaluation criteria. The results demonstrate that DBN-GSA based classifier is more accurate than the rest of the classifiers.

关键词： Facial expression recognition Deep belief network Semi-supervised approach gravitational search algorithm Histogram oriented gradients

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gravitational search algorithm for synthesis of selectively thinned concentric ring array antenna with minimum sidelobe level and with fixed and variable first null beamwidth

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INTERNATIONAL JOURNAL OF MICROWAVE AND WIRELESS TECHNOLOGIES 2015年第6期7卷 775-781页

作者： Chatterjee, Anirban Mahanti, Gautam Kumar Pathak, Narendra Nath Natl Inst Technol Goa Dept Elect & Commun Engn Farmagudi Ponda India Natl Inst Technol Durgapur Dept Elect & Commun Engn Durgapur India Dr BC Roy Engn Coll Durgapur Dept Elect & Commun Engn Durgapur India

Thinning a large concentric ring array by an evolutionary algorithm needs to handle a large amount of variables. The computational time to find out the optimum elements set increases with the increase of array size. Moreover, thinning significantly reduces the directivity of the array. In this paper, the authors propose a pattern synthesis method to reduce the peak sidelobe level (peak SLL) while keeping first null beamwidth (FNBW) of the array fixed by thinning the outermost rings of the array based on gravitational search algorithm (GSA). Two different cases have been studied. In the first case only the outermost ring of the array is thinned and in the second case the two outermost rings are thinned. The FNBW of the optimized array is kept equal to or less than that of a fully populated, uniformly excited and 0.5 spaced concentric ring array of same number of elements and rings. The directivity of the optimized array for the above two cases are compared with an array optimized by thinning all the rings, while keeping the design criteria same as the above two cases. The optimized array by thinning the outermost rings gives higher directivity over the optimized array by thinning all the rings. Time required for computing the optimum elements state for the above two cases using GSA are shown lesser compared to the optimized array by thinning all the rings using the same algorithm. The peak SLL and the FNBW of the optimized array for the above two cases are also compared with the optimized array by thinning all the rings.

关键词： Concentric ring array Directivity First null beamwidth gravitational search algorithm Peak SLL Thinning

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gravitational search algorithm for determining the optimal kinetic parameters of propane pre-reforming reaction

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REACTION KINETICS MECHANISMS AND CATALYSIS 2021年第1期132卷 111-122页

作者： Enikeeva, L., V Potemkin, D., I Uskov, S., I Snytnikov, P., V Enikeev, M. R. Gubaydullin, I. M. Novosibirsk State Univ 2 Pirogova St Novosibirsk 630090 Russia Ufa State Petr Technol Univ 1 Kosmonavtov St Ufa 450062 Russia Boreskov Inst Catalysis SB RAS 5 Lavrentiev Ave Novosibirsk 630090 Russia Novosibirsk State Tech Univ 20 Pr K Marksa Novosibirsk 630073 Russia Russian Acad Sci Inst Petrochem & Catalysis 141 Pr Oktyabrya Ufa 450075 Russia

The article describes the gravitational search algorithm and its application to solve the inverse problem of chemical kinetics. It is shown that metaheuristic algorithms have become more and more popular recently. In this regard, it is logical to apply these algorithms to various optimization problems. The optimization problem is formulated on the example of solving the inverse problem of chemical kinetics. The process under study is the pre-reformation of propane into a methane-rich gas on a Ni catalyst, which is an industrially important chemical process. The article briefly describes the algorithm and its pseudocode, and then compares the performance of the gravitational search algorithm with other metaheuristic methods. The algorithm showed adequate results and was applied to solve a specific technological problem. Using this algorithm, the inverse problem of chemical kinetics was solved and the optimal values of the kinetic parameters of the reaction were found. It was proved that the model correctly described the available experimental data. Moreover, the sensitivity analysis of the algorithm parameters was performed.

关键词： Mathematical modeling Inverse problem of chemical kinetics gravitational search algorithm Propane pre-reforming

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gravitational search algorithm: A new feature selection method for QSAR study of anticancer potency of imidazo[4,5-b]pyridine derivatives

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2013年 122卷 1-11页

作者： Bababdani, Behnam Mohseni Mousavi, Mehdi Shahid Bahonar Univ Kerman Dept Chem Fac Sci Kerman Iran

Choosing the most suitable subset of descriptors among a large number of structural parameters is one of the most important and challenging steps in quantitative structure-activity relationship (QSAR) studies. So far, many feature selection algorithms have been applied in these studies, but none of them behave generally. In this study, a binary version of gravitational search algorithm (GSA) as a novel feature selection method is developed and coded for QSAR studies. The GSA is applied as a descriptor selection tool for anticancer potency modeling of a set of imidazo[4,5-b]pyridine derivatives consisting of 65 compounds. The GSA selected descriptors were subjected to Bayesian regularized artificial neural networks to model the anticancer potency. The generated model satisfactorily describes the experimental variation in the biological activity of the data set compounds. The results of external validation R-v(2) = 0.98) and internal cross-validation tests (Q(LOO)(2) = 0.94, R-L4O(2) = 0.93, R-L8O(2) = 0.92) in conjunction with Y-randomization confirm the predictive ability, robustness and effectiveness of the generated model. Also, comparison between GSA and genetic algorithm (GA) indicates that GSA has certain advantages over the GA. (C) 2013 Elsevier B.V. All rights reserved.

关键词： gravitational search algorithm Feature selection QSAR study Imidazo[4,5-b]pyridine Anti-cancer potency Aurora A kinase

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