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A cluster based routing for maximizing the lifetime of underwater wireless sensor network using gravitational search algorithm

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RESULTS IN ENGINEERING 2025年 25卷

作者： Shyamsundar, R. Harshavarthan, M. Thangavelu, Shankar Vellore Inst Technol Sch Elect Engn Vellore India

Underwater Wireless Sensor Networks (UWSN) is a type of Wireless Sensor Network that branches from Ad Hoc networks. This type of network is designed to investigate and explore underwater environments and oceans. The UWSN network interconnects multiple sensory devices, forming a temporary communication network that forwards the data. Due to the attenuation issue, UWSN uses an acoustic modem for transmission and reception. The energy depletion of UWSN is comparatively higher than Terrestrial Wireless Sensor Networks (TWSN) as it uses an acoustic modem, and the channel mitigations in UWSN are of a higher order than TWSN. Due to this, extensive research is being undertaken worldwide to increase the lifetime of a UWSN network. This paper focuses on the clustering-based routing of the devices using the gravitational search algorithm. The proposed algorithm works in four phases: exploration phase, clustering phase, routing phase, and transmission phase. Also, the proposed algorithm works well for higher and lower node densities. The proposed GSA shows 33.06 %, 11.77 %, and 57.2 % improvements compared to EECMR, EERBLC, and conventional LEACH, respectively. The proposed GSA has been compared with competitive meta-heuristic algorithms, including Particle Swarm Optimization, Whale Optimization algorithm, and Moth Flame Optimizer.

关键词： Underwater wireless sensor networks Clustering Network topology gravitational search algorithm Optimization load balancing

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GSA-LA: gravitational search algorithm based on learning automata

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2021年第1期33卷 109-125页

作者： Alirezanejad, Mehdi Enayatifar, Rasul Motameni, Homayun Nematzadeh, Hossein Islamic Azad Univ Sari Branch Dept Comp Engn Sari Iran Islamic Azad Univ Firoozkooh Branch Dept Comp Engn Firoozkooh Iran

Regardless of the performance of gravitational search algorithm (GSA), it is nearly incapable of avoiding local optima in high-dimension problems. To improve the accuracy of GSA, it is necessary to fine tune its parameters. This study introduces a gravitational search algorithm based on learning automata (GSA-LA) for optimisation of continuous problems. gravitational constant G(t) is a significant parameter that is used to adjust the accuracy of the search. In this work, learning capability is utilised to select G(t) based on spontaneous reactions. To measure the performance of the introduced algorithm, numerical analysis is conducted on several well-designed test functions, and the results are compared with the original GSA and other evolutionary-based algorithms. Simulation results demonstrate that the learning automata-based gravitational search algorithm is more efficient in finding optimum solutions and outperforms the existing algorithms.

关键词： Learning automata gravitational search algorithm unconstrained optimisation

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An improved multi-leader comprehensive learning particle swarm optimisation based on gravitational search algorithm

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CONNECTION SCIENCE 2021年第4期33卷 803-834页

作者： Amponsah, Alfred Adutwum Han, Fei Osei-Kwakye, Jeremiah Bonah, Ernest Ling, Qing-Hua Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Jiangsu Peoples R China Jiangsu Key Lab Secur Technol Ind Cyberspace Zhenjiang 212013 Jiangsu Peoples R China Jiangsu Univ Sci & Technol Sch Comp Sci Zhenjiang Jiangsu Peoples R China Garden City Univ Coll Fac Appl Sci Kenyase Kumasi Ghana

Multi-leader comprehensive learning particle swarm optimiser possesses strong exploitation ability, by randomly selecting and assigning best-ranked particles as leaders during optimisation. However, it lacks the ability to preserve diversity by mainly focusing on exploitation, and adopting random selection to choose leaders also hinders its performance. To overcome these deficiencies, an improved multi-leader comprehensive learning particle swarm optimiser is proposed based on Karush-Kuhn-Tucker proximity measure and gravitational search algorithm. Karush-Kuhn-Tucker proximity measure is employed to determine the best-ranked particles' contribution to the swarm's convergence to influence their selection as guides for other particles. gravitational search algorithm is introduced to preserve the algorithm's ability to maintain diversity. To curb premature convergence and particles getting trapped in a local optimum, an adaptive reset velocity strategy is incorporated to activate stagnated particles. Some benchmark test functions are employed to compare the proposed algorithm with seven other peer algorithms. The results verify that our proposed algorithm possesses a better capability to elude local optima with faster convergence than other algorithms. Furthermore, to prove the efficacy of the application of our proposed algorithm in real-life, the algorithms are used to train a Feedforward neural network for epilepsy detection, of which our proposed algorithm outperforms the others.

关键词： Adaptive reset velocity strategy comprehensive learning gravitational search algorithm particle swarm optimisation multi-leader strategy

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A nonlinear model predictive controller based on the gravitational search algorithm

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OPTIMAL CONTROL APPLICATIONS & METHODS 2021年第6期42卷 1734-1761页

作者： Nobahari, Hadi Alizad, Meysam Nasrollahi, Saeed Sharif Univ Technol Dept Aerosp Engn Tehran Iran

A heuristic nonlinear model predictive controller is proposed, based on the gravitational search algorithm. The proposed method models a constrained nonlinear model predictive control problem in the form of a dynamic optimization and uses a set of virtual particles, moving within the search space, to find the best control sequence in an online manner. Particles affect the movement of each other through the gravitational forces. The optimality of the points, experienced by the particles, is evaluated by a cost function. This function reduces the tracking error, control effort, and control chattering. The better control sequence a particle finds, the more mass is assigned to that particle. Therefore, it will apply more gravitational force to the other particles and will absorb them more strongly. Stability of the new controller is investigated. Finally, performance of the controller is evaluated in two nonlinear benchmark problems as well as an experimental attitude control of a quadrotor as a nonlinear multi-input multi-output system with input constraints. Results confirm successful performance of the proposed controller.

关键词： gravitational search algorithm multi-input multi-output control nonlinear model predictive control quadrotor real-time optimization

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Application of constriction coefficient-based particle swarm optimisation and gravitational search algorithm for solving practical engineering design problems

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2021年第4期17卷 246-259页

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

The constriction coefficient-based particle swarm optimisation and gravitational search algorithm (CPSOGSA) is a heuristic optimisation technique. It uses the diversification capability of the GSA and the convergence power of CPSO. In this paper, the hybrid CPSOGSA is applied to three constrained engineering design problems, namely welded beam design (WBD), compression spring design (CSD), and pressure vessel design (PVD), to find the optimal value of the engineering objective function and design variables. The investigation of experimental outcomes has been performed through various performance metrics like statistical measures, run time analysis, convergence rate, and box plots. Moreover, statistical verification of experimental results is carried out using a signed Wilcoxon rank-sum test. Furthermore, seven heuristic algorithms were employed for comparative analysis. The simulation results clearly indicate that CPSOGSA provides better outcomes for all the three engineering benchmarks than classical PSO, standard GSA, and most of the competing algorithms.

关键词： gravitational search algorithm GSA constriction coefficient hybridisation swarm intelligence engineering optimisation particle swarm optimisation PSO

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Impact Behaviour Modelling of Magnetorheological Elastomer Using a Non-Parametric Polynomial Model Optimized with gravitational search algorithm

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STROJNISKI VESTNIK-JOURNAL OF MECHANICAL ENGINEERING 2023年第11-12期69卷 471-482页

作者： Zubir, Amrina Rasyada Hudha, Khisbullah Kadir, Zulkiffli Abd. Amer, Noor Hafizah Natl Def Univ Malaysia Dept Mech Engn Fac Engn Kuala Lumpur Malaysia

This paper presents an approach to model the impact behaviour of a dual-acting magnetorheological elastomer (MRE) damper using 4th order polynomial functions optimized with a gravitational search algorithm. MRE is a type of smart material that can change its mechanical properties in response to an injected current, making it well-suited for a wide range of applications such as vibration absorption, noise cancellation, and shock mitigation. The proposed model uses a combination of polynomial functions designed to predict the nonlinearity of MRE during compression and extension stages. The model is tuned and validated using experimental data from impact tests conducted on the MRE damper under various currents. The results indicate that the developed model can accurately track the impact behaviour of MRE with minimum error. Additionally, an interpolation model is proposed to estimate the appropriate forces for median currents. The interpolation model predicts the force between the upper and lower currents, demonstrating the model's ability to predict MRE behaviour accurately. The main contribution of this study is proposing a non-parametric model of MRE that is able to identify the hysteretic behaviour of the MRE based on specific current applied. In addition, an interpolation model is introduced in this study to cover not only the input current starting from 0 A to 2 A but also the intermediate current such as 0.3 A, 0.7 A, 1.3 A and 1.7 A.

关键词： magnetorheological damper polynomial model gravitational search algorithm force-displacement characteristic interpolated model

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An IoT-enabled Healthcare Framework For Wireless Body Region Networks Using The gravitational search algorithm

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淡江理工学刊 2024年第3期27卷 2169-2178页

作者： Peng Zhang Man Zhao

The smart city idea has gained much popularity in recent years because of its potential to enhance urban people's quality of life. The idea encompasses a wide range of fields, including smart community, smart transportation, and smart healthcare. For intelligent decision-making, most smart city services, particularly those in the smart healthcare field, demand analyzing, processing, and real-time sharing of big healthcare data. The current state of the healthcare industry requires the supply of healthcare products and services, increasing its viability for everyone involved. This paper presents a novel healthcare framework based on clustering sensor nodes, where the physical body is separated into three areas: the bottom, top, and intermediate body regions. The regions are clustered using an enhanced LEACH algorithm. Cluster heads are selected using the gravitational search algorithm (GSA). The MATLAB environment is used to evaluate the proposed framework by comparing it to other approaches. Our framework outperforms previous methods in terms of energy consumption and throughput by 20% and 30%, respectively.

关键词： Internet of things Healthcare gravitational search algorithm Clustering Optimization

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An Intelligent Multi-robot Path Planning in a Dynamic Environment Using Improved gravitational search algorithm

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International Journal of Automation and computing 2021年第6期18卷 1032-1044页

作者： P.K.Das H.S.Behera P.K.Jena B.K.Panigrahi Department of Computer Sceime and Engineering and Information TechnologyVeer Surendra Sai University of TechnologyBurlaOdishaIndia Departmet of Mechanical Engineering Veer Surendra Sai University of TechnologyBurlaOdishaIndia Departmet of Electrical Engineering Indian Institute of TechnologyDelhiIndia

This paper proposes a new methodology to optimize trajectory of the path for multi-robots using improved gravitational search algorithm(IGSA) in clutter environment. Classical GSA has been improved in this paper based on the communication and memory characteristics of particle swarm optimization(PSO). IGSA technique is incorporated into the multi-robot system in a dynamic framework, which will provide robust performance, self-deterministic cooperation, and coping with an inhospitable environment. The robots in the team make independent decisions, coordinate, and cooperate with each other to accomplish a common goal using the developed IGSA. A path planning scheme has been developed using IGSA to optimally obtain the succeeding positions of the robots from the existing position in the proposed environment. Finally, the analytical and experimental results of the multi-robot path planning were compared with those obtained by IGSA, GSA and differential evolution(DE) in a similar environment. The simulation and the Khepera environment result show outperforms of IGSA as compared to GSA and DE with respect to the average total trajectory path deviation, average uncovered trajectory target distance and energy optimization in terms of rotation.

关键词： gravitational search algorithm multi-robot path planning average total trajectory path deviation average uncovered trajectory target distance average path length

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Optimizing the parameters of hybrid active power filters through a comprehensive and dynamic multi-swarm gravitational search algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartC期123卷

作者： Chauhan, Dikshit Yadav, Anupam Dr BR Ambedkar Natl Inst Technol Jalandhar Dept Math Jalandhar 144011 Punjab India

This paper introduces a dynamic comprehensive multi-swarm gravitational search algorithm with non-uniform mutation (cdGSA-2m) for optimizing hybrid active power filter (HAPF) parameters. The cdGSA-2m algorithm divides the population into two heterogeneous sub-populations with different learning mechanisms. The first sub-population focuses on exploitation with comprehensive learning, while the second sub-population emphasizes exploration with a dynamic multi-swarm strategy (DMS) with non-uniform mutation. Additionally, a non-linear adaptive parameter balances the search capabilities of the DMS sub-population. These mechanisms effectively control the exploration, exploitation, and diversity of the population. To analyse the effectiveness of implemented mechanisms in the cdGSA-2m, diversity and exploration-exploitation are discussed at length with a few experiments. An independent structural study of cdGSA-2m is also carried out to understand the effects of comprehensive learning, dynamic multi-swarm strategy, and non-uniform mutation mechanisms in the algorithm. The proposed cdGSA-2m is tested on IEEE-CEC 2017 benchmark problems, and the results of these problems are compared with twenty-two state-of-the-art algorithms in terms of mean error values and standard deviation of the fitness values. Based on the results, the algorithms are ranked against each other, along with statistical validation of the results. The proposed cdGSA-2 m also solves two case studies of the HAPF model and compares the results with existing state-of-the-art algorithms for the HAPF model. Experimental results demonstrate that the cdGSA-2m algorithm has better accuracy, convergence rate, search capability, and stability. The overall ranking of the algorithms suggests that cdGSA-2 m has outstanding potential to deal with difficult optimization problems.

关键词： Evolutionary computation Soft computing gravitational search algorithm Dynamic multi-swarm Comprehensive learning Mutation operator

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An antigravity-based fuzzy gravitational search algorithm for economic dispatch problems

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APPLIED SOFT COMPUTING 2023年 145卷

作者： Yu, Xianrui Zhao, Qiuhong Wang, Tongyu Li, Yuanrui 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 Safet Beijing 100191 Peoples R China Ocean Univ China Sch Econ Qingdao 266100 Peoples R China

This paper presents an antigravity-based fuzzy gravitational search algorithm (AFGSA) to solve the economic dispatch problem (EDP) with various constraints. Based on an antigravity mechanism combined with an adaptive fuzzy parameter controller AFGSA is designed. By doing so, it ensures a proper balance between exploration and exploitation of GSA. Furthermore, the simulated annealing (SA) process is introduced to control the application of anti-gravitation forces and guarantee the convergence rate. A fuzzy optimizing system is applied to adaptively control the parameters in GSA. The success of the suggested algorithm is firstly shown in eight various test EDPs including static and dynamic economic load problems as well as hydrothermal scheduling problems by varying the complex constraints. Then, the efficiency of the proposed optimizer is validated through ten CEC2020 test functions. The performance of AFGSA is verified by comparison with numerous state-of-the-art techniques. The experimental results show that AFGSA provides a more competing and robust solution to the EDPs than the compared algorithms. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： Economic dispatch Optimal economic load gravitational search algorithm Antigravity Fuzzy system

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