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Analysis of Vector Evaluated Particle Swarm Optimization Guided by Non-Dominated Solutions : Inertia Weight, Cognitive, and Social Constants
Published 2014“…Recently, an improved Vector Evaluated Particle Swarm Optimisation (VEPSO) algorithm is introduced by redefining the swarm's leader as non-dominated solutions. …”
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Conference or Workshop Item -
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PID TUNING OF DC MOTOR USING SWARM ITELLIGENCE ALGORITHM
Published 2012“…In order to evaluate the control performance, the three control parameters will be used to tune DC Motor simulated in MATLAB. …”
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Final Year Project -
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Parameters optimization of surface grinding process with particles swarm optimization, gravitational search, and sine cosine algorithms: a comparative analysis
Published 2018“…In this paper, three optimization algorithms which are particle swarm optimization (PSO), gravitational search, and Sine Cosine algorithms are employed to optimize the grinding process parameters that may either reduce the cost, increase the productivity or obtain the finest surface finish and resulting a higher grinding process performance. …”
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An Improved Hybrid of Particle Swarm Optimization and the Gravitational Search Algorithm to Produce a Kinetic Parameter Estimation of Aspartate Biochemical Pathways
Published 2017“…This paper describes an improved hybrid of particle swarm optimization and the gravitational search algorithm (IPSOGSA) to improve the efficiency of a global optimum (the best set of kinetic parameter values) search. …”
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Performance Comparison of Particle Swarm Optimization and Gravitational Search Algorithm to the Designed of Controller for Nonlinear System
Published 2014“…Since the performance of the designed controller depends on the value of control parameters, gravitational search algorithm (GSA) and particle swarm optimization(PSO) techniques are used to optimise these parameters in order to achieve a predefined system performance. …”
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A comparative evaluation of PID-based optimisation controller algorithms for DC motor
Published 2023“…Controllers; DC motors; Electric control equipment; Particle swarm optimization (PSO); Proportional control systems; Three term control systems; Two term control systems; Backtracking search algorithms; Comparative analysis; Comparative evaluations; Controller algorithm; Industrial activities; Optimum parameters; Particle swarm optimisation; Proportional integral derivative controllers; Electric machine control…”
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Robustness Analysis Of An Optimized Controller Via Particle Swarm Algorithm
Published 2017“…This paper deals with an evaluation on the effectiveness of the robust controller in terms of its robustness towards the changes in the electro-hydraulic actuator (EHA) system parameters. …”
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Evaluation Algorithm - Based on PID Controller Design for the Unstable Systems
Published 2015“…In this project, the evaluating PSO algorithm based on PID controller design for unstable system was proposed. …”
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A Hybrid of Particle Swarm Optimization and Harmony Search to Estimate Kinetic Parameters in Arabidopsis thaliana
Published 2020“…The proposed algorithm outperformed the other two methods, namely Simulated Annealing and the downhill simplex method, and proved that PSOHS is a more suitable algorithm for estimating kinetic parameter values.…”
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An Adaptive Switching Cooperative Source Searching And Tracing Algorithms For Underwater Acoustic Source Localization
Published 2019“…By considering the ASVs as swarm robotics testing platforms, each algorithm is evaluated and benchmarked against several existing algorithms through simulation studies. …”
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Thesis -
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Optimized PV-Battery Systems using Backtracking Search Algorithm for Sustainable Energy Solutions
Published 2024“…The research strategically applies advanced algorithms to elevate PV-battery system performance and compares outcomes with Particle Swarm Optimization (PSO) and other studies, offering a comprehensive benchmark for evaluation…”
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Comparison of mabsa, PSO and GWO of PI-PD controller for dc motor
Published 2024“…Three distinct mathematical models pertaining to the DC motor system are derived from a thorough analysis of previous research. The swarm intelligence group selected Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Modified Adaptive Bats Sonar Algorithms (MABSA) to optimize the parameters of the PI-PD controller. …”
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Thesis -
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Sliding Mode Controller Design With Optimized PID Sliding Surface Using Particle Swarm Algorithm
Published 2017“…In the performance assessment on the designed PID sliding surface, the controller parameter is first obtained through conventional tuning method known as Ziegler-Nichols (ZN), which is then compared with the particle swarm optimization (PSO) computational tuning algorithm. …”
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