Journal ArticleDOI
A particle swarm optimization approach for optimum design of PID controller in AVR system
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TLDR
The proposed PSO method was indeed more efficient and robust in improving the step response of an AVR system and had superior features, including easy implementation, stable convergence characteristic, and good computational efficiency.Abstract:
In this paper, a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters of an AVR system using the particle swarm optimization (PSO) algorithm is presented. This paper demonstrated in detail how to employ the PSO method to search efficiently the optimal PID controller parameters of an AVR system. The proposed approach had superior features, including easy implementation, stable convergence characteristic, and good computational efficiency. Fast tuning of optimum PID controller parameters yields high-quality solution. In order to assist estimating the performance of the proposed PSO-PID controller, a new time-domain performance criterion function was also defined. Compared with the genetic algorithm (GA), the proposed method was indeed more efficient and robust in improving the step response of an AVR system.read more
Citations
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Methods for improving reliability of evolutionary computation algorithms and accelerating problem solving
TL;DR: This dissertation deals with improving the reliability of evolutionary computation algorithms and accelerating problem-solving in optimization problems by proposing algorithms and the means to reduce the time required by the objective function evaluation.
Journal ArticleDOI
Optimal PID plus second-order derivative controller design for AVR system using a modified Runge Kutta optimizer and Bode’s ideal reference model
Proceedings ArticleDOI
Proactive digital forensics in the cloud using virtual machines
D. Ras,H.S. Venter +1 more
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Journal ArticleDOI
A novel fractional order PID plus derivative (PIλDµDµ2) controller for AVR system using equilibrium optimizer
TL;DR: The proposed FOPIDD controller has shown superior performance on AVR systems because of having seven optimization parameters and being fractional order based.
Journal ArticleDOI
A comparison between quantum inspired bacterial foraging algorithm and ga-like algorithm for global optimization
Sharina Huang,Guoliang Zhao +1 more
TL;DR: A novel optimization algorithm is proposed, named quantum inspired bacterial foraging algorithm (QBFA), which applies several quantum computing principles, and a new mechanism is proposed to encode and observe the population.
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Proceedings ArticleDOI
A modified particle swarm optimizer
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.
Proceedings ArticleDOI
Empirical study of particle swarm optimization
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: The experimental results show that the PSO is a promising optimization method and a new approach is suggested to improve PSO's performance near the optima, such as using an adaptive inertia weight.
Book
Power System Analysis
TL;DR: This is the first text in this area to fully integrate MATLAB and SIMULINK throughout and provides students with an author-developed POWER TOOLBOX DISK organized to perform analyses and explore power system design issues with ease.
Book
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
TL;DR: In-depth and updated, Evolutionary Computation shows you how to use simulated evolution to achieve machine intelligence and carefully reviews the "no free lunch theorem" and discusses new theoretical findings that challenge some of the mathematical foundations of simulated evolution.