Levy guided Firefly Algorithm based tuning of decentralised PI Controller of Nonlinear Multivariable system - Coal Gasifier

Kotteeswaran, R.; Sivakumar, L.

doi:10.3182/20140313-3-in-3024.00202

Cited by 4 publications

(2 citation statements)

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“…Yang [13] developed Multi Objective Firefly Algorithm (MOFA) under complex nonlinear functions. Kotteeswaran and Sivakumar [14], [15] has tuned decentralized PI controller for Alstom benchmark challenge using Firefly algorithm and has shown improved results. Decentralized PI controller is designed by pairings between manipulated inputs and controlled outputs using (RGA) analysis to weaken the interactions.…”

Section: Firefly Algorithmmentioning

confidence: 99%

Firefly algorithm based multivariable PID controller design for MIMO process

Ramyashree¹,

Roja²,

Sivagurunathan³

et al. 2018

IJET

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Distillation is the process of separating the components or substances from a liquid mixture by selective boiling and condensation. It is one of the most underestimated fields of chemical engineering and has been around for well over hundred years. This paper deals with the tuning of centralized and decentralized Multivariable PID controller for Wood and Berry distillation column using Firefly algorithm (FA). FA uses controller parameters as decision variables and minimization of IAE as objective function. At the end of the search, optimum solutions for controller parameters are obtained which upon implementation provides challenging results for both top and bottom products. Simulation has been carried out using Matlab/Simulink platform.

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Section: Firefly Algorithmmentioning

confidence: 99%

Firefly algorithm based multivariable PID controller design for MIMO process

Ramyashree¹,

Roja²,

Sivagurunathan³

et al. 2018

IJET

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“…For example, Gandomi et al [34] combined chaos with FA to improve its exploration and the robustness of solution. Kotteeswaran and Sivakumar [35] introduced Lévy -flight into FA to enhance the performance of FA.…”

Section: Introductionmentioning

confidence: 99%

An Improved Firefly Algorithm With Specific Probability and Its Engineering Application

Wang¹,

Chu²

2019

IEEE Access

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Firefly algorithm (FA) belongs to the swarm intelligence algorithm, which is famous for its strong exploration, a small number of parameter settings and effortless operation. However, there are some drawbacks in the searching process for FA, as the poor accuracy of the solution, high-computational time complexity, and doughty oscillation. These phenomenons are attributed to two factors: 1) in classical FA, the firefly, which is gloomier than others can be attracted by any one of them and 2) FA cannot fully utilize the information of objective function and its fitness. In this paper, to overcome these shortcomings, based on specific probability p fit , a new modified firefly algorithm (pFA) is proposed. In this algorithm, for speeding up the convergence, the specific probability p fit determined by the value of fitness of the firefly is used to choose a neighbor among the better fireflies compared with the predefined firefly, which helps the predefined firefly to move toward a better direction. If there is no neighbor, the opposite learning strategy is employed to lead the firefly to move. The performance of pFA is tested on some well-known benchmark functions. The findings of the test show that pFA is outperformed to FA and some other state-of-the-art algorithms. Finally, we apply pFA to solve four engineering applications.

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