An approach of firefly algorithm with modified brightness for PID and I-PD controllers of SISO systems

Meena, S.; Chitra, K.

doi:10.1007/s12652-018-0747-x

Cited by 10 publications

(4 citation statements)

References 22 publications

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“…Many results were taken by their study. Meena et al [ 51 ] had designed two algorithms based on modified firefly in the context of updation equation with higher accuracy values.…”

Section: Related Workmentioning

confidence: 99%

Episode of Dual Neural Genetic Firefly (DNGF) Transmission Key Generation in New Normal Mode of COVID-19 Second Wave Telepsychiatry

Dey¹,

Karforma

Chowdhury

2022

J. Inst. Eng. India Ser. B

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The volume of E-commerce transactions had accelerated on huge scale due to COVID-19. Telemedicine comes under the segment of E-commerce, where the patients can get their treatments from isolates. Patients’ information security is a basic challenge in this COVID-19 telemedicine segment. The majority of the non-obtrusive and non-emergency patients are encouraged and treated distantly from their secludes without the inclusion of COVID-19 transmission. Anxiety, depressive disorders, stress, dementia, mood disorder, OCD, aggression, etc. are the critical mental challenges that have abruptly occurred during this COVID period. The present work focused on “New Normal Mode” of COVID-19 telepsychiatry so that the patients’ mental illness can be treated remotely in a secure way. An episode of dual neural-genetic firefly (DNGF) has been proposed on COVID-19 "New Normal Mode" 2nd wave telepsychiatry. The pool of transmission keys was generated with the help of firefly algorithm, neural perceptron, and genetic operations. Besides these, the proposed DNGF keys are effective to be used for different online psychiatric transactions. The objectivity of this paper is to generate a robust pool of transmission keys in order to nullify different types of intruding. It has effectively cleared Avalanche test and Strict Avalanche test. The outcome of parameterized functional security test has been recorded with adequacy. These were: 0.327933, 0.350467, 0.332533, 0.317867, and 0.350267 on the generated pool of DNGF. The correlation coefficient between the key generation time and parameterized functional security has been found to be Distinctive mathematical arranged examinations were directed on the proposed key pool. It has shown better reasonability on the part of the COVID-19 2nd wave telepsychiatry, which is a component of E-commerce.

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“…Many results were taken by their study. Meena et al [ 51 ] had designed two algorithms based on modified firefly in the context of updation equation with higher accuracy values.…”

Section: Related Workmentioning

confidence: 99%

Episode of Dual Neural Genetic Firefly (DNGF) Transmission Key Generation in New Normal Mode of COVID-19 Second Wave Telepsychiatry

Dey¹,

Karforma

Chowdhury

2022

J. Inst. Eng. India Ser. B

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“…In (21) did a comparative study between FA and bees algorithm, by finding the optimal solutions for continuous mathematical functions. Both in FA and a hybrid approach called FA-PSO, the convergence is modified by updating the brightness alone (22) . As per the results it was concluded that FA had outperformed bees optimization.…”

Section: Variants Of Famentioning

confidence: 99%

Modified Approach of NaFA based on its Step Size and its Performance in Bioreactor Processes

Meena¹,

Sankaran²,

Nivethithadevi³

2021

IJST

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Objectives: The objective of this work is to fine tune the variant of FA (Firefly Algorithm), NaFA (Firefly Algorithm with neighbourhood Attraction) by parameter tuning such as α, β , γ. Furthermore, a new variant calledStep Size Modified FA with neighbourhood attraction (SSMFA-N) has been proposed in which the step size is updated during the algorithm run so that a balance between local and global search is achieved. The considered objective functions are PO and ITAE. Methods: It is well known that parameters that are considered initially for any metaheuristic algorithms are purely trial and error basis and this leads to erroneous optimized results. While analysing the algorithms, NaFA (a variant of FA) has been considered for efficient convergence and good performance. On analysing it is noticed that FA and its variants' performances and convergence depend on Step Size(α), brightness (β ) and adsorption coefficient(γ). In both the FA and NaFA the parameters to be tuned for effective convergence are α, β and γ. It is also understood that the parameter β had been done separately and the parameters α and γ cannot be fine-tuned simultaneously. Therefore, in NaFA the parameter tuning for α has been done for the processes FOPDT, stable and unstable SOPDT and the new variant SSMFA-N is thus proposed. Findings: The algorithm is made to run in MATLAB and Simulink environment for three different processes such as FOPDT, Stable and Unstable Second order Process (bioreactor processes) with the objective functions of less PO and ITAE. The obtained results from all the three processes are compared with the conventional and optimization methods (PSO) and shown that SSMFA-N outperforms the conventional and optimization approaches in both the time domain and performance indices. Novelty: The novelty is the modification of step size of NaFA, which ultimately leads to a new variant called SSMFA-N.

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“…The firefly search algorithm and its variants have been applied to trajectory optimization [56][57][58], control parameter optimization [59,60], and dynamics [ [61][62][63] in what can be considered as an introductory investigation by looking for initial successes in applying the FA toward these astronautical research areas.…”

Section: Firefly Search Algorithmmentioning

confidence: 99%

An Overview of Evolutionary Algorithms toward Spacecraft Attitude Control

Cooper¹,

Smeresky²

2020

Advances in Spacecraft Attitude Control

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Evolutionary algorithms can be used to solve interesting problems for aeronautical and astronautical applications, and it is a must to review the fundamentals of the most common evolutionary algorithms being used for those applications. Genetic algorithms, particle swarm optimization, firefly algorithm, ant colony optimization, artificial bee colony optimization, and the cuckoo search algorithm are presented and discussed with an emphasis on astronautical applications. In summary, the genetic algorithm and its variants can be used for a large parameter space but is more efficient in global optimization using a smaller chromosome size such that the number of parameters being optimized simultaneously is less than 1000. It is found that PID controller parameters, nonlinear parameter identification, and trajectory optimization are applications ripe for the genetic algorithm. Ant colony optimization and artificial bee colony optimization are optimization routines more suited for combinatorics, such as with trajectory optimization, path planning, scheduling, and spacecraft load bearing. Particle swarm optimization, firefly algorithm, and cuckoo search algorithms are best suited for large parameter spaces due to the decrease in computation need and function calls when compared to the genetic algorithm family of optimizers. Key areas of investigation for these social evolution algorithms are in spacecraft trajectory planning and in parameter identification.

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An approach of firefly algorithm with modified brightness for PID and I-PD controllers of SISO systems

Cited by 10 publications

References 22 publications

Episode of Dual Neural Genetic Firefly (DNGF) Transmission Key Generation in New Normal Mode of COVID-19 Second Wave Telepsychiatry

Episode of Dual Neural Genetic Firefly (DNGF) Transmission Key Generation in New Normal Mode of COVID-19 Second Wave Telepsychiatry

Modified Approach of NaFA based on its Step Size and its Performance in Bioreactor Processes

An Overview of Evolutionary Algorithms toward Spacecraft Attitude Control

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