A hybrid discrete firefly algorithm to solve flow shop scheduling problems to minimise total flow time

Marichelvam, M.K.; Mani, Geetha

doi:10.1504/ijbic.2016.079572

Cited by 16 publications

(6 citation statements)

References 48 publications

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“…The Firefly Algorithm (FA) is based on the communication behaviour of tropical fireflies and the idealized behaviour of the flashing patterns. FA uses the following three idealized rules (22)(23) to build the mathematical model of the algorithm:…”

Section: Results and Discussion Firefly Algorithm (Fa)mentioning

confidence: 99%

Optimal Power Flow Using firefly-algorithm

Karboune

Chaib

Arif

et al. 2021

International Journal of Development

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In this paper, the Firefly Algorithm (FA) is proposed to find the optimal solution for the Optimal Power Flow (OPF) problem in a power system. The proposed algorithm is applied to determine the optimal settings of control variables of the OPF problem. The performance of the proposed algorithm was examined and tested on the standard IEEE 30bus test system with different objective functions and compared to other methods reported in the literature recently. Simulation results clearly obtained from the proposed (FA) approach indicated that (FA) provides an effective and robust high-quality solution for the OPF problem.

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Section: Results and Discussion Firefly Algorithm (Fa)mentioning

confidence: 99%

Optimal Power Flow Using firefly-algorithm

Karboune

Chaib

Arif

et al. 2021

International Journal of Development

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“…The luminescence of the tropical fireflies acts as the inspiration for the firefly algorithm, a swarm intelligence, metaheuristic optimisation technique. 41 – 43 Firefly algorithm depends on three idealised rules: (1) Since fireflies are all unisex, they can be attracted to one another regardless of sex. (2) The attractiveness reduces with increasing distance between the fireflies and is closely correlated to the intensity of the light of the fireflies.…”

Section: Methodsmentioning

confidence: 99%

Firefly-SVM predictive model for breast cancer subgroup classification with clinicopathological parameters

Sarkar,

Mali

2023

DIGITAL HEALTH

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Background Breast cancer is a highly predominant destructive disease among women characterised with varied tumour biology, molecular subgroups and diverse clinicopathological specifications. The potentiality of machine learning to transform complex medical data into meaningful knowledge has led to its application in breast cancer detection and prognostic evaluation. Objective The emergence of data-driven diagnostic model for assisting clinicians in diagnostic decision making has gained an increasing curiosity in breast cancer identification and analysis. This motivated us to develop a breast cancer data-driven model for subtype classification more accurately. Method In this article, we proposed a firefly-support vector machine (SVM) breast cancer predictive model that uses clinicopathological and demographic data gathered from various tertiary care cancer hospitals or oncological centres to distinguish between patients with triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC). Results The results of the firefly-support vector machine (firefly-SVM) predictive model were distinguished from the traditional grid search-support vector machine (Grid-SVM) model, particle swarm optimisation-support vector machine (PSO-SVM) and genetic algorithm-support vector machine (GA-SVM) hybrid models through hyperparameter tuning. The findings show that the recommended firefly-SVM classification model outperformed other existing models in terms of prediction accuracy (93.4%, 86.6%, 69.6%) for automated SVM parameter selection. The effectiveness of the prediction model was also evaluated using well-known metrics, such as the F1-score, mean square error, area under the ROC curve, logarithmic loss and precision–recall curve. Conclusion Firefly-SVM predictive model may be treated as an alternate tool for breast cancer subgroup classification that would benefit the clinicians for managing the patient with proper treatment and diagnostic outcome.

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“…For the hybrid methods, discrete firefly algorithm usually focuses on an extensive search for the solution space while the local search algorithm is generally used to reschedule the results for a speedy and accuracy convergence. Marichelvam et al (2016) [113] proposed a hybrid discrete firefly algorithm (HDFA) to solve the FSSPs to minimize the total flow time.…”

Section: (12) Combination Algorithms and Recent Studies Based On Famentioning

confidence: 99%

Review of job shop scheduling research and its new perspectives under Industry 4.0

et al. 2017

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Traditional job shop scheduling is concentrated on centralized scheduling or semi-distributed scheduling. Under the Industry 4.0, the scheduling should deal with a smart and distributed manufacturing system supported by novel and emerging manufacturing technologies such as mass customization, Cyber-Physics Systems (CPS), Digital Twin, and SMAC (Social, Mobile, Analytics, Cloud). The scheduling research needs to shift its focus to smart distributed scheduling modeling and optimization. In order to transferring traditional scheduling into smart distributed scheduling (SDS), we aim to answer two questions: (1) what traditional scheduling methods and techniques can be combined and reused in SDS and (2) what are new methods and techniques required for SDS. In this paper, we first review existing researches from over 120 papers and answer the first question and then we explore a future research direction in SDS and discuss the new techniques for developing future new JSP scheduling models and constructing a framework on solving the JSP problem under Industry 4.0.

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A hybrid discrete firefly algorithm to solve flow shop scheduling problems to minimise total flow time

Cited by 16 publications

References 48 publications

Optimal Power Flow Using firefly-algorithm

Optimal Power Flow Using firefly-algorithm

Firefly-SVM predictive model for breast cancer subgroup classification with clinicopathological parameters

Review of job shop scheduling research and its new perspectives under Industry 4.0

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