A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm

Brajević, Ivona; Stanimirović, Predrag S.; Li, Shuai; Cao, Xinwei

doi:10.2991/ijcis.d.200612.001

Cited by 22 publications

(7 citation statements)

References 30 publications

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“…After numerous successful deployments of updated and hybridized FA variants, there are still opportunities for improvement. Initiatives to hybridize metaheuristic algorithms, such as those described in [25]- [27] have emerged. Similarly, the exploration mechanism of the FA can be combined with processes from various metaheuristics.…”

Section: Metaheuristicmentioning

confidence: 99%

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Scouting Firefly Algorithm and its Performance on Global Optimization Problems

Villaruz¹,

Gerardo²,

Gamao³

et al. 2023

IJACSA

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For effective optimization, metaheuristics should maintain the proper balance between exploration and exploitation. However, the standard firefly algorithm (FA) posted some limitations in its exploration process that can eventually lead to premature convergence, affecting its performance and adding uncertainty to the optimization results. To address these constraints, this study introduces an additional novel search mechanism for the standard FA inspired by the behavior of the scout bee in the artificial bee colony (ABC) algorithm, termed the "Scouting FA". Specifically, fireflies stuck in the local optima will take directed extra random walks to escape toward the region of the optimum solution, thus improving convergence accuracy. Empirical findings on the five standard benchmark functions have validated the effects of this modification and revealed that Scouting FA is superior to its original version.

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Section: Metaheuristicmentioning

confidence: 99%

“…A hybrid of firefly and multi-strategy ABC for optimizing single-objective problems is presented in [27]. The FA performs the global search, while the novel multi-strategy ABC does the local search.…”

Section: Related Workmentioning

confidence: 99%

Scouting Firefly Algorithm and its Performance on Global Optimization Problems

Villaruz¹,

Gerardo²,

Gamao³

et al. 2023

IJACSA

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“…But the time and space costs it takes are often unacceptable. Common metaheuristic algorithms include GA [23], bee colony algorithm [24], water drop algorithm, etc. [25].…”

Section: Automatic Planningmentioning

confidence: 99%

Tourism Route Selection Model for Tourism Sustainable Development Based on Improved Genetic Algorithm

Qiu-hong

2022

International Transactions on Electrical Energy Systems

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The improvement of people’s quality of life also promotes the development of tourism. The traditional travel mode is no longer suitable for the needs of modern people. How to quickly determine the optimal tourist route according to the needs of different families is the current sustainable development direction of tourism. Scientific planning of tourist routes to minimize the cost and time of tourists is very important for the improvement of tourism experience. This study employs an improved genetic algorithm (IGA) to find the best tourist route based on this requirement. With the increase of tourist attractions, routes, and demands, the traditional genetic algorithm (GA) has problems such as premature convergence and poor local search ability when planning the tourist route. The existence of these problems will affect the effect of route planning. IGA proposes three improvements to traditional GA. One is to introduce the ant colony algorithm (ACA) to initialize the parameters in the GA. This algorithm is introduced to alleviate the over-reliance of GA on initializing the population and the poor adaptability of individual populations. Second, because the crossover probability parameter in GA has such a large influence on the final solution, this study proposes an adaptive strategy for adjusting the crossover probability to improve population fitness. Third, considering the weak local search ability of GA and the problem of premature convergence, this study introduces the 2-opt optimization algorithm to improve the quality of the solution. The results of the experimental analysis confirm the effectiveness of the proposed method.

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“…In the higher throughput model, SVM is used to estimate the accuracy of thyroid disease. Our proposed classifier system uses rough type‐2 fuzzy logic‐based SVM algorithm to classify data as hyperthyroid, normal, and hypothyroid (Brajević et al, 2020; Gebremedhen et al, 2020; Zheng, 2019).…”

Section: Thyroid Disease Multi‐class Classificationmentioning

confidence: 99%

A hybrid optimization algorithm‐based feature selection for thyroid disease classifier with rough type‐2 fuzzy support vector machine

Sureshkumar¹,

Balasubramaniam²,

Ravi

et al. 2021

Expert Systems

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Thyroid hormones are essential for all the metabolic and reproductive activities with significance to growth, and neuron development in the human body. The thyroid hormone dysfunction has many ill consequences, affecting the human population; thereby being a global epidemic. It is noticed that every one in 10 persons suffer from different thyroid disorders in India. In recent years, many researchers have implemented various disease predictive models based on Information and Communications Technology (ICT). Increasing the accuracy of disease classification is a critical and challenging task. To increase the accuracy of classification, in this paper, we propose a hybrid optimization algorithm-based feature selection design for thyroid disease classifier with rough type-2 fuzzy support vector machine. This work uses the hybrid optimization algorithm, which combines the firefly algorithm (FA) and butterfly optimization algorithm (BOA) to select the top-n features. The proposed hybrid firefly butterfly optimization-rough type-2 fuzzy support vector machine (HFBO-RT2FSVM) is evaluated with several key metrics such as specificity, accuracy, and sensitivity. We compare our approach with well-known benchmark methods such as improved grey wolf optimization linear support vector machine (IGWO Linear SVM) and mixedkernel support vector machine (MKSVM) methods. From the experimental evaluations, we justify that our technique improves the accuracy by large thereby precise in identifying the thyroid disease. HFBO-RT2FSVM model attained an accuracy of 99.28%, having specificity and sensitivity of 98 and 99.2%, respectively.

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A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm

Cited by 22 publications

References 30 publications

Scouting Firefly Algorithm and its Performance on Global Optimization Problems

Scouting Firefly Algorithm and its Performance on Global Optimization Problems

Tourism Route Selection Model for Tourism Sustainable Development Based on Improved Genetic Algorithm

A hybrid optimization algorithm‐based feature selection for thyroid disease classifier with rough type‐2 fuzzy support vector machine

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