Nature-Inspired Algorithms of Bees, Firefly and Bat for Noisy Non-Linear Optimisation Problems

Chai-Ead, Narumon; Aungkulanon, Pasura; Luangpaiboon, Pongchanun

doi:10.1142/9789814390019_0005

Cited by 17 publications

(18 citation statements)

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“…Firefly uses the flashing as a signal to attract other fireflies. A FA assumes three basic rules (Alomoush et al, 2013;Chai-ead et al, 2011;Yang, 2010b) which are described as follows:…”

Section: Firefly Algorithm (Fa)mentioning

confidence: 99%

A Hybrid Firefly Algorithm With Fuzzy-C Mean Algorithm for Mri Brain Segmentation

Alsmadi¹

2014

American Journal of Applied Sciences

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Image processing is one of the essential tasks to extract suspicious region and robust features from the Magnetic Resonance Imaging (MRI). A numbers of the segmentation algorithms were developed in order to satisfy and increasing the accuracy of brain tumor detection. In the medical image processing brain image segmentation is considered as a complex and challenging part. Fuzzy c-means is unsupervised method that has been implemented for clustering of the MRI and different purposes such as recognition of the pattern of interest and image segmentation. However; fuzzy c-means algorithm still suffers many drawbacks, such as low convergence rate, getting stuck in the local minima and vulnerable to initialization sensitivity. Firefly algorithm is a new population-based optimization method that has been used successfully for solving many complex problems. This paper proposed a new dynamic and intelligent clustering method for brain tumor segmentation using the hybridization of Firefly Algorithm (FA) with Fuzzy C-Means algorithm (FCM). In order to automatically segment MRI brain images and improve the capability of the FCM to automatically elicit the proper number and location of cluster centres and the number of pixels in each cluster in the abnormal (multiple sclerosis lesions) MRI images. The experimental results proved the effectiveness of the proposed FAFCM in enhancing the performance of the traditional FCM clustering. Moreover; the superiority of the FAFCM with other state-of-the-art segmentation methods is shown qualitatively and quantitatively. Conclusion: A novel efficient and reliable clustering algorithm presented in this work, which is called FAFCM based on the hybridization of the firefly algorithm with fuzzy c-mean clustering algorithm. Automatically; the hybridized algorithm has the capability to cluster and segment MRI brain images.

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“…Firefly uses the flashing as a signal to attract other fireflies. A FA assumes three basic rules (Alomoush et al, 2013;Chai-ead et al, 2011;Yang, 2010b) which are described as follows:…”

Section: Firefly Algorithm (Fa)mentioning

confidence: 99%

A Hybrid Firefly Algorithm With Fuzzy-C Mean Algorithm for Mri Brain Segmentation

Alsmadi¹

2014

American Journal of Applied Sciences

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“…The Bees Algorithm was also used to optimise noisy non-linear problems [79] and digital image filters [80], and for biometric fusion under missing data scenario [81].…”

Section: Applications Of the Bees Algorithmmentioning

confidence: 99%

Nature-Inspired Intelligent Optimisation Using the Bees Algorithm

Pham

Castellani

Thi

2014

Transactions on Computational Intelligence XIII

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Abstract.The Bees Algorithm models the foraging behaviour of honey bees in order to solve optimisation problems. The algorithm performs a kind of exploitative neighbourhood search combined with random explorative search. This paper describes the Bees Algorithm, and compares its functioning and performance with those of other state-of-the-art nature-inspired intelligent optimisation methods. Two application cases are presented: the minimisation of a set of well-known benchmark functions, and the training of neural networks to reproduce the inverse kinematics of a robot manipulator. In both cases, the Bees Algorithm proved its effectiveness and speed. Compared with other state-ofthe-art methods, the performance of the Bees Algorithm was very competitive.

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“…The most important parameter in firefly algorithm is γ, it plays very crucial role in determining the speed of convergence and how FA algorithm behaves. Theoretically, γ Є [0, ∞) but in actual practice, γ O(1) is determined by the characteristic length Г of the system to be optimized [20], [21]. So for almost all the applications it varies from 0.1 to 10.…”

Section: Light Intensity and Attractivenessmentioning

confidence: 99%

The Firefly Optimization Algorithm: Convergence Analysis and Parameter Selection

Arora¹,

Singh²

2013

IJCA

108

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The bio-inspired optimization techniques have obtained great attention in recent years due to its robustness, simplicity and efficiency to solve complex optimization problems. The firefly Optimization (FA or FFA) algorithm is an optimization method with these features. The algorithm is inspired by the flashing behavior of fireflies. In the algorithm, randomly generated solutions will be considered as fireflies, and brightness is assigned depending on their performance on the objective function. The algorithm is analyzed on basis of performance and success rate using five standard benchmark functions by which guidelines of parameter selection are derived. The tradeoff between exploration and exploitation is illustrated and discussed.

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Nature-Inspired Algorithms of Bees, Firefly and Bat for Noisy Non-Linear Optimisation Problems

Cited by 17 publications

References 0 publications

A Hybrid Firefly Algorithm With Fuzzy-C Mean Algorithm for Mri Brain Segmentation

A Hybrid Firefly Algorithm With Fuzzy-C Mean Algorithm for Mri Brain Segmentation

Nature-Inspired Intelligent Optimisation Using the Bees Algorithm

The Firefly Optimization Algorithm: Convergence Analysis and Parameter Selection

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