Self-Adaptive Step Firefly Algorithm

Yu, Shaoming; Yang, Shanlin; Su, Shoubao

doi:10.1155/2013/832718

Cited by 44 publications

(46 citation statements)

References 19 publications

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“…On the other hand, equation 2 is used to compute the brightness of each firefly which will define the value of the firefly's attractiveness [18] [19]. In the equation, the distance between the two fireflies is denoted by 'r' and β0 defines their attractiveness at r=0.…”

Section: B Firefly Algorithmmentioning

confidence: 99%

Improved Expectation Maximization (EM) Algorithm based on Initial Parameter Selection

Masangcap¹

2018

IJRASET

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and high computational cost. Therefore, these remain the need to enhance EM algorithm in its initialization stage to produce good clustering results and to take less computational time. Since good initialization leads to fast convergence, there is a challenge of improving the initialization stage of the EM Algorithm. With that, this study aims to improve the Expectation-Maximization (EM) algorithm based on the initial parameters selection. An improved initial parameters selection technique for EM algorithm is introduced in this study which uses the concept of firefly movement and light intensity of firefly algorithm. A comparison of the clustering performance of the Enhanced EM algorithm (EnEM) to the Standard EM algorithm (StEM) in terms of clustering fitness, clustering error and computing time of the two algorithms is discussed. The application of an enhanced EM algorithm as a clustering method will open more opportunities to discover new knowledge as an outcome of a more precise data analysis.

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Section: B Firefly Algorithmmentioning

confidence: 99%

Improved Expectation Maximization (EM) Algorithm based on Initial Parameter Selection

Masangcap¹

2018

IJRASET

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“…In [19], they used different chaotic maps to replace the parameters of the basic FA, and revealed the improvement of the chaotic FA due to the application of deterministic chaotic signals in place of constant values. Yu et al [18] proposed a self-adaptive step firefly algorithm. Its core idea was to set the step of each firefly varying with the iteration according to each firefly's historical information and current situation.…”

Section: State-of-the-art Fa Variantsmentioning

confidence: 99%

“…Such as fuzzy FA [17], self-adaptive step FA [18], chaotic FA [19], jumper FA [20], Lévy-flight FA [21], etc.…”

Section: Introductionmentioning

confidence: 99%

Enhancing firefly algorithm using generalized opposition-based learning

Zhu²,

et al. 2015

Computing

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Firefly algorithm has been shown to yield good performance for solving various optimization problems. However, under some conditions, FA may converge prematurely and thus may be trapped in local optima due to loss of population diversity. To overcome this defect, inspired by the concept of opposition-based learning, a strategy to increase the performance of firefly algorithm is proposed. The idea is to replace the worst firefly with a new constructed firefly. This new constructed firefly is created by taken some elements from the opposition number of the worst firefly or the position of the brightest firefly. After this operation, the worst firefly is forced to escape from the normal path and can help it to escape from local optima. Experiments on 16 standard benchmark functions show that our method can improve accuracy of the basic firefly algorithm.

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“…This MSA-FFA was modified by Galvez and Iglesias and adopted for continuous optimization problems [81]. Yu et al [113] proposed a self-adaptive step FA to avoid falling into the local optimum and reduce the impact of the maximum of generations. Author's core idea was to set the step of each firefly varying with the iteration according to current situation and also historical information of fireflies.…”

Section: Adaptive Si-based Algorithmsmentioning

confidence: 99%

Adaptation and Hybridization in Nature-Inspired Algorithms

Fister

Strnad

Yang³

2015

Adaptation, Learning, and Optimization

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Abstract. The aim of this chapter is to familiarize readers with the basics of adaptation and hybridization in nature-inspired algorithms as necessary for understanding the main contents of this book. Adaptation is a metaphor for flexible autonomous systems that respond to external changing factors (mostly environmental) by adapting their wellestablished behavior. Adaptation emerges in practically all areas of human activities as well. Such adaptation mechanisms can be used as a general problem-solving approach, though it may suffer from a lack of problem-specific knowledge. To solve specific problems with additional improvements of possible performance, hybridization can be used in order to incorporate a problem-specific knowledge from a problem domain. In order to discuss relevant issues as general as possible, the classification of problems is identified at first. Additionally, we focus on the biological foundations of adaptation that constitute the basis for the formulation of nature-inspired algorithms. This book highlights three types of inspirations from nature: the human brain, Darwinian natural selection, and the behavior of social living insects (e.g., ants, bees, etc.) and animals (e.g., swarm of birds, shoals of fish, etc.), which influence the development of artificial neural networks. evolutionary algorithms, and swarm intelligence, respectively. The mentioned algorithms that can be placed under the umbrella of computational intelligence are described from the viewpoint of adaptation and hybridization so as to show that these mechanisms are simple to develop and yet very efficient. Finally, a brief review of recent developed applications is presented.

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Self-Adaptive Step Firefly Algorithm

Cited by 44 publications

References 19 publications

Improved Expectation Maximization (EM) Algorithm based on Initial Parameter Selection

Improved Expectation Maximization (EM) Algorithm based on Initial Parameter Selection

Enhancing firefly algorithm using generalized opposition-based learning

Adaptation and Hybridization in Nature-Inspired Algorithms

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