Performance evaluation of firefly algorithm with variation in sorting for non-linear benchmark problems

Umbarkar, A. J.; Balande, Umesh; Seth, P. D.

doi:10.1063/1.4981972

Cited by 15 publications

(20 citation statements)

References 19 publications

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“…Nayak et al (2020) Johari et al (2017) to achieve an improved solution for search space exploration. To decrease time complexity of the original FA algorithm, Umbarkar, Balande & Seth (2017) proposed a modified FA algorithm that ranks the fireflies based on a quick sort algorithm instead of bubble sort technique.…”

Section: Artificial Bee Colony Optimization (Abc)mentioning

confidence: 99%

A review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing environments

Qawqzeh

Alharbi

Jaradat

et al. 2021

PeerJ Computer Science

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Background This review focuses on reviewing the recent publications of swarm intelligence algorithms (particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), and the firefly algorithm (FA)) in scheduling and optimization problems. Swarm intelligence (SI) can be described as the intelligent behavior of natural living animals, fishes, and insects. In fact, it is based on agent groups or populations in which they have a reliable connection among them and with their environment. Inside such a group or population, each agent (member) performs according to certain rules that make it capable of maximizing the overall utility of that certain group or population. It can be described as a collective intelligence among self-organized members in certain group or population. In fact, biology inspired many researchers to mimic the behavior of certain natural swarms (birds, animals, or insects) to solve some computational problems effectively. Methodology SI techniques were utilized in cloud computing environment seeking optimum scheduling strategies. Hence, the most recent publications (2015–2021) that belongs to SI algorithms are reviewed and summarized. Results It is clear that the number of algorithms for cloud computing optimization is increasing rapidly. The number of PSO, ACO, ABC, and FA related journal papers has been visibility increased. However, it is noticeably that many recently emerging algorithms were emerged based on the amendment on the original SI algorithms especially the PSO algorithm. Conclusions The major intention of this work is to motivate interested researchers to develop and innovate new SI-based solutions that can handle complex and multi-objective computational problems.

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Section: Artificial Bee Colony Optimization (Abc)mentioning

confidence: 99%

A review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing environments

Qawqzeh

Alharbi

Jaradat

et al. 2021

PeerJ Computer Science

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“…The FA is an algorithm that was developed by Xin-She Yang at Cambridge University in 2007 [13]. The FA is a swarm intelligence-based metaheuristic approach inspired by the behaviour of fireflies.…”

Section: Firefly Algorithmmentioning

confidence: 99%

Fuzzy Modelling using Firefly Algorithm for Phishing Detection

Syahirah¹,

Arfian²,

Mezhuyev³

et al. 2019

Adv. sci. technol. eng. syst. j.

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A fuzzy system is a rule-based system that uses human experts' knowledge to make a particular decision, while fuzzy modeling refers to the identification process of the fuzzy parameters. To generate the fuzzy parameters automatically, an optimization method is needed. One of the suitable methods provides the Firefly Algorithm (FA). FA is a natureinspired algorithm that uses fireflies' behavior to interpret data. This study explains in detail how fuzzy modeling works by using FA for detecting phishing. Phishing is an unsettled security problem that occurs in the world of internet connected computers. In order to experiment with the proposed method for the security threats, a database of phishing websites and SMS from different sources were used. As a result, the average accuracy for the phishing websites dataset achieved 98.86%, while the average value for the SMS dataset is 97.49%. In conclusion, both datasets show the best result in terms of the accuracy value for fuzzy modeling by using FA.

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“…Regression analysis used to predict or find a relationship between the independent variable and dependent variable moreover, its impact on the dependent variable. Thus regression analysis finds a causal relationship between the variables [8]. The linear regression equation is shown in Equation 1.…”

Section: Fig 1 Research Frameworkmentioning

confidence: 99%

“…Sorting refers to ordering data in an increasing or decreasing fashion according to some linear relationship among the data items in a particular data structure. Sorting techniques have attracted a great deal of research for efficiency, practicality, performance, complexity and types of data structures [7,8]. Rigorous efforts have been taken to improve sorting techniques like merge sort, bubble sort, insertion sort, quick sort, selection sort, and each of them has a different mechanism to reorder elements which increase the performance and efficiency of the practical applications as well as reducing the time complexity for each one.…”

Section: Introductionmentioning

confidence: 99%

Comparison Study of Sorting Techniques in Static Data Structure

Frak¹,

Saringat²,

Prasetyo³

et al. 2018

IJIE

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To manage and organize large data is imperative in order to formulate the data analysis and data processing efficiency. Therefore, this paper investigates the set of sorting techniques to observe which technique to provide better efficiency. Five types of sorting techniques of static data structure, Bubble, Insertion, Selection with O(n 2) complexity and Merge, Quick with O(n log n) complexity have been used and tested on four groups between (100-30000) of dataset. To validate the performance of sorting techniques, three performance metrics which are time complexity, execution time and size of dataset were used. All experimental setups were accomplished using simple linear regression. The experimental results illustrate that Quick sort is more efficiency than other sorting and Selection sort is more efficient than Bubble and Insertion in large data size using array. In addition, Bubble, Insertion and Selection have good performance for small data size using array thus, sorting technique with behaviour O(n log n) is more efficient than sorting technique with behaviour O(n 2) using array.

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Performance evaluation of firefly algorithm with variation in sorting for non-linear benchmark problems

Cited by 15 publications

References 19 publications

A review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing environments

A review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing environments

Fuzzy Modelling using Firefly Algorithm for Phishing Detection

Comparison Study of Sorting Techniques in Static Data Structure

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