An improved butterfly optimization algorithm with chaos

Arora, Sankalap; Singh, Satvir

doi:10.3233/jifs-16798

Cited by 117 publications

(56 citation statements)

References 39 publications

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“…Until now, several researchers have developed various metaheuristics, which find their source of inspiration in nature. Some of them are as follows: genetic algorithm (GA) (first described by John Henry Holland in the 1960s) is based on the natural selection [2][3][4], ant colony optimization (ACO) (initially proposed by Marco Dorigo in 1992) finds its inspiration from ant colony behavior [5][6][7], particle swarm optimization (PSO) (developed by James Kennedy and Russell C. Eberhart in 1995) is based on social flocking behavior of birds [8][9][10], artificial bee colony (ABC) (invented by Dervis Karaboga in 2005) is inspired by intelligent foraging behavior of honey bee swarm [11,12], magnetic charged system search [13], charged system search [14], firefly algorithm (FA) (created by Xin-She Yang in 2008) is inspired by the flashing light pattern of fireflies [15][16][17][18], biogeography-based optimization (BBO) (introduced by Dan Simon in 2008) is based on the equilibrium theory of island biogeography [19][20][21], bat algorithm (BA) (proposed by Xin-She Yang in 2010) is a metaheuristic algorithm, which is inspired by the echolocation behavior of microbats [22,23], and more recently, butterfly optimization algorithm (BOA) (developed by Arora and Singh in 2015) which finds its source of inspiration in food foraging behavior of butterflies [24].…”

Section: List Of Symbols Bmentioning

confidence: 99%

A modified butterfly optimization algorithm for mechanical design optimization problems

Arora

Singh

Yetilmezsoy

2018

J Braz. Soc. Mech. Sci. Eng.

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This paper presents a modified butterfly optimization algorithm (MBOA) for solving mechanical design optimization problems. The modification is focused on an additional intensive exploitation phase which provides more chance to solutions to improve itself. The performance of the proposed algorithm is validated on fifteen benchmark test functions and three engineering design problems which have different natures of objective functions, constraints and decision variables. The experimental results are analyzed in comparison with those reported in the literature. The results indicate that the MBOA provides very competitive results in comparison with other existing optimization algorithms.

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Section: List Of Symbols Bmentioning

confidence: 99%

A modified butterfly optimization algorithm for mechanical design optimization problems

Arora

Singh

Yetilmezsoy

2018

J Braz. Soc. Mech. Sci. Eng.

Self Cite

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“…It has been reported that the PSO algorithm increases the ANFIS performance. Although Mahdavi‐Meymand et al . reported that the BBO has decreased the performance of ANFIS, some researchers reported the suitable results of ANFIS‐BBO .…”

Section: Resultsmentioning

confidence: 99%

“…In other words, the butterflies took the smell and analyzed it to distinguish the suitable pathway of a food source. The BOA algorithm imitated this procedure of food foraging of butterflies to find an optimal solution in the search space …”

Section: Methodsmentioning

confidence: 99%

Seasonal Short‐Term Prediction of Dissolved Oxygen in Rivers via Nature‐Inspired Algorithms

Fadaee

Mahdavi‐Meymand

Zounemat‐Kermani

2020

CLEAN Soil Air Water

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This study challenges the use of three nature-inspired algorithms as learning frameworks of the adaptive-neuro-fuzzy inference system (ANFIS) machine learning model for short-term modeling of dissolved oxygen (DO) concentrations. Particle swarm optimization (PSO), butterfly optimization algorithm (BOA), and biogeography-based optimization (BBO) are employed for developing predictive ANFIS models using seasonal 15 min data collected from the Rock Creek River in Washington, DC. Four independent variables are used as model inputs including water temperature (T), river discharge (Q), specific conductance (SC), and pH. The Mallow's Cp and R 2 parameters are used for choosing the best input parameters for the models. The models are assessed by several statistics such as the coefficient of determination (R 2 ), root-mean-square error (RMSE), Nash-Sutcliffe efficiency, mean absolute error, and the percent bias. The results indicate that the performance of all-nature-inspired algorithms is close to each other. However, based on the calculated RMSE, they enhance the accuracy of standard ANFIS in the spring, summer, fall, and winter around 13.79%, 15.94%, 6.25%, and 12.74%, respectively. Overall, the ANFIS-PSO and ANFIS-BOA provide slightly better results than the other ANFIS models.

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“…Biologically, butterflies find the source of food using sense receptors. e role of these sense receptors, also called chemoreceptors, is to sense fragrance/smell [33].…”

Section: Butterfly Optimization Algorithmmentioning

confidence: 99%

Salp Swarm Algorithm for Node Localization in Wireless Sensor Networks

Kanoosh

Houssein

Selim

2019

Journal of Computer Networks and Communications

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Nodes localization in a wireless sensor network (WSN) aims for calculating the coordinates of unknown nodes with the assist of known nodes. The performance of a WSN can be greatly affected by the localization accuracy. In this paper, a node localization scheme is proposed based on a recent bioinspired algorithm called Salp Swarm Algorithm (SSA). The proposed algorithm is compared to well-known optimization algorithms, namely, particle swarm optimization (PSO), Butterfly optimization algorithm (BOA), firefly algorithm (FA), and grey wolf optimizer (GWO) under different WSN deployments. The simulation results show that the proposed localization algorithm is better than the other algorithms in terms of mean localization error, computing time, and the number of localized nodes.

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An improved butterfly optimization algorithm with chaos

Cited by 117 publications

References 39 publications

A modified butterfly optimization algorithm for mechanical design optimization problems

A modified butterfly optimization algorithm for mechanical design optimization problems

Seasonal Short‐Term Prediction of Dissolved Oxygen in Rivers via Nature‐Inspired Algorithms

Salp Swarm Algorithm for Node Localization in Wireless Sensor Networks

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