An Effective Hybrid Butterfly Optimization Algorithm with Artificial Bee Colony for Numerical Optimization

Arora, Sankalap; Singh, Satvir

doi:10.9781/ijimai.2017.442

Cited by 44 publications

(23 citation statements)

References 27 publications

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“…As yet, there is no universal algorithm or method that can solve all above cases. The new path planning method should be researched, such as artificial immune algorithm [72], artificial bee colony [73,74], etc. Especially two or more algorithms are combined to improve the quality and efficiency of the solution.…”

Section: Discussionmentioning

confidence: 99%

Path Planning for the Mobile Robot: A Review

2018

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Good path planning technology of mobile robot can not only save a lot of time, but also reduce the wear and capital investment of mobile robot. Several methodologies have been proposed and reported in the literature for the path planning of mobile robot. Although these methodologies do not guarantee an optimal solution, they have been successfully applied in their works. The purpose of this paper is to review the modeling, optimization criteria and solution algorithms for the path planning of mobile robot. The survey shows GA (genetic algorithm), PSO (particle swarm optimization algorithm), APF (artificial potential field), and ACO (ant colony optimization algorithm) are the most used approaches to solve the path planning of mobile robot. Finally, future research is discussed which could provide reference for the path planning of mobile robot.

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Section: Discussionmentioning

confidence: 99%

Path Planning for the Mobile Robot: A Review

2018

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“…The polarization curves of the PEMFC is highly nonlinear and the proposed model is based on a set of nonlinear equations that contain a set of semi-empirical adjustable parameters. The conventional optimization methods are not suitable for such complicated optimization problems, so metaheuristic optimization techniques are used for such issues [18], [19]. Based on parameter modelling, various algorithms have been proposed to solve the optimization problem of PEMFC parameters.…”

Section: Introductionmentioning

confidence: 99%

Tree Growth Algorithm for Parameter Identification of Proton Exchange Membrane Fuel Cell Models

Kamel¹,

Jurado²,

Sultan³

et al. 2020

IJIMAI

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Demonstrating an accurate mathematical model is a mandatory issue for realistic simulation, optimization and performance evaluation of proton exchange membrane fuel cells (PEMFCs). The main goal of this study is to demonstrate a precise mathematical model of PEMFCs through estimating the optimal values of the unknown parameters of these cells. In this paper, an efficient optimization technique, namely, Tree Growth Algorithm (TGA) is applied for extracting the optimal parameters of different PEMFC stacks. The total of the squared deviations (TSD) between the experimentally measured data and the estimated ones is adopted as the objective function. The effectiveness of the developed parameter identification algorithm is validated through four case studies of commercial PEMFC stacks under various operating conditions. Moreover, comprehensive comparisons with other optimization algorithms under the same study cases are demonstrated. Statistical analysis is presented to evaluate the accuracy and reliability of the developed algorithm in solving the studied optimization problem.

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“…Solving nonlinear problems is very important in numerical analysis and finds many applications in physics, engineering, and other applied sciences [1,2]. These problems occur in a variety of areas such as initial and boundary values, heat and fluid flow, electrostatics, or even in global positioning systems (GPS) [3][4][5][6]. It is difficult to find analytical solutions for nonlinear problems, but numerical techniques may be used to obtain approximate solutions.…”

Section: Introductionmentioning

confidence: 99%

Efficient Three-Step Class of Eighth-Order Multiple Root Solvers and Their Dynamics

et al. 2019

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This article proposes a wide general class of optimal eighth-order techniques for approximating multiple zeros of scalar nonlinear equations. The new strategy adopts a weight function with an approach involving the function-to-function ratio. An extensive convergence analysis is performed for the eighth-order convergence of the algorithm. It is verified that some of the existing techniques are special cases of the new scheme. The algorithms are tested in several real-life problems to check their accuracy and applicability. The results of the dynamical study confirm that the new methods are more stable and accurate than the existing schemes.

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An Effective Hybrid Butterfly Optimization Algorithm with Artificial Bee Colony for Numerical Optimization

Cited by 44 publications

References 27 publications

Path Planning for the Mobile Robot: A Review

Path Planning for the Mobile Robot: A Review

Tree Growth Algorithm for Parameter Identification of Proton Exchange Membrane Fuel Cell Models

Efficient Three-Step Class of Eighth-Order Multiple Root Solvers and Their Dynamics

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