An Educational Tool for the Ant Colony Optimization Algorithm

Li, Jing; Liu, Wei

doi:10.1109/etcs.2009.21

Cited by 4 publications

(5 citation statements)

References 13 publications

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“…The toolbox ACO for traveling salesman problem (TSP) is an experimental tool that is used to run the ant colony optimization (ACO) with friendly graphical user interfaces. 7 It provides a Visual Basic code with interfaces that has the ACO algorithm ready to change its parameters, also has optimization problems similar to the TSP, and a space to simulate each step. Despite being a complete guide of the ACO algorithm it does not teach the basics of optimization, it only reinforces the knowledge about ACO.…”

Section: Introductionmentioning

confidence: 99%

Learning classical and metaheuristic optimization techniques by using an educational platform based on LEGO robots

Zaldívar

Cuevas

Maciel

et al. 2019

The International Journal of Electrical Engineering & Educa

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Optimization is a very important issue to efficiently solve engineering problems. The clear comprehension of the elements and processes of optimizer algorithms is fundamental to ensure that engineer students are capable to apply, tune, and design new algorithms. This paper presents an educational platform based on LEGO! EV3 and MATLAB to assist the learning of the principles of classical and metaheuristic optimization algorithms at undergraduate level, by providing a simple and easy-to-follow teaching setup. The proposed study aims to accompany students through the learning of optimization fundamentals by building hands-on robotic experiments. The proposed educational platform has been successfully applied to several undergraduate courses within the Electronics Department at the University of Guadalajara. The description of each experiment and the evaluation of their impact in the student performance are both provided in the paper.

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Section: Introductionmentioning

confidence: 99%

Learning classical and metaheuristic optimization techniques by using an educational platform based on LEGO robots

Zaldívar

Cuevas

Maciel

et al. 2019

The International Journal of Electrical Engineering & Educa

View full text Add to dashboard Cite

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“…In the literature, there have been a number of studies on selecting the proper algorithm and determining the best parameter values for the selected algorithm . In the first study, metaheuristic algorithms were developed for the web‐based education tool .…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the new bio‐inspired optimization methods can be researched with benchmark mathematical functions. In a study proposed by Li and Liu, an education tool was proposed for ant colony optimization . The traveling salesman problem was used to implement this tool, and a useful graphical interface was developed.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, these tools are web‐based or program‐dependent (like Matlab) . There is no cross‐platform support in those, except web‐based ones . None of these has a specially designed interface to operate on mobile devices .…”

Section: Introductionmentioning

confidence: 99%

“…There is no cross‐platform support in those, except web‐based ones . None of these has a specially designed interface to operate on mobile devices . In this study, a test tool was developed for eight different optimization algorithms commonly used by researchers (genetic algorithms, artificial immune system, differential evolution algorithm, particle swarm optimization algorithm, simulated annealing algorithm, tabu search algorithm, artificial bee colony algorithm, and ant colony optimization algorithm).…”

Section: Introductionmentioning

confidence: 99%

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Mobile device based test tool for optimization algorithms

İlhan

2016

Comp Applic In Engineering

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Optimization can be defined as the operation of finding the best solution for a problem. This operation is performed by changing the initial parameters using existing data. There are various optimization algorithms to solve these kinds of problems; however, it cannot be expected that all optimization algorithms offer a proper and effective solution to all optimization problems. Therefore, it is necessary to select the proper algorithm by using similar benchmark functions to the problem, and to determine the best parameter values for the selected algorithm. In this study, a test tool that can run on the devices using the Windows, OS X, Android, and IOS operating systems was developed for eight different optimization algorithms: genetic, artificial immune, differential evolution, particle swarm optimization, simulated annealing, tabu search, artificial bee colony, and ant colony optimization algorithms. Six Hump Camel Back, Rastrigin, Shubert, Schwefel, and Drop Wave were the preferred benchmark functions. Special user defined special functions were also permitted. The developed test tool was tested for all the optimization algorithms in different platforms. In spite of the small differences in the running times, the results show that the tool can easily be used in Windows, OS X, Android, and IOS devices. ß 2016 Wiley Periodicals, Inc. Comput Appl Eng Educ 24:744-754, 2016; View this article online at wileyonlinelibrary.com/journal/ cae;

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