A time-critical investigation of parameter tuning in differential evolution for non-linear global optimization

Parameter searching is one of the most important aspects in getting favorable results in optimization problems. It is even more important if the optimization problems are limited by time constraints. In a limited time constraint problems, it is crucial for any algorithms to get the best results or n...

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Main Authors: Jia, Hui Ong, Teo, Jason Tze Wi
Format: Article
Language:English
English
Published: Indonesian Society for Knowledge and Human Development 2016
Subjects:
Online Access:https://eprints.ums.edu.my/id/eprint/18800/1/A%20time.pdf
https://eprints.ums.edu.my/id/eprint/18800/7/A%20Time-Critical%20Investigation%20of%20Parameter%20Tuning%20in%20Differential.pdf
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author Jia, Hui Ong
Teo, Jason Tze Wi
author_facet Jia, Hui Ong
Teo, Jason Tze Wi
author_sort Jia, Hui Ong
collection UMS
description Parameter searching is one of the most important aspects in getting favorable results in optimization problems. It is even more important if the optimization problems are limited by time constraints. In a limited time constraint problems, it is crucial for any algorithms to get the best results or near-optimum results. In a previous study, Differential Evolution (DE) has been found as one of the best performing algorithms under time constraints. As this has help in answering which algorithm that yields results that are near-optimum under a limited time constraint. Hence to further enhance the performance of DE under time constraint evaluation, a throughout parameter searching for population size, mutation constant and f constant have been carried out. CEC 2015 Global Optimization Competition’s 15 scalable test problems are used as test suite for this study. In the previous study the same test suits has been used and the results from DE will be use as the benchmark for this study since it shows the best results among the previous tested algorithms. Eight different populations size are used and they are 10, 30, 50, 100, 150, 200, 300, and 500. Each of these populations size will run with mutation constant of 0.1 until 0.9 and from 0.1 until 0.9. It was found that population size 100, Cr = 0.9, F=0.5 outperform the benchmark results. It is also observed from the results that good higher Cr around 0.8 and 0.9 with low F around 0.3 to 0.4 yields good results for DE under time constraints evaluation
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spelling ums.eprints-188002020-12-20T04:17:03Z https://eprints.ums.edu.my/id/eprint/18800/ A time-critical investigation of parameter tuning in differential evolution for non-linear global optimization Jia, Hui Ong Teo, Jason Tze Wi QA Mathematics Parameter searching is one of the most important aspects in getting favorable results in optimization problems. It is even more important if the optimization problems are limited by time constraints. In a limited time constraint problems, it is crucial for any algorithms to get the best results or near-optimum results. In a previous study, Differential Evolution (DE) has been found as one of the best performing algorithms under time constraints. As this has help in answering which algorithm that yields results that are near-optimum under a limited time constraint. Hence to further enhance the performance of DE under time constraint evaluation, a throughout parameter searching for population size, mutation constant and f constant have been carried out. CEC 2015 Global Optimization Competition’s 15 scalable test problems are used as test suite for this study. In the previous study the same test suits has been used and the results from DE will be use as the benchmark for this study since it shows the best results among the previous tested algorithms. Eight different populations size are used and they are 10, 30, 50, 100, 150, 200, 300, and 500. Each of these populations size will run with mutation constant of 0.1 until 0.9 and from 0.1 until 0.9. It was found that population size 100, Cr = 0.9, F=0.5 outperform the benchmark results. It is also observed from the results that good higher Cr around 0.8 and 0.9 with low F around 0.3 to 0.4 yields good results for DE under time constraints evaluation Indonesian Society for Knowledge and Human Development 2016 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/18800/1/A%20time.pdf text en https://eprints.ums.edu.my/id/eprint/18800/7/A%20Time-Critical%20Investigation%20of%20Parameter%20Tuning%20in%20Differential.pdf Jia, Hui Ong and Teo, Jason Tze Wi (2016) A time-critical investigation of parameter tuning in differential evolution for non-linear global optimization. International Journal on Advanced Science, Engineering and Information Technology, 6 (4). pp. 426-436. ISSN 2088-5334 http://www.ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=799
spellingShingle QA Mathematics
Jia, Hui Ong
Teo, Jason Tze Wi
A time-critical investigation of parameter tuning in differential evolution for non-linear global optimization
title A time-critical investigation of parameter tuning in differential evolution for non-linear global optimization
title_full A time-critical investigation of parameter tuning in differential evolution for non-linear global optimization
title_fullStr A time-critical investigation of parameter tuning in differential evolution for non-linear global optimization
title_full_unstemmed A time-critical investigation of parameter tuning in differential evolution for non-linear global optimization
title_short A time-critical investigation of parameter tuning in differential evolution for non-linear global optimization
title_sort time critical investigation of parameter tuning in differential evolution for non linear global optimization
topic QA Mathematics
url https://eprints.ums.edu.my/id/eprint/18800/1/A%20time.pdf
https://eprints.ums.edu.my/id/eprint/18800/7/A%20Time-Critical%20Investigation%20of%20Parameter%20Tuning%20in%20Differential.pdf
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