Fuzzy Dynamic Parameter Adaptation in the Harmony Search Algorithm for the Optimization of the Ball and Beam Controller
This paper presents a method for dynamic parameter adaptation in the harmony search algorithm (HS) based on fuzzy logic. The adaptation is performed using Type 1 (FHS), interval Type 2 (IT2FHS), and generalized Type 2 (GT2FHS) fuzzy systems as the number of improvisations or iterations advances, ach...
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| Veröffentlicht in: | Advances in Operations Research 2018-01, Vol.2018 (2018), p.1-16 |
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| creator | Castillo, Oscar Castro, Juan R. Valdez, Fevrier Peraza, Cinthia |
| description | This paper presents a method for dynamic parameter adaptation in the harmony search algorithm (HS) based on fuzzy logic. The adaptation is performed using Type 1 (FHS), interval Type 2 (IT2FHS), and generalized Type 2 (GT2FHS) fuzzy systems as the number of improvisations or iterations advances, achieving a better intensification and diversification. The main contribution of this work is the dynamic parameter adaptation using different types of fuzzy systems in the harmony search algorithm applied to optimization of the membership functions for a benchmark control problem; in this case it is focused on the ball and beam controller. Experiments are presented with the HS, FHS, IT2FHS, and GT2FHS with noise (uniform random number) and without noise for the controller, and the following error metrics are obtained: ITAE, ITSE, IAE, ISE, and RMSE, to validate the efficacy of the proposed methods. |
| doi_str_mv | 10.1155/2018/3092872 |
| format | Article |
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The adaptation is performed using Type 1 (FHS), interval Type 2 (IT2FHS), and generalized Type 2 (GT2FHS) fuzzy systems as the number of improvisations or iterations advances, achieving a better intensification and diversification. The main contribution of this work is the dynamic parameter adaptation using different types of fuzzy systems in the harmony search algorithm applied to optimization of the membership functions for a benchmark control problem; in this case it is focused on the ball and beam controller. Experiments are presented with the HS, FHS, IT2FHS, and GT2FHS with noise (uniform random number) and without noise for the controller, and the following error metrics are obtained: ITAE, ITSE, IAE, ISE, and RMSE, to validate the efficacy of the proposed methods.</description><identifier>ISSN: 1687-9147</identifier><identifier>EISSN: 1687-9155</identifier><identifier>DOI: 10.1155/2018/3092872</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Adaptation ; Algorithms ; Analysis ; Control systems ; Controllers ; Fuzzy algorithms ; Fuzzy control ; Fuzzy logic ; Fuzzy sets ; Fuzzy systems ; Harmony (Music) ; Improvisation ; Information science ; Linguistics ; Mathematical optimization ; Methods ; Operations research ; Optimization ; Parameters ; Random numbers ; Search algorithms ; Variables</subject><ispartof>Advances in Operations Research, 2018-01, Vol.2018 (2018), p.1-16</ispartof><rights>Copyright © 2018 Cinthia Peraza et al.</rights><rights>COPYRIGHT 2018 John Wiley & Sons, Inc.</rights><rights>Copyright © 2018 Cinthia Peraza et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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The adaptation is performed using Type 1 (FHS), interval Type 2 (IT2FHS), and generalized Type 2 (GT2FHS) fuzzy systems as the number of improvisations or iterations advances, achieving a better intensification and diversification. The main contribution of this work is the dynamic parameter adaptation using different types of fuzzy systems in the harmony search algorithm applied to optimization of the membership functions for a benchmark control problem; in this case it is focused on the ball and beam controller. 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| subjects | Adaptation Algorithms Analysis Control systems Controllers Fuzzy algorithms Fuzzy control Fuzzy logic Fuzzy sets Fuzzy systems Harmony (Music) Improvisation Information science Linguistics Mathematical optimization Methods Operations research Optimization Parameters Random numbers Search algorithms Variables |
| title | Fuzzy Dynamic Parameter Adaptation in the Harmony Search Algorithm for the Optimization of the Ball and Beam Controller |
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