AnFiS-MoH: Systematic exploration of hybrid ANFIS frameworks via metaheuristic optimization hybridization with evolutionary and swarm-based algorithms
The adaptive neuro-fuzzy inference system (ANFIS) has shown promising performance in modeling nonlinear problems, leveraging the strengths of both neural networks and fuzzy inference systems. However, as the problem scale increases, the growing number of tunable parameters in ANFIS can make it chall...
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| Vydané v: | Applied soft computing Ročník 167; s. 112334 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
01.12.2024
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| ISSN: | 1568-4946 |
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| Abstract | The adaptive neuro-fuzzy inference system (ANFIS) has shown promising performance in modeling nonlinear problems, leveraging the strengths of both neural networks and fuzzy inference systems. However, as the problem scale increases, the growing number of tunable parameters in ANFIS can make it challenging to optimize via traditional gradient-based methods alone. This study introduces AnFiS-MoH, a novel framework that synergistically integrates ANFIS with metaheuristic optimization algorithms to address these challenges. By leveraging the global search capabilities of metaheuristics such as ant colony optimization (ACO), particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA), ANFIS-MOH enhances the parameter tuning process of ANFIS models. We evaluate ANFIS-MOH on benchmark datasets including Boston Housing and Wine Quality, demonstrating significant improvements in prediction accuracy and generalization compared to traditional ANFIS and neural network approaches. The proposed framework achieves up to 20% reduction in Mean Squared Error and 15% increase in R2 scores, particularly excelling in handling high-dimensional, noisy data. This work contributes to the field of hybrid intelligent systems by introducing effective ways to combine the strengths of ANFIS with powerful metaheuristic optimization algorithms. The findings suggest that such hybrid approaches can be effective in tackling challenging nonlinear modeling problems. Our code is available at https://github.com/AmbitYuki/Metaheuristic-Adaptive-ANFIS.
•Hybrid models combining fuzzy inference and metaheuristics.•Effective global optimization of ANFIS parameters.•Robust generalization capability on nonlinear regression problems.•Promising approach for complex optimization across diverse domains. |
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| AbstractList | The adaptive neuro-fuzzy inference system (ANFIS) has shown promising performance in modeling nonlinear problems, leveraging the strengths of both neural networks and fuzzy inference systems. However, as the problem scale increases, the growing number of tunable parameters in ANFIS can make it challenging to optimize via traditional gradient-based methods alone. This study introduces AnFiS-MoH, a novel framework that synergistically integrates ANFIS with metaheuristic optimization algorithms to address these challenges. By leveraging the global search capabilities of metaheuristics such as ant colony optimization (ACO), particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA), ANFIS-MOH enhances the parameter tuning process of ANFIS models. We evaluate ANFIS-MOH on benchmark datasets including Boston Housing and Wine Quality, demonstrating significant improvements in prediction accuracy and generalization compared to traditional ANFIS and neural network approaches. The proposed framework achieves up to 20% reduction in Mean Squared Error and 15% increase in R2 scores, particularly excelling in handling high-dimensional, noisy data. This work contributes to the field of hybrid intelligent systems by introducing effective ways to combine the strengths of ANFIS with powerful metaheuristic optimization algorithms. The findings suggest that such hybrid approaches can be effective in tackling challenging nonlinear modeling problems. Our code is available at https://github.com/AmbitYuki/Metaheuristic-Adaptive-ANFIS.
•Hybrid models combining fuzzy inference and metaheuristics.•Effective global optimization of ANFIS parameters.•Robust generalization capability on nonlinear regression problems.•Promising approach for complex optimization across diverse domains. |
| ArticleNumber | 112334 |
| Author | Li, Anji Sun, Hangling Wang, Haoyu Chen, Bin Zhou, Chenyu |
| Author_xml | – sequence: 1 givenname: Haoyu orcidid: 0000-0001-9575-7345 surname: Wang fullname: Wang, Haoyu organization: School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China – sequence: 2 givenname: Bin surname: Chen fullname: Chen, Bin email: chenbin1991@usst.edu.cn organization: School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China – sequence: 3 givenname: Hangling surname: Sun fullname: Sun, Hangling organization: Hengtu Imalligent Technology (Shanghai) Co., Ltd., Shanghai, China – sequence: 4 givenname: Anji surname: Li fullname: Li, Anji organization: Abbott Laboratories(Shanghai) Co., Ltd., Shanghai, China – sequence: 5 givenname: Chenyu surname: Zhou fullname: Zhou, Chenyu organization: School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China |
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