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  • Estimation of continuous parameters is frequent task in modelling and simulation. There are several general purpose algorithms available for this task. We benchmarked these algorithms in order to recommend an appropriate algorithm for our model identification problem. We present results of optimization algorithms for standard benchmarking functions and show the importance of proper parameter setting. When these algorithms are applied to the estimation of model parameters, results are quite different. For this task, the gradient (quasi-Newton) and the nature inspired method (CMAES) can be efficiently combined, achieving the best optimization performance.
  • Estimation of continuous parameters is frequent task in modelling and simulation. There are several general purpose algorithms available for this task. We benchmarked these algorithms in order to recommend an appropriate algorithm for our model identification problem. We present results of optimization algorithms for standard benchmarking functions and show the importance of proper parameter setting. When these algorithms are applied to the estimation of model parameters, results are quite different. For this task, the gradient (quasi-Newton) and the nature inspired method (CMAES) can be efficiently combined, achieving the best optimization performance. (en)
Title
  • Continuous Optimization Algorithms: Performance on Benchmarking Functions and Model Parameters
  • Continuous Optimization Algorithms: Performance on Benchmarking Functions and Model Parameters (en)
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  • Continuous Optimization Algorithms: Performance on Benchmarking Functions and Model Parameters
  • Continuous Optimization Algorithms: Performance on Benchmarking Functions and Model Parameters (en)
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  • RIV/68407700:21240/10:00171939!RIV11-MSM-21240___
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  • 251986
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  • RIV/68407700:21240/10:00171939
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  • Continuous Optimization, Neural Network, Modelling, Parameter Estimation (en)
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  • [88F4944A1AA5]
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  • Praha
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  • Prague
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  • Proceedings of the 7th EUROSIM Congress on Modelling and Simulation, Vol. 2: Full Papers
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  • Bičík, Vladimír
  • Kordík, Pavel
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  • Department of Computer Science and Engineering, FEE, CTU in Prague
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  • 978-80-01-04589-3
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  • 21240
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