Nonlinear Multi-Modal Optimization: Theory, Algorithm, and Design Applications

 
9783843374019: Nonlinear Multi-Modal Optimization: Theory, Algorithm, and Design Applications
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This book introduces a new optimization algorithm for simulation-based design of systems with multi-modal, nonlinear, black box objective functions. The algorithm extends the recently introduced adaptive multi-modal optimization by incorporating surrogate modeling features similar to response surface methods (RSM). The resulting optimization algorithm has reduced computational intensity and is therefore well-suited for optimization of expensive black box objective functions. The algorithm produces progressively accurate surrogate models around the local minima which can be used for post-optimization studies such as sensitivity and tolerance analyses with minimal computational effort. This algorithm is suitable for optimal design of complex engineering systems and enhances the design cycle by enabling computationally affordable uncertainty analysis. The mathematical basis of the algorithm is explained in detail. The book also demonstrates the effectiveness of the algorithm using comparative optimization of several multi-modal objective functions. It also shows several practical applications of the algorithm in the design of complex power and power-electronic systems.

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About the Author:

Farhad Yahyaie is an NSERC-IPS scholar at the University of Manitoba. His areas of interest include application of power electronics in power systems and nonlinear optimization. Shaahin Filizadeh is an Associate Professor of Electrical and Computer Engineering at the University of Manitoba. His interests include power electronics and optimization.

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Book Description Condition: New. Publisher/Verlag: LAP Lambert Academic Publishing | Theory, Algorithm, and Design Applications | This book introduces a new optimization algorithm for simulation-based design of systems with multi-modal, nonlinear, black box objective functions. The algorithm extends the recently introduced adaptive multi-modal optimization by incorporating surrogate modeling features similar to response surface methods (RSM). The resulting optimization algorithm has reduced computational intensity and is therefore well-suited for optimization of expensive black box objective functions. The algorithm produces progressively accurate surrogate models around the local minima which can be used for post-optimization studies such as sensitivity and tolerance analyses with minimal computational effort. This algorithm is suitable for optimal design of complex engineering systems and enhances the design cycle by enabling computationally affordable uncertainty analysis. The mathematical basis of the algorithm is explained in detail. The book also demonstrates the effectiveness of the algorithm using comparative optimization of several multi-modal objective functions. It also shows several practical applications of the algorithm in the design of complex power and power-electronic systems. | Format: Paperback | Language/Sprache: english | 180 gr | 124 pp. Seller Inventory # K9783843374019

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Book Description LAP Lambert Acad. Publ. Nov 2010, 2010. Taschenbuch. Condition: Neu. Neuware - This book introduces a new optimization algorithm for simulation-based design of systems with multi-modal, nonlinear, black box objective functions. The algorithm extends the recently introduced adaptive multi-modal optimization by incorporating surrogate modeling features similar to response surface methods (RSM). The resulting optimization algorithm has reduced computational intensity and is therefore well-suited for optimization of expensive black box objective functions. The algorithm produces progressively accurate surrogate models around the local minima which can be used for post-optimization studies such as sensitivity and tolerance analyses with minimal computational effort. This algorithm is suitable for optimal design of complex engineering systems and enhances the design cycle by enabling computationally affordable uncertainty analysis. The mathematical basis of the algorithm is explained in detail. The book also demonstrates the effectiveness of the algorithm using comparative optimization of several multi-modal objective functions. It also shows several practical applications of the algorithm in the design of complex power and power-electronic systems. 124 pp. Englisch. Seller Inventory # 9783843374019

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Book Description LAP Lambert Acad. Publ. Nov 2010, 2010. Taschenbuch. Condition: Neu. Neuware - This book introduces a new optimization algorithm for simulation-based design of systems with multi-modal, nonlinear, black box objective functions. The algorithm extends the recently introduced adaptive multi-modal optimization by incorporating surrogate modeling features similar to response surface methods (RSM). The resulting optimization algorithm has reduced computational intensity and is therefore well-suited for optimization of expensive black box objective functions. The algorithm produces progressively accurate surrogate models around the local minima which can be used for post-optimization studies such as sensitivity and tolerance analyses with minimal computational effort. This algorithm is suitable for optimal design of complex engineering systems and enhances the design cycle by enabling computationally affordable uncertainty analysis. The mathematical basis of the algorithm is explained in detail. The book also demonstrates the effectiveness of the algorithm using comparative optimization of several multi-modal objective functions. It also shows several practical applications of the algorithm in the design of complex power and power-electronic systems. 124 pp. Englisch. Seller Inventory # 9783843374019

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Book Description LAP Lambert Acad. Publ. Nov 2010, 2010. Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Neuware - This book introduces a new optimization algorithm for simulation-based design of systems with multi-modal, nonlinear, black box objective functions. The algorithm extends the recently introduced adaptive multi-modal optimization by incorporating surrogate modeling features similar to response surface methods (RSM). The resulting optimization algorithm has reduced computational intensity and is therefore well-suited for optimization of expensive black box objective functions. The algorithm produces progressively accurate surrogate models around the local minima which can be used for post-optimization studies such as sensitivity and tolerance analyses with minimal computational effort. This algorithm is suitable for optimal design of complex engineering systems and enhances the design cycle by enabling computationally affordable uncertainty analysis. The mathematical basis of the algorithm is explained in detail. The book also demonstrates the effectiveness of the algorithm using comparative optimization of several multi-modal objective functions. It also shows several practical applications of the algorithm in the design of complex power and power-electronic systems. 124 pp. Englisch. Seller Inventory # 9783843374019

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Book Description LAP Lambert Academic Publishing, Germany, 2010. Paperback. Condition: New. Language: English . Brand New Book. This book introduces a new optimization algorithm for simulation-based design of systems with multi-modal, nonlinear, black box objective functions. The algorithm extends the recently introduced adaptive multi-modal optimization by incorporating surrogate modeling features similar to response surface methods (RSM). The resulting optimization algorithm has reduced computational intensity and is therefore well-suited for optimization of expensive black box objective functions. The algorithm produces progressively accurate surrogate models around the local minima which can be used for post-optimization studies such as sensitivity and tolerance analyses with minimal computational effort. This algorithm is suitable for optimal design of complex engineering systems and enhances the design cycle by enabling computationally affordable uncertainty analysis. The mathematical basis of the algorithm is explained in detail. The book also demonstrates the effectiveness of the algorithm using comparative optimization of several multi-modal objective functions. It also shows several practical applications of the algorithm in the design of complex power and power-electronic systems. Seller Inventory # KNV9783843374019

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Book Description LAP Lambert Academic Publishing. Paperback. Condition: New. 124 pages. Dimensions: 8.7in. x 5.9in. x 0.3in.This book introduces a new optimization algorithm for simulation-based design of systems with multi-modal, nonlinear, black box objective functions. The algorithm extends the recently introduced adaptive multi-modal optimization by incorporating surrogate modeling features similar to response surface methods (RSM). The resulting optimization algorithm has reduced computational intensity and is therefore well-suited for optimization of expensive black box objective functions. The algorithm produces progressively accurate surrogate models around the local minima which can be used for post-optimization studies such as sensitivity and tolerance analyses with minimal computational effort. This algorithm is suitable for optimal design of complex engineering systems and enhances the design cycle by enabling computationally affordable uncertainty analysis. The mathematical basis of the algorithm is explained in detail. The book also demonstrates the effectiveness of the algorithm using comparative optimization of several multi-modal objective functions. It also shows several practical applications of the algorithm in the design of complex power and power-electronic systems. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Seller Inventory # 9783843374019

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