Engineering Design Optimization

Based on course-tested material, this rigorous yet accessible graduate textbook covers both fundamental and advanced optimization theory and algorithms. It covers a wide range of numerical methods and topics, including both gradient-based and gradient-free algorithms, multidisciplinary design optimization, and uncertainty, with instruction on how to determine which algorithm should be used for a given application. It also provides an overview of models and how to prepare them for use with numerical optimization, including derivative computation. Over 400 high-quality visualizations and numerous examples facilitate understanding of the theory, and practical tips address common issues encountered in practical engineering design optimization and how to address them. Numerous end-of-chapter homework problems, progressing in difficulty, help put knowledge into practice. Accompanied online by a solutions manual for instructors and source code for problems, this is ideal for a one- or two-semester graduate course on optimization in aerospace, civil, mechanical, electrical, and chemical engineering departments.

• Hands on and applied applications related to aerospace, civil, mechanical, electrical, and chemical engineering.
• Multidisciplinary approach.
• Discusses the OpenMDAO framework an open-source high-performance computing platform for efficient optimization.
• Covers a wide range of numerical methods and topics, including both gradient-based and gradient-free algorithms, multidisciplinary design optimization, and uncertainty.
• Includes over 400 high-quality visualizations and numerous examples.
• Provides numerous end-of-chapter homework problems, progressing from easier problems through to open-ended problems, with a solutions manual online for instructors.