Among all aspects of engineering, design is the most important step in developing a new product. A systematic approach to managing design issues can only be accomplished by applying mathematical optimization methods. Furthermore, due to the practical issues in engineering problems, there are limitations in using traditional methods. As such, stochastic optimization methods such as differential evolution, simulated annealing, and genetic algorithms are preferable in finding solutions in design optimization problems. This book reviews mechanical engineering design optimization using stochastic methods. It introduces students and design engineers to practical aspects of complicated mathematical optimization procedures, and outlines steps for wide range of selected engineering design problems. It shows how engineering structures are systematically designed. Many new engineering design applications based on stochastic optimization techniques in automotive, energy, military, naval, manufacturing process and fluids-heat transfer, are described in the book. For each design optimization problem described, background is provided for understanding the solutions. There are very few books on optimization that include engineering applications. They cover limited applications, and that too of well-known design problems of advanced and niche nature. Common problems are hardly addressed. Thus, the subject has remained fairly theoretical. To overcome this, each chapter in this book is contributed by at least one academic and one industrial expert researcher.

Among all aspects of engineering, design is the most important step in developing a new product. A systematic approach to managing design issues can only be accomplished by applying mathematical optimization methods. Furthermore, due to the practical issues in engineering problems, there are limitations in using traditional methods. As such, stochastic optimization methods such as differential evolution, simulated annealing, and genetic algorithms are preferable in finding solutions in design optimization problems. This book reviews mechanical engineering design optimization using stochastic methods. It introduces students and design engineers to practical aspects of complicated mathematical optimization procedures, and outlines steps for wide range of selected engineering design problems. It shows how engineering structures are systematically designed. Many new engineering design applications based on stochastic optimization techniques in automotive, energy, military, naval, manufacturing process and fluids-heat transfer, are described in the book. For each design optimization problem described, background is provided for understanding the solutions. There are very few books on optimization that include engineering applications. They cover limited applications, and that too of well-known design problems of advanced and niche nature. Common problems are hardly addressed. Thus, the subject has remained fairly theoretical. To overcome this, each chapter in this book is contributed by at least one academic and one industrial expert researcher.

This contributed book focuses on optimization methods inspired by nature such as Harmony Search Algorithm, Drosophila Food-Search Algorithm, Cohort intelligence algorithm and its variations, fuzzy logic along with their hybridization variants. It also focuses on multi-objective optimization algorithms such as Non-Dominated Sorting Genetic Algorithm, Particle Swarm Optimization, Evolutionary Algorithm, Pareto Envelope Selection Algorithm, and Strength Pareto Evolutionary Algorithm. The content focuses on topics such as the optimal design of truss systems with various applications, the design and simulation of quarter car systems for comfort design, the road handling design and a balanced system, and topology optimization of 2-dimensional and 3-dimensional structure in linear elasticity, plasticity and fracture mechanics among others. This book is a useful reference for those in academia and industry.

This book examines optimization problems that in practice involve random model parameters. It details the computation of robust optimal solutions, i.e., optimal solutions that are insensitive with respect to random parameter variations, where appropriate deterministic substitute problems are needed. Based on the probability distribution of the random data and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into appropriate deterministic substitute problems. Due to the probabilities and expectations involved, the book also shows how to apply approximative solution techniques. Several deterministic and stochastic approximation methods are provided: Taylor expansion methods, regression and response surface methods (RSM), probability inequalities, multiple linearization of survival/failure domains, discretization methods, convex approximation/deterministic descent directions/efficient points, stochastic approximation and gradient procedures and differentiation formulas for probabilities and expectations. In the third edition, this book further develops stochastic optimization methods. In particular, it now shows how to apply stochastic optimization methods to the approximate solution of important concrete problems arising in engineering, economics and operations research.

Throughout the past few years, there has been extensive research done on structural design in terms of optimization methods or problem formulation. But, much of this attention has been on the linear elastic structural behavior, under static loading condition. Such a focus has left researchers scratching their heads as it has led to vulnerable structural configurations. What researchers have left out of the equation is the element of seismic loading. It is essential for researchers to take this into account in order to develop earthquake resistant real-world structures. Structural Seismic Design Optimization and Earthquake Engineering: Formulations and Applications focuses on the research around earthquake engineering, in particular, the field of implementation of optimization algorithms in earthquake engineering problems. Topics discussed within this book include, but are not limited to, simulation issues for the accurate prediction of the seismic response of structures, design optimization procedures, soft computing applications, and other important advancements in seismic analysis and design where optimization algorithms can be implemented. Readers will discover that this book provides relevant theoretical frameworks in order to enhance their learning on earthquake engineering as it deals with the latest research findings and their practical implementations, as well as new formulations and solutions.

Computational optimization methods have matured over the last few years due to extensive research by applied mathematicians and engineers. These methods have been applied to many practical applications. Several general-purpose optimization programs and programs for specific engineering applications have become available to solve particular optimization problems.Written by leading researchers in the field of optimization, this highly readable book covers state-of-the-art computational algorithms as well as applications of optimization to structural and mechanical systems. Formulations of the problems and numerical solutions are presented, and topics requiring further research are also suggested.

Optimization methods are perceived to be at the heart of computer methods for designing engineering systems. With these optimization methods, the designer can evaluate more alternatives, resulting in a better and more cost-effective design. This guide describes the use of modern optimization methods with simple yet meaningful structural design examples. Optimum solutions are obtained and, where possible, compared with the solutions obtained using traditional design procedures.