The book describes the basic components of an optimization problem along with the formulation of design problems as mathematical programming problems using an objective function that expresses the main aim of the model, and how it is to be either minimized or maximized; subsequently, the concept of optimization and its relevance towards an optimal solution in engineering applications, is explained.

This book aims to present some of the recent developments in the area of optimization theory, methods, and applications in engineering. It focuses on the metaphor of the inspired system and how to configure and apply the various algorithms. The book comprises 30 chapters and is organized into two parts: Part I — Soft Computing and Evolutionary-Based Optimization; and Part II — Decision Science and Simulation-Based Optimization, which contains application-based chapters.

Readers and users will find in the book:

• An overview and brief background of optimization methods which are used very popularly in almost all applications of science, engineering, technology, and mathematics;
• An in-depth treatment of contributions to optimal learning and optimizing engineering systems;
• Maps out the relations between optimization and other mathematical topics and disciplines;
• A problem-solving approach and a large number of illustrative examples, leading to a step-by-step formulation and solving of optimization problems.

Audience

Researchers, industry professionals, academicians, and doctoral scholars in major domains of engineering, production, thermal, electrical, industrial, materials, design, computer engineering, and natural sciences. The book is also suitable for researchers and postgraduate students in mathematics, applied mathematics, and industrial mathematics.

The book describes the basic components of an optimization problem along with the formulation of design problems as mathematical programming problems using an objective function that expresses the main aim of the model, and how it is to be either minimized or maximized; subsequently, the concept of optimization and its relevance towards an optimal solution in engineering applications, is explained.

This book aims to present some of the recent developments in the area of optimization theory, methods, and applications in engineering. It focuses on the metaphor of the inspired system and how to configure and apply the various algorithms. The book comprises 30 chapters and is organized into two parts: Part I — Soft Computing and Evolutionary-Based Optimization; and Part II — Decision Science and Simulation-Based Optimization, which contains application-based chapters.

Readers and users will find in the book:

• An overview and brief background of optimization methods which are used very popularly in almost all applications of science, engineering, technology, and mathematics;
• An in-depth treatment of contributions to optimal learning and optimizing engineering systems;
• Maps out the relations between optimization and other mathematical topics and disciplines;
• A problem-solving approach and a large number of illustrative examples, leading to a step-by-step formulation and solving of optimization problems.

Audience

Researchers, industry professionals, academicians, and doctoral scholars in major domains of engineering, production, thermal, electrical, industrial, materials, design, computer engineering, and natural sciences. The book is also suitable for researchers and postgraduate students in mathematics, applied mathematics, and industrial mathematics.

The book describes the basic components of an optimization problem along with the formulation of design problems as mathematical programming problems using an objective function that expresses the main aim of the model, and how it is to be either minimized or maximized; subsequently, the concept of optimization and its relevance towards an optimal solution in engineering applications, is explained.

This book aims to present some of the recent developments in the area of optimization theory, methods, and applications in engineering. It focuses on the metaphor of the inspired system and how to configure and apply the various algorithms. The book comprises 30 chapters and is organized into two parts: Part I — Soft Computing and Evolutionary-Based Optimization; and Part II — Decision Science and Simulation-Based Optimization, which contains application-based chapters.

Readers and users will find in the book:

• An overview and brief background of optimization methods which are used very popularly in almost all applications of science, engineering, technology, and mathematics;
• An in-depth treatment of contributions to optimal learning and optimizing engineering systems;
• Maps out the relations between optimization and other mathematical topics and disciplines;
• A problem-solving approach and a large number of illustrative examples, leading to a step-by-step formulation and solving of optimization problems.

Audience

Researchers, industry professionals, academicians, and doctoral scholars in major domains of engineering, production, thermal, electrical, industrial, materials, design, computer engineering, and natural sciences. The book is also suitable for researchers and postgraduate students in mathematics, applied mathematics, and industrial mathematics.