Meta-heuristic and Evolutionary Algorithms for Engineering Optimization

<p><b>A detailed review of a wide range of meta-heuristic and evolutionary algorithms in a systematic manner and how they relate to engineering optimization problems</b></p> <p>This book introduces the main metaheuristic algorithms and their applications in optimization. It describes 20 leading meta-heuristic and evolutionary algorithms and presents discussions and assessments of their performance in solving optimization problems from several fields of engineering. The book features clear and concise principles and presents detailed descriptions of leading methods such as the pattern search (PS) algorithm, the genetic algorithm (GA), the simulated annealing (SA) algorithm, the Tabu search (TS) algorithm, the ant colony optimization (ACO), and the particle swarm optimization (PSO) technique.</p> <p>Chapter 1 of <i>Meta-heuristic and Evolutionary Algorithms for Engineering Optimization </i>provides an overview of optimization and defines it by presenting examples of optimization problems in different engineering domains. Chapter 2 presents an introduction to meta-heuristic and evolutionary algorithms and links them to engineering problems. Chapters 3 to 22 are each devoted to a separate algorithm— and they each start with a brief literature review of the development of the algorithm, and its applications to engineering problems. The principles, steps, and execution of the algorithms are described in detail, and a pseudo code of the algorithm is presented, which serves as a guideline for coding the algorithm to solve specific applications. This book:</p> <ul style=”line-height: 25px; margin-left: 15px; margin-top: 0px; font-family: Arial; font-size: 13.3333px; background-color: #f7f3e7;”> <li>Introduces state-of-the-art metaheuristic algorithms and their applications to engineering optimization;</li> <li>Fills a gap in the current literature by compiling and explaining the various meta-heuristic and evolutionary algorithms in a clear and systematic manner;</li> <li>Provides a step-by-step presentation of each algorithm and guidelines for practical implementation and coding of algorithms;</li> <li>Discusses and assesses the performance of metaheuristic algorithms in multiple problems from many fields of engineering;</li> <li>Relates optimization algorithms to engineering problems employing a unifying approach.</li> </ul> <p><i>Meta-heuristic and Evolutionary Algorithms for Engineering Optimization </i>is a reference intended for students, engineers, researchers, and instructors in the fields of industrial engineering, operations research, optimization/mathematics, engineering optimization, and computer science.</p> <p><b>OMID BOZORG-HADDAD, PhD, </b>is Professor in the Department of Irrigation and Reclamation Engineering at the University of Tehran, Iran.</p> <p><b>MOHAMMAD SOLGI, M.Sc., </b>is Teacher Assistant for M.Sc. courses at the University of Tehran, Iran.</p> <p><b>HUGO A. LOÁICIGA, PhD, </b>is Professor in the Department of Geography at the University of California, Santa Barbara, United States of America.</p>