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
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.
Chapter 1 of Meta-heuristic and Evolutionary Algorithms for Engineering
Optimization 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:
- Introduces state-of-the-art metaheuristic algorithms and their
applications to engineering optimization;
- Fills a gap in the current literature by compiling and explaining the
various meta-heuristic and evolutionary algorithms in a clear and
systematic manner;
- Provides a step-by-step presentation of each algorithm and guidelines
for practical implementation and coding of algorithms;
- Discusses and assesses the performance of metaheuristic algorithms in
multiple problems from many fields of engineering;
- Relates optimization algorithms to engineering problems employing a
unifying approach.
Meta-heuristic and Evolutionary Algorithms for Engineering
Optimization 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.
OMID BOZORG-HADDAD, PhD, is Professor in the Department of
Irrigation and Reclamation Engineering at the University of Tehran,
Iran.
MOHAMMAD SOLGI, M.Sc., is Teacher Assistant for M.Sc. courses at the
University of Tehran, Iran.
HUGO A. LOÁICIGA, PhD, is Professor in the Department of Geography
at the University of California, Santa Barbara, United States of
America.
