Meta-heuristic and evolutionary algorithms for engineering optimization / Omid Bozorg-Haddad, Mohammad Solgi, Hugo A. Loaiciga.

Format:

Book

Author/Creator:

Bozorg-Haddad, Omid, 1974- author.

Solgi, Mohammad, author.

Loaiciga, Hugo A., author.

Series:

Wiley series in operations research and management science.

Wiley Series in Operations Research and Management Science

Language:

English

Subjects (All):

Mathematical optimization.

Engineering design--Mathematics.

Engineering design.

Physical Description:

1 online resource (281 pages) : illustrations.

Edition:

1st edition

Place of Publication:

Hoboken, New Jersey : Wiley, 2017.

System Details:

text file

Summary:

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 Irriga...

Contents:

Overview of optimization

Introduction to meta-heuristic and evolutionary algorithms

Pattern search

Genetic algorithm

Simulated annealing

Tabu search

Ant colony optimization

Particle swarm optimization

Differential evolution

Harmony search

Shuffled frog-leaping algorithm

Honey-bee mating optimization

Invasive weed optimization

Central force optimization

Biogeography-based optimization

Firefly algorithm

Gravity search algorithm

Bat algorithm

Plant propagation algorithm

Water cycle algorithm

Symbiotic organisms search

Comprehensive evolutionary algorithm.

Notes:

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781119387060

111938706X

9781119387077

1119387078

9781119387053

1119387051

OCLC:

988749666