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Engineering Optimization

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  • Author(s)Xin-She Yang
  • SubtitleAn Introduction with Metaheuristic Applications
  • Edition1
  • Published17th June 2010
  • PublisherJohn Wiley & Sons Inc (US)
  • ISBN9780470582466

An Introduction with Metaheuristic Applications

An accessible introduction to metaheuristics and optimization,
featuring powerful and modern algorithms for application across
engineering and the sciences

From engineering and computer science to economics and
management science, optimization is a core component for problem
solving. Highlighting the latest developments that have evolved in
recent years, Engineering Optimization: An Introduction with
Metaheuristic Applications
outlines popular metaheuristic
algorithms and equips readers with the skills needed to apply these
techniques to their own optimization problems. With insightful
examples from various fields of study, the author highlights key
concepts and techniques for the successful application of
commonly-used metaheuristc algorithms, including simulated
annealing, particle swarm optimization, harmony search, and genetic

The author introduces all major metaheuristic algorithms and
their applications in optimization through a presentation that is
organized into three succinct parts:

  • Foundations of Optimization and Algorithms
    provides a brief introduction to the underlying nature of
    optimization and the common approaches to optimization problems,
    random number generation, the Monte Carlo method, and the Markov
    chain Monte Carlo method

  • Metaheuristic Algorithms presents common
    metaheuristic algorithms in detail, including genetic algorithms,
    simulated annealing, ant algorithms, bee algorithms, particle swarm
    optimization, firefly algorithms, and harmony search

  • Applications outlines a wide range of
    applications that use metaheuristic algorithms to solve challenging
    optimization problems with detailed implementation while also
    introducing various modifications used for multi-objective

Throughout the book, the author presents worked-out examples and
real-world applications that illustrate the modern relevance of the
topic. A detailed appendix features important and popular
algorithms using MATLAB® and Octave software packages, and a
related FTP site houses MATLAB code and programs for easy
implementation of the discussed techniques. In addition, references
to the current literature enable readers to investigate individual
algorithms and methods in greater detail.

Engineering Optimization: An Introduction with Metaheuristic
is an excellent book for courses on optimization
and computer simulation at the upper-undergraduate and graduate
levels. It is also a valuable reference for researchers and
practitioners working in the fields of mathematics, engineering,
computer science, operations research, and management science who
use metaheuristic algorithms to solve problems in their everyday

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