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Metaheuristics: From Design to Implementation

4.3 out of 5 stars 3 customer reviews
ISBN-13: 978-0470278581
ISBN-10: 0470278587
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Editorial Reviews


“In conclusion, I found reading Metaheuristics: From Design to Implementation to be pleasant and enjoyable. I particularly recommend it as a reference for researchers and students of computer science or operations research who want a global outlook of metaheuristics methods. It would also be extremely useful for introducing graduate and PhD students who are new to the field of heuristics and metaheuristics to the amazing world of the designing of these procedures.”  (Informs, 1 July 2012)

"It will be an indispensable text for advanced undergraduate and graduate students in computer science, operations research, applied mathematics, control, business and management and engineering." (Zentralblatt MATH, 2010)


From the Back Cover


This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code.

Throughout the book, the key search components of metaheuristics are considered as a toolbox for:

  • Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems
  • Designing efficient metaheuristics for multi-objective optimization problems
  • Designing hybrid, parallel, and distributed metaheuristics
  • Implementing metaheuristics on sequential and parallel machines

Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.


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Product Details

  • Hardcover: 624 pages
  • Publisher: Wiley (June 22, 2009)
  • Language: English
  • ISBN-10: 0470278587
  • ISBN-13: 978-0470278581
  • Product Dimensions: 6.5 x 1.3 x 9.2 inches
  • Shipping Weight: 2.2 pounds (View shipping rates and policies)
  • Average Customer Review: 4.3 out of 5 stars  See all reviews (3 customer reviews)
  • Amazon Best Sellers Rank: #132,010 in Books (See Top 100 in Books)

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Top Customer Reviews

Format: Hardcover Verified Purchase
This book provides an amazingly thorough overview of its subject. It describes most algorithms in detail. If you are trying to solve a specific optimization problem, this book is a great place to start before diving into research papers that might be more specific to your domain. Overall, I highly recommend the book.

As someone who was reading it with a specific purpose in mind, I think the book has two minor issues. First, it is sometimes needlessly academic. It occasionally expresses concepts which are quite simple in unnecessarily mathematical and confusing terms.

Second, it doesn't always do a good job of explaining why you would choose a particular class of algorithms for a particular problem domain. For many algorithms in the book, it contains a one sentence list of the kinds of problems the algorithm is used for, but it often doesn't discuss why the algorithm fits a particular problem domain well, or how you would decide exactly which algorithm to use. To some extent, I think this is a problem with metaheuristics in general (they are kind of like "if you knew a clever way to guess better and better solutions, you'll do well, but its really hard to guess better and better solutions consistently, so here are lots of different ways to do it, but who knows which way will work." In fact, the book often suggests 'self-tuning' algorithms, which are basically algorithms that 'guess how to guess.' Its all very meta. Or maybe meta-meta.)

Those minor criticisms aside, I doubt you will find a more thorough overview of the topic. The book is readable, and if this is an area you need to learn about in detail, I highly recommend it.
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Format: Hardcover
The book is a good and detailed description of what metaheuristics involves. This is applied to solving hard computational problems. There are summaries of many methods developed over the last 50 years. The simplex method. Metropolis Monte Carlo. Simulated annealing. Genetic algorithms. And others. There is deliberately not enough information about most of these for you to use them given only the book as a starting point. Space considerations.

But mostly the book explains at a higher level, how methods can be understood. Some are for exploiting; ie. intensively looking in a given region of the objective space around a starting point. Simulated annealing is a good example of such a method.

Other methods are for exploring. A broader search in the objective or solution space. Genetic algorithms, with their mutations and crossover recombinations are very strong here, using ideas borrowed from biological evolution.

More importantly, the book shows how many hard problems have to be tackled by a combination of exploring and exploiting. The combining of algorithms is what gives metaheuristics its name.

One caveat is that even at a summary level, the description of tabu search was a bit unclear, compared to the excellent synopses of simulated annealing and genetic algorithms.
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Format: Hardcover Verified Purchase
Excellent book for people who are learning from the beginning
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