Average Reviews:
(More customer reviews)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.
Click Here to see more reviews about: Metaheuristics: From Design to Implementation (Wiley Series on Parallel and Distributed Computing)
A unified view of metaheuristics
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.
