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Metaheuristic Computation with MATLAB®

Erik Cuevas | Alma Rodriguez
€62.99
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A01=Alma Rodriguez
A01=Erik Cuevas
Acceptance Probability Function
algorithmic problem solving
Author_Alma Rodriguez
Author_Erik Cuevas
Candidate Solutions
Category=UMB
Category=UYQ
computational optimisation
Crossover Operation
CS
CS Algorithm
Depuration Procedure
Elitist Selection Strategy
End End
End End End
eq_bestseller
eq_computing
eq_isMigrated=1
eq_nobargain
eq_non-fiction
Evolution Strategies Algorithm
evolutionary algorithms
Firefly Algorithm
Fitness Value
IEEE Congress
Metaheuristic Algorithm
Metaheuristic Method
Metaheuristic Schemes
multimodal optimisation techniques
Multimodal Optimization
Multimodal Optimization Problem
Mutant Vector
nature inspired computation
Objective Function Figure
Onlooker Bee Phase
Onlooker Bees
Penalty Multiplier
PSO Algorithm
Search Space
swarm intelligence
undergraduate engineering textbook

Product details

  • ISBN 9780367523800
  • Weight: 453g
  • Dimensions: 178 x 254mm
  • Publication Date: 06 May 2022
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Description

Metaheuristic algorithms are considered as generic optimization tools that can solve very complex problems characterized by having very large search spaces. Metaheuristic methods reduce the effective size of the search space through the use of effective search strategies.

Book Features:

  • Provides a unified view of the most popular metaheuristic methods currently in use

  • Includes the necessary concepts to enable readers to implement and modify already known metaheuristic methods to solve problems

  • Covers design aspects and implementation in MATLAB®

  • Contains numerous examples of problems and solutions that demonstrate the power of these methods of optimization

The material has been written from a teaching perspective and, for this reason, this book is primarily intended for undergraduate and postgraduate students of artificial intelligence, metaheuristic methods, and/or evolutionary computation. The objective is to bridge the gap between metaheuristic techniques and complex optimization problems that profit from the convenient properties of metaheuristic approaches. Therefore, engineer practitioners who are not familiar with metaheuristic computation will appreciate that the techniques discussed are beyond simple theoretical tools, since they have been adapted to solve significant problems that commonly arise in such areas.

About Alma Rodriguez | Erik Cuevas

Erik Cuevas is a professor in the Department of Electronics at the University of Guadalajara, Mexico.

Alma Rodríguez is a PhD candidate in electronics and computer science at the University of Guadalajara, Mexico.

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