Meta-heuristic and Evolutionary Algorithms for Engineering Optimization

Name: Meta-heuristic and Evolutionary Algorithms for Engineering Optimization
Brand: Wiley
Price: 120.99 EUR
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Omid Bozorg-Haddad Mohammad Solgi Hugo A. Loáiciga(Autor*in)

Wiley (Verlag)

Erschienen am 30. August 2017

304 Seiten

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Beschreibung

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 Irrigation and Reclamation Engineering at the University of Tehran, Iran.
MOHAMMAD SOLGI, M.Sc., is Teacher Assistant for M.Sc. courses at the University of Tehran, Iran.
HUGO A. LOÁICIGA, PhD, is Professor in the Department of Geography at the University of California, Santa Barbara, United States of America.

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Inhalt

Intro
Title Page
Copyright Page
Contents
Preface
About the Authors
List of Figures
Chapter 1 Overview of Optimization
Summary
1.1 Optimization
1.1.1 Objective Function
1.1.2 Decision Variables
1.1.3 Solutions of an Optimization Problem
1.1.4 Decision Space
1.1.5 Constraints or Restrictions
1.1.6 State Variables
1.1.7 Local and Global Optima
1.1.8 Near-Optimal Solutions
1.1.9 Simulation
1.2 Examples of the Formulation of Various Engineering Optimization Problems
1.2.1 Mechanical Design
1.2.2 Structural Design
1.2.3 Electrical Engineering Optimization
1.2.4 Water Resources Optimization
1.2.5 Calibration of Hydrologic Models
1.3 Conclusion
Chapter 2 Introduction to Meta-Heuristic and Evolutionary Algorithms
Summary
2.1 Searching the Decision Space for Optimal Solutions
2.2 Definition of Terms of Meta-Heuristic and Evolutionary Algorithms
2.2.1 Initial State
2.2.2 Iterations
2.2.3 Final State
2.2.4 Initial Data (Information)
2.2.5 Decision Variables
2.2.6 State Variables
2.2.7 Objective Function
2.2.8 Simulation Model
2.2.9 Constraints
2.2.10 Fitness Function
2.3 Principles of Meta-Heuristic and Evolutionary Algorithms
2.4 Classification of Meta-Heuristic and Evolutionary Algorithms
2.4.1 Nature-Inspired and Non-Nature-Inspired Algorithms
2.4.2 Population-Based and Single-Point Search Algorithms
2.4.3 Memory-Based and Memory-Less Algorithms
2.5 Meta-Heuristic and Evolutionary Algorithms in Discrete or Continuous Domains
2.6 Generating Random Values of the Decision Variables
2.7 Dealing with Constraints
2.7.1 Removal Method
2.7.2 Refinement Method
2.7.3 Penalty Functions
2.8 Fitness Function
2.9 Selection of Solutions in Each Iteration
2.10 Generating New Solutions
2.11 The Best Solution in Each Algorithmic Iteration
2.12 Termination Criteria
2.13 General Algorithm
2.14 Performance Evaluation of Meta-Heuristic and Evolutionary Algorithms
2.15 Search Strategies
2.16 Conclusion
References
Chapter 3 Pattern Search
Summary
3.1 Introduction
3.2 Pattern Search (PS) Fundamentals
3.3 Generating an Initial Solution
3.4 Generating Trial Solutions
3.4.1 Exploratory Move
3.4.2 Pattern Move
3.5 Updating the Mesh Size
3.6 Termination Criteria
3.7 User-Defined Parameters of the PS
3.8 Pseudocode of the PS
3.9 Conclusion
References
Chapter 4 Genetic Algorithm
Summary
4.1 Introduction
4.2 Mapping the Genetic Algorithm (GA) to Natural Evolution
4.3 Creating an Initial Population
4.4 Selection of Parents to Create a New Generation
4.4.1 Proportionate Selection
4.4.2 Ranking Selection
4.4.3 Tournament Selection
4.5 Population Diversity and Selective Pressure
4.6 Reproduction
4.6.1 Crossover
4.6.2 Mutation
4.7 Termination Criteria
4.8 User-Defined Parameters of the GA
4.9 Pseudocode of the GA
4.10 Conclusion
References
Chapter 5 Simulated Annealing
Summary
5.1 Introduction
5.2 Mapping the Simulated Annealing (SA) Algorithm to the Physical Annealing Process
5.3 Generating an Initial State
5.4 Generating a New State
5.5 Acceptance Function
5.6 Thermal Equilibrium
5.7 Temperature Reduction
5.8 Termination Criteria
5.9 User-Defined Parameters of the SA
5.10 Pseudocode of the SA
5.11 Conclusion
References
Chapter 6 Tabu Search
Summary
6.1 Introduction
6.2 Tabu Search (TS) Foundation
6.3 Generating an Initial Searching Point
6.4 Neighboring Points
6.5 Tabu Lists
6.6 Updating the Tabu List
6.7 Attributive Memory
6.7.1 Frequency-Based Memory
6.7.2 Recency-Based Memory
6.8 Aspiration Criteria
6.9 Intensification and Diversification Strategies
6.10 Termination Criteria
6.11 User-Defined Parameters of the TS
6.12 Pseudocode of the TS
6.13 Conclusion
References
Chapter 7 Ant Colony Optimization
Summary
7.1 Introduction
7.2 Mapping Ant Colony Optimization (ACO) to Ants' Foraging Behavior
7.3 Creating an Initial Population
7.4 Allocating Pheromone to the Decision Space
7.5 Generation of New Solutions
7.6 Termination Criteria
7.7 User-Defined Parameters of the ACO
7.8 Pseudocode of the ACO
7.9 Conclusion
References
Chapter 8 Particle Swarm Optimization
Summary
8.1 Introduction
8.2 Mapping Particle Swarm Optimization (PSO) to the Social Behavior of Some Animals
8.3 Creating an Initial Population of Particles
8.4 The Individual and Global Best Positions
8.5 Velocities of Particles
8.6 Updating the Positions of Particles
8.7 Termination Criteria
8.8 User-Defined Parameters of the PSO
8.9 Pseudocode of the PSO
8.10 Conclusion
References
Chapter 9 Differential Evolution
Summary
9.1 Introduction
9.2 Differential Evolution (DE) Fundamentals
9.3 Creating an Initial Population
9.4 Generating Trial Solutions
9.4.1 Mutation
9.4.2 Crossover
9.5 Greedy Criteria
9.6 Termination Criteria
9.7 User-Defined Parameters of the DE
9.8 Pseudocode of the DE
9.9 Conclusion
References
Chapter 10 Harmony Search
Summary
10.1 Introduction
10.2 Inspiration of the Harmony Search (HS)
10.3 Initializing the Harmony Memory
10.4 Generating New Harmonies (Solutions)
10.4.1 Memory Strategy
10.4.2 Random Selection
10.4.3 Pitch Adjustment
10.5 Updating the Harmony Memory
10.6 Termination Criteria
10.7 User-Defined Parameters of the HS
10.8 Pseudocode of the HS
10.9 Conclusion
References
Chapter 11 Shuffled Frog-Leaping Algorithm
Summary
11.1 Introduction
11.2 Mapping Memetic Evolution of Frogs to the Shuffled Frog Leaping Algorithm (SFLA)
11.3 Creating an Initial Population
11.4 Classifying Frogs into Memeplexes
11.5 Frog Leaping
11.6 Shuffling Process
11.7 Termination Criteria
11.8 User-Defined Parameters of the SFLA
11.9 Pseudocode of the SFLA
11.10 Conclusion
References
Chapter 12 Honey-Bee Mating Optimization
Summary
12.1 Introduction
12.2 Mapping Honey-Bee Mating Optimization (HBMO) to the Honey-Bee Colony Structure
12.3 Creating an Initial Population
12.4 The Queen
12.5 Drone Selection
12.5.1 Mating Flights
12.5.2 Trial Solutions
12.6 Brood (New Solution) Production
12.7 Improving Broods (New Solutions) by Workers
12.8 Termination Criteria
12.9 User-Defined Parameters of the HBMO
12.10 Pseudocode of the HBMO
12.11 Conclusion
References
Chapter 13 Invasive Weed Optimization
Summary
13.1 Introduction
13.2 Mapping Invasive Weed Optimization (IWO) to Weeds' Biology
13.3 Creating an Initial Population
13.4 Reproduction
13.5 The Spread of Seeds
13.6 Eliminating Weeds with Low Fitness
13.7 Termination Criteria
13.8 User-Defined Parameters of the IWO
13.9 Pseudocode of the IWO
13.10 Conclusion
References
Chapter 14 Central Force Optimization
Summary
14.1 Introduction
14.2 Mapping Central Force Optimization (CFO) to Newton's Gravitational Law
14.3 Initializing the Position of Probes
14.4 Calculation of Accelerations
14.5 Movement of Probes
14.6 Modification of Deviated Probes
14.7 Termination Criteria
14.8 User-Defined Parameters of the CFO
14.9 Pseudocode of the CFO
14.10 Conclusion
References
Chapter 15 Biogeography-Based Optimization
Summary
15.1 Introduction
15.2 Mapping Biogeography-Based Optimization (BBO) to Biogeography Concepts
15.3 Creating an Initial Population
15.4 Migration Process
15.5 Mutation
15.6 Termination Criteria
15.7 User-Defined Parameters of the BBO
15.8 Pseudocode of the BBO
15.9 Conclusion
References
Chapter 16 Firefly Algorithm
Summary
16.1 Introduction
16.2 Mapping the Firefly Algorithm (FA) to the Flashing Characteristics of Fireflies
16.3 Creating an Initial Population
16.4 Attractiveness
16.5 Distance and Movement
16.6 Termination Criteria
16.7 User-Defined Parameters of the FA
16.8 Pseudocode of the FA
16.9 Conclusion
References
Chapter 17 Gravity Search Algorithm
Summary
17.1 Introduction
17.2 Mapping the Gravity Search Algorithm (GSA) to the Law of Gravity
17.3 Creating an Initial Population
17.4 Evaluation of Particle Masses
17.5 Updating Velocities and Positions
17.6 Updating Newton's Gravitational Factor
17.7 Termination Criteria
17.8 User-Defined Parameters of the GSA
17.9 Pseudocode of the GSA
17.10 Conclusion
References
Chapter 18 Bat Algorithm
Summary
18.1 Introduction
18.2 Mapping the Bat Algorithm (BA) to the Behavior of Microbats
18.3 Creating an Initial Population
18.4 Movement of Virtual Bats
18.5 Local Search and Random Flying
18.6 Loudness and Pulse Emission
18.7 Termination Criteria
18.8 User-Defined Parameters of the BA
18.9 Pseudocode of the BA
18.10 Conclusion
References
Chapter 19 Plant Propagation Algorithm
Summary
19.1 Introduction
19.2 Mapping the Natural Process to the Planet Propagation Algorithm (PPA)
19.3 Creating an Initial Population of Plants
19.4 Normalizing the Fitness Function
19.5 Propagation
19.6 Elimination of Extra Solutions
19.7 Termination Criteria
19.8 User-Defined Parameters of the PPA
19.9 Pseudocode of the PPA
19.10 Conclusion
References
Chapter 20 Water Cycle Algorithm
Summary
20.1 Introduction
20.2 Mapping the Water Cycle Algorithm (WCA) to the Water Cycle
20.3 Creating an Initial Population
20.4 Classification of Raindrops
20.5 Streams Flowing to the Rivers or Sea
20.6 Evaporation
20.7 Raining Process
20.8 Termination Criteria
20.9 User-Defined Parameters of the WCA
20.10 Pseudocode of the WCA
20.11 Conclusion
References
Chapter 21 Symbiotic Organisms Search
Summary
21.1 Introduction
21.2 Mapping Symbiotic Relations to the Symbiotic Organisms Search (SOS)
21.3 Creating an Initial Ecosystem
21.4 Mutualism
21.5 Commensalism
21.6 Parasitism
21.7 Termination Criteria
21.8 Pseudocode of the SOS
21.9 Conclusion
References
Chapter 22 Comprehensive Evolutionary Algorithm
Summary
22.1 Introduction
22.2 Fundamentals of the Comprehensive Evolutionary Algorithm (CEA)
22.3 Generating an Initial Population of Solutions
22.4 Selection
22.5 Reproduction
22.5.1 Crossover Operators
22.5.2 Mutation Operators
22.6 Roles of Operators
22.7 Input Data to the CEA
22.8 Termination Criteria
22.9 Pseudocode of the CEA
22.10 Conclusion
References
Wiley Series in Operations Research and Management Science
Index
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