Recent Metaheuristic Computation Schemes in Engineering (Studies in Computational Intelligence, 948)

✍ Scribed by Erik Cuevas, Alma Rodríguez, Avelina Alejo-Reyes, Carolina Del-Valle-Soto

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 282
• Category: Library

✦ Synopsis

This book includes two objectives. The first goal is to present advances and developments which have proved to be effective in their application to several complex problems. The second objective is to present the performance comparison of various metaheuristic techniques when they face complex optimization problems. The material has been compiled from a teaching perspective. Most of the problems in science, engineering, economics, and other areas can be translated as an optimization or a search problem. According to their characteristics, some problems can be simple that can be solved by traditional optimization methods based on mathematical analysis. However, most of the problems of practical importance in engineering represent complex scenarios so that they are very hard to be solved by using traditional approaches. Under such circumstances, metaheuristic has emerged as the best alternative to solve this kind of complex formulations. This book is primarily intended for undergraduate and postgraduate students. Engineers and application developers can also benefit from the book contents since it has been structured so that each chapter can be read independently from the others, and therefore, only potential interesting information can be quickly available for solving an industrial problem at hand.

✦ Table of Contents

Preface
Contents
1 Introductory Concepts of Metaheuristic Computation
1.1 Formulation of an Optimization Problem
1.2 Classical Optimization Methods
1.3 Metaheuristic Computation Schemes
1.3.1 Generic Structure of a Metaheuristic Method
References
2 A Metaheuristic Scheme Based on the Hunting Model of Yellow Saddle Goatfish
2.1 Introduction
2.2 Yellow Saddle Goatfish Shoal Behavior
2.3 Yellow Saddle Goatfish Algorithm (YSGA)
2.3.1 Initial Population
2.3.2 Chaser Fish
2.3.3 Blocker Fish
2.3.4 Exchange of Roles
2.3.5 Change of Zone
2.3.6 Computational Procedure
2.4 Experimental Results
2.4.1 Results of Unimodal Test Functions
2.4.2 Results of Multimodal Test Functions
2.4.3 Results of Composite Test Functions
2.4.4 Convergence Analysis
2.4.5 Engineering Optimization Problems
2.4.6 Benchmark Functions
2.4.7 Description of Engineering Problems
2.5 Summary
References
3 Metaheuristic Algorithm Based on Hybridization of Invasive Weed Optimization asnd Estimation Distribution Methods
3.1 Introduction
3.2 The Invasive Weed Optimization (IWO) Algorithm
3.2.1 Initialization
3.2.2 Reproduction
3.2.3 Spatial Localization
3.2.4 Competitive Exclusion
3.3 Estimation Distribution Algorithms (EDA)
3.3.1 Initialization
3.3.2 Selection
3.3.3 Model Construction
3.3.4 Individual Production
3.3.5 Truncation
3.4 Mixed Gaussian-Cauchy Distribution
3.4.1 Gaussian Distribution
3.4.2 Cauchy Distribution
3.4.3 Mixed Distribution
3.5 Hybrid Algorithm
3.5.1 Reproduction
3.5.2 Spatial Localization
3.5.3 Model Construction
3.5.4 Individual Generation
3.5.5 Selection of the New Population
3.5.6 Computational Procedure
3.6 Experimental Study
3.6.1 Unimodal Test Functions
3.6.2 Multimodal Test Functions
3.6.3 Composite Test Functions
3.6.4 Benchmark Functions
3.6.5 Convergence Evaluation
3.6.6 Computational Complexity
3.7 Summary
References
4 Corner Detection Algorithm Based on Cellular Neural Networks (CNN) and Differential Evolution (DE)
4.1 Introduction
4.2 Cellular Nonlinear/Neural Network (CNN)
4.3 Differential Evolution Method
4.4 Learning Scenario for the CNN
4.4.1 Adaptation of the Cloning Template Processing
4.4.2 Learning Scenario for the CNN
4.5 Experimental Results and Performance Evaluation
4.5.1 Detection and Localization Using Images with Ground Truth
4.5.2 Repeatability Evaluation Under Image Transformations
4.5.3 Computational Time Evaluation
4.6 Conclusions
References
5 Blood Vessel Segmentation Using Differential Evolution Algorithm
5.1 Introduction
5.2 Methodology
5.2.1 Preprocessing
5.2.2 Processing
5.2.3 Postprocessing
5.3 Experiments
5.4 Summary
References
6 Clustering Model Based on the Human Visual System
6.1 Introduction
6.2 Cellular-Nonlinear Neural Network
6.3 Human Visual Models
6.3.1 Receptive Cells
6.3.2 Modification of the Spatial Resolution
6.4 Clustering Method
6.4.1 Representation of Data Distribution as a Binary Image
6.4.2 Receptive Cells
6.4.3 Modification of the Spatial Resolution
6.4.4 Computational Clustering Model
6.5 Experiments
6.6 Summary
References
7 Metaheuristic Algorithms for Wireless Sensor Networks
7.1 Introduction
7.2 Fast Energy-Aware OLSR
7.2.1 Differential Evolution
7.3 Ant Colony Optimization (ACO) for Ad Hoc Mobile Networks
7.4 Greedy Randomized Adaptive Search Procedure (GRASP)
7.5 Gray Wolf Optimizer (GWO)
7.5.1 Application to a Network Model for Energy Optimization
7.6 Intelligent Water Drops (IWD)
7.7 Particle Swarm Optimization (PSO)
7.7.1 Application in Routing in Networks. Minimum Spanning Tree Problem
7.8 Tabu Search
7.8.1 Performance of Tabu Search for Location in Wireless Sensor Networks
7.8.2 Location Algorithm for Wireless Sensor Networks
7.9 Firefly Algorithm (FA)
7.9.1 Firefly Meta-Heuristic Algorithm Applied to Artificial Neural Network
7.10 Scatter Search (SS)
7.10.1 Performance Metrics
7.11 Greedy Randomized Adaptive Search Procedures (GRASP)
7.11.1 GRASP for Spare Capacity Allocation Problem (SCA)
7.11.2 GRASP Optimization for the Multi-Level Capacitated Minimum Spanning Tree Problem
7.12 Applications
7.13 Conclusions
References
8 Metaheuristic Algorithms Applied to the Inventory Problem
8.1 Introduction
8.1.1 The Inventory Example Problem
8.1.2 Behavior of a Cost Function Under Quantity Discounts
8.1.3 The Solution, Format, and Parameters
8.1.4 Testing if a Possible Solution Is Feasible
8.1.5 The Total Average Cost Function
8.1.6 Solutions
8.2 Solving Using Metaheuristics Algorithms
8.2.1 Particle Swarm Optimization
8.2.2 Genetic Algorithm (GA)
8.2.3 Differential Evolution (dE)
8.2.4 Tabu Search
8.2.5 Simulated Annealing
8.2.6 Grey Wolf Optimizer
8.3 More Information About the Inventory Problem in the State of Art and in History
8.3.1 How Has Lot-Sizing, Supplier Selection, and Inventory Problems Been Solved Over the Years?
8.4 Conclusions
References