Applied Optimization and Swarm Intelligence (Springer Tracts in Nature-Inspired Computing)

✍ Scribed by Eneko Osaba (editor), Xin-She Yang (editor)

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 236
• Edition: 1st ed. 2021
• Category: Library

✦ Synopsis

This book gravitates on the prominent theories and recent developments of swarm intelligence methods, and their application in both synthetic and real-world optimization problems. The special interest will be placed in those algorithmic variants where biological processes observed in nature have underpinned the core operators underlying their search mechanisms. In other words, the book centers its attention on swarm intelligence and nature-inspired methods for efficient optimization and problem solving. The content of this book unleashes a great opportunity for researchers, lecturers and practitioners interested in swarm intelligence, optimization problems and artificial intelligence.

✦ Table of Contents

Preface
Contents
Editors and Contributors
1 Applied Optimization and Swarm Intelligence: A Systematic Review and Prospect Opportunities
1 Introduction
2 Swarm Intelligence in Recent Years
3 Swarm Intelligence and Applied Optimization
3.1 Swarm Intelligence in Transportation and Logistics
3.2 Swarm Intelligence in Industry
3.3 Swarm Intelligence in Medicine
3.4 Swarm Intelligence in Energy
4 Challenges and Opportunities
5 Conclusions
References
2 A Review on Ensemble Methods and their Applications to Optimization Problems
1 Introduction to Ensemble Methods for Optimization
2 Techniques in Assembling EEMs
3 Main Ensemble Evolutionary Methods Proposed in Literature
3.1 Ensemble Differential Evolution as a Competitive Single Population EEM
3.2 Ensemble Genetic Algorithms as a Competitive Multi-population EEM
3.3 Memetic Algorithms as a Cooperative Single Population EEM
3.4 Coral Reef Optimization with Substrate Layer as a Cooperative Multi-population EEM
4 Challenges and Future Works in EEM Study
5 Conclusions
References
3 A Brief Overview of Swarm Intelligence-Based Algorithms for Numerical Association Rule Mining
1 Introduction
2 Swarm Intelligence in a Nutshell
3 Overview of SI-Based Algorithm for NARM
3.1 Particle Swarm Optimization NARM Variants
3.2 Ant Colony Optimization NARM Variants
3.3 Bat Algorithm NARM Variants
3.4 Other NARM Variants
4 Analysis of Algorithms for Numerical Association Rule Mining
4.1 Representation of Solutions
4.2 Definition of the Fitness Function
4.3 Discussion
5 Conclusions and Future Challenges
References
4 Review of Swarm Intelligence for Improving Time Series Forecasting
1 Introduction
2 Time Series Analysis
2.1 Nature and Use of Forecasts
2.2 Forecasting Process
2.3 Classical LInear Forecasting Models
3 Deep Learning for Time Series Forecasting
3.1 Neural Network Architecture
3.2 Feed Forward Neural Networks
3.3 Recurrent Neural Network and Long Short-Term Memory
4 Swarm Intelligence for Time Series Forecasting
4.1 Hybridization of Optimization and Time Series Prediction
4.2 Particle Swarm Optimization (PSO) Algorithm
4.3 Artificial Fish Swarm Algorithm (AFSA)
4.4 Artificial Bee Colony Algorithm
4.5 Grey Wolf Optimizer
4.6 Cuckoo Search
4.7 Other SI Algorithms
5 Challenges and Opportunities
6 Conclusion
References
5 Soccer-Inspired Metaheuristics: Systematic Review of Recent Research and Applications
1 Introduction
2 Nature-Inspired Computation and Optimization Metaheuristics
3 Soccer-Inspired Metaheuristics: A Systematic Review
3.1 Football Optimization Algorithm
3.2 Soccer Game Optimization
3.3 Golden Ball Metaheuristic
3.4 Soccer League Competition Algorithm
3.5 Soccer League Optimization
3.6 World Cup Competition Algorithm
3.7 Football Game Inspired Algorithm
3.8 Tiki-Taka Algorithm
4 Conclusions
References
6 Formal Cognitive Modeling of Swarm Intelligence for Decision-Making Optimization Problems
1 Introduction
1.1 Cognitive Informatics
1.2 Decision-Making
1.3 Aims and Structure of This Chapter
2 Swarm Intelligence
2.1 Particle Swarm Optimization
2.2 The Firefly Algorithm
2.3 The Cuckoo Search Algorithm
2.4 The Bat Algorithm
3 Formal Cognitive Modeling of Swarm Intelligence for Decision-Making
3.1 Formal Cognitive Model for Decision-Making
3.2 A Formal Cognitive Modeling Approach to Swarm Intelligence
3.3 Cognitive Model of Swarm Intelligence for Decision-Making
4 Discussion and Advantages of Our Cognitive Formalism
5 Conclusions and Future Work
References
7 Nature-Inspired Optimization Algorithms for Path Planning and Fuzzy Tracking Control of Mobile Robots
1 Introduction
2 Optimal Path Planning Problem and Approach to Solve It
3 Optimal PI-Fuzzy Controller-Based Tracking Control Problem and Approach to Solve It
4 Inclusion of WOA in Optimal Path Planning and Controller Tuning Approaches
5 Implementation Details
6 Conclusions
References
8 A Hardware Architecture and Physical Prototype for General-Purpose Swarm Minirobotics: Proteus II
1 Introduction
1.1 Swarm Intelligence
1.2 Swarm Robotics
1.3 A First Swarm Robotic Prototype: Proteus I
1.4 Aims and Structure of This Chapter
2 Previous Work
3 A General-Purpose Minirobotic Prototype for Swarm Intelligence: Proteus II
3.1 Conceptual Design
3.2 Physical Arrangement of Components
3.3 Hardware Architecture and Main Components
3.4 Programming Framework
4 Prototype Applicability to Swarm Minirobotics
5 Conclusions and Future Work
References
9 Evolving a Multi-objective Optimization Framework
1 Introduction
2 The jMetal Framework
3 Component-Based Evolutionary Algorithm Template
4 Visualization
4.1 Plotting Fronts
4.2 Visualization of Comparative Studies Results
5 Automatic Configuration of Metaheuristics
6 Asynchronous Parallelism
7 Discussion
8 Conclusions
References
10 Swarm Intelligence Based Optimum Design of Deep Excavation Systems
1 Introduction
2 Design of the Deep Excavation Systems
2.1 The Design of SRASW According to FHWA-IF-99-015
2.2 The Numerical Analyses for SRASW Design
3 Swarm Intelligence and Particle Swarm Optimizer
3.1 Swarm Intelligence
3.2 Particle Swarm Optimizer (PSO)
4 The Optimum Design of a Single-Row Anchored Sheet Wall
4.1 Design Parameters
4.2 Constraints
4.3 Objective Functions
4.4 Optimization Process
4.5 Design Examples
5 Conclusions
References