New Optimization Algorithms and their Applications: Atom-Based, Ecosystem-Based and Economics-Based

✍ Scribed by Zhenxing Zhang, Liying Wang, Weiguo Zhao

• Publisher: Elsevier
• Year: 2021
• Tongue: English
• Leaves: 180
• Edition: 1
• Category: Library

✦ Synopsis

New Optimization Algorithms and Applications: Atom-Based, Ecosystem-Based and Economics-Based presents the development of three new optimization algorithms - an Atom Search Optimization (ASO) algorithm, an Artificial Ecosystem-Based Optimization algorithm (AEO), a Supply Demand Based Optimization (SDO), and their applications within engineering. These algorithms are based on benchmark functions and typical engineering cases. The book describes the algorithms in detail and demonstrates how to use them in engineering. The title verifies the performance of the algorithms presented, simulation results are given, and MATLAB® codes are provided for the methods described.

Over seven chapters, the book introduces ASO, AEO and SDO, and presents benchmark functions, engineering problems, and coding. This volume offers technicians and researchers engaged in computer and intelligent algorithm work and engineering with one source of information on novel optimization algorithms.

✦ Table of Contents

Front Cover
New Optimization Algorithms and their Applications: Atom-Based, Ecosystem-Based and Economics-Based
Copyright
Contents
Preface
Acknowledgments
Chapter 1: Introduction
1.1. Optimization algorithms
1.2. A short outline of optimization algorithms
1.3. Organization of this book
References
Chapter 2: Atom search optimization algorithm
2.1. Introduction
2.2. Basic molecular dynamics
2.3. Atom search optimization
2.3.1. Mathematical representation of interaction force
2.3.2. Mathematical representation of geometric constraint
2.3.3. Mathematical representation of atomic motion
2.3.4. Framework of the ASO algorithm
2.4. Experimental results
2.4.1. Benchmark functions
2.4.2. Experimental setup
2.4.3. Results and discussion
2.4.3.1. Qualitative results of ASO
2.4.3.2. Convergence preference of the algorithm
2.5. Conclusions
References
Chapter 3: Engineering applications of atom search optimization algorithm
3.1. Introduction
3.2. Parameter estimation for chaotic system
3.2.1. Simulation model
3.2.2. Results and discussion
3.3. Circular antenna array design problem
3.3.1. Problem description
3.3.2. Results and discussion
3.4. Spread spectrum radar polyphase code design
3.4.1. Problem description
3.4.2. Results and discussion
3.5. Conclusions
References
Chapter 4: Artificial ecosystem-based optimization algorithm
4.1. Introduction
4.2. Artificial ecosystem-based optimization
4.2.1. Inspiration
4.2.2. Artificial ecosystem-based optimization
4.2.2.1. Production
4.2.2.2. Consumption
4.2.2.3. Decomposition
4.3. Results and discussion
4.3.1. Analysis of exploitation capability
4.3.2. Analysis of exploration capability
4.3.3. Analysis of avoidance of local optima
4.3.4. Analysis of convergence behavior
4.3.5. Statistical significance analysis
4.3.6. Sensitivity analysis
4.4. Conclusions
References
Chapter 5: Engineering applications of artificial ecosystem-based optimization
5.1. Engineering optimization using the AEO algorithm
5.1.1. Tension/compression spring design
5.1.2. Pressure vessel design
5.1.3. Welded beam design
5.1.4. Speed reducer design
5.1.5. Multiple disc clutch brake design
5.2. Static economic load dispatch problem
5.2.1. Problem description
5.2.2. Results and discussion
5.3. Hydrothermal scheduling problem
5.3.1. Problem description
5.3.2. Results and discussion
5.4. Conclusions
References
Chapter 6: Supply-demand-based optimization
6.1. Introduction
6.2. Supply-demand-based optimization
6.2.1. Inspiration
6.2.2. Mathematical representation of SDO
6.3. Experimental results and discussion
6.3.1. Experimental setup
6.3.2. Analysis of exploitation capability
6.3.3. Analysis of exploration capability
6.3.4. Analysis of avoidance of local optima
6.3.5. Analysis of convergence behavior
6.4. Conclusions
References
Chapter 7: Engineering applications of supply-demand-based optimization
7.1. Introduction
7.2. Three-bar truss design
7.3. Cantilever beam design
7.4. Rolling element bearing design
7.5. Gear train design
7.6. Conclusions
References
Appendix
Appendix A. Benchmark functions
Appendix B. Engineering design problems
B.1. Tension/compression spring design
B.2. Pressure vessel design
B.3. Welded beam design
B.4. Speed reducer design
B.5. Multiple disc clutch brake design
B.6. Three-bar truss design
B.7. Cantilever beam design
B.8. Rolling element bearing design
B.9. Gear train design
Appendix C. Codes in MATLAB
Index
Back Cover