Evolutionary Large-Scale Multi-Objective Optimization and Applications
1. Auflage Juli 2024
352 Seiten, Hardcover
Wiley & Sons Ltd
Tackle the most challenging problems in science and engineering with these cutting-edge algorithms
Multi-objective optimization problems (MOPs) are those in which more than one objective needs to be optimized simultaneously. As a ubiquitous component of research and engineering projects, these problems are notoriously challenging. In recent years, evolutionary algorithms (EAs) have shown significant promise in their ability to solve MOPs, but challenges remain at the level of large-scale multi-objective optimization problems (LSMOPs), where the number of variables increases and the optimized solution is correspondingly harder to reach.
Evolutionary Large-Scale Multi-Objective Optimization and Applications constitutes a systematic overview of EAs and their capacity to tackle LSMOPs. It offers an introduction to both the problem class and the algorithms before delving into some of the cutting-edge algorithms which have been specifically adapted to solving LSMOPs. Deeply engaged with specific applications and alert to the latest developments in the field, it's a must-read for students and researchers facing these famously complex but crucial optimization problems.
The book's readers will also find:
* Analysis of multi-optimization problems in fields such as machine learning, network science, vehicle routing, and more
* Discussion of benchmark problems and performance indicators for LSMOPs
* Presentation of a new taxonomy of algorithms in the field
Evolutionary Large-Scale Multi-Objective Optimization and Applications is ideal for advanced students, researchers, and scientists and engineers facing complex optimization problems.
Foreword xiii
Preface xv
Acronyms xix
Symbols xxiii
1 Multi-Objective Evolutionary Algorithms and Evolutionary Large-Scale Optimization 1
1.1 Introduction 1
1.2 Multi-Objective Evolutionary Algorithms (MOEAs) 5
1.3 Evolutionary Large-Scale Optimization 21
1.4 Summary 24
2 Evolutionary Large-Scale Multi-Objective Optimization 31
2.1 Introduction 31
2.2 Test Problems for Large-Scale Multi-Objective Optimization 32
2.3 Performance Indicators 54
2.4 Test Problems for Sparse Large-Scale Multi-Objective Optimization 58
2.5 Performance Indicator for Sparse Large-Scale Multi-Objective Optimization 71
2.6 Summary 76
3 Evolutionary Algorithms for Large-Scale Multi-Objective Optimization 83
3.1 Introduction 83
3.2 Random Grouping-Based Evolutionary Algorithm 89
3.3 Decision Variable Clustering-Based Evolutionary Algorithm 93
3.4 Problem Reformulation-Based Evolutionary Algorithm 101
3.5 Competitive Swarm Optimizer-Based Evolutionary Algorithm 106
3.6 Experimental Comparisons 110
3.7 Summary 112
4 Evolutionary Algorithms for Sparse Large-Scale Multi-Objective Optimization 119
4.1 Introduction 119
4.2 Bi-Level Encoding-Based Evolutionary Algorithm 121
4.3 Machine Learning-Assisted Evolutionary Algorithm 127
4.4 Data Mining-Assisted Evolutionary Algorithm 134
4.5 Experimental Comparisons 143
4.6 Summary 146
5 Evolutionary Large-Scale Multi-Objective Optimization for Community Detection in Complex Networks 151
5.1 Introduction 151
5.2 Network Reduction-Based Multi-Objective Evolutionary Algorithm for Community Detection 152
5.3 Parallel Multi-Objective Evolutionary Algorithm for Community Detection 165
5.4 Summary 178
6 Evolutionary Large-Scale Multi-Objective Optimization in Logistics Scheduling 183
6.1 Introduction 183
6.2 Evolutionary Multi-Objective Route Grouping-Based Heuristic Algorithm for Large-Scale Capacitated Vehicle Routing Problems 184
6.3 Clustering-Based Surrogate-Assisted Multi-Objective Evolutionary Algorithm for Shelter Location Problem Under Uncertainty of Road Networks 195
6.4 Summary 206
7 Evolutionary Large-Scale Multi-Objective Optimization in Power Systems 211
7.1 Introduction 211
7.2 Ratio Error Estimation of Voltage Transformers 212
7.3 Problem Knowledge-Driven Coevolutionary Algorithm for Time-Varying Ratio Error Estimation 221
7.4 Summary 229
8 Evolutionary Large-Scale Multi-Objective Optimization in Radiotherapy Planning 235
8.1 Introduction 235
8.2 Problem Formulation 237
8.3 Bi-Encoding Coevolutionary Algorithm for IMRT Planning 240
8.4 Experimental Studies 252
8.5 Summary 255
9 Evolutionary Large-Scale Multi-Objective Optimization in Deep Learning 259
9.1 Introduction 259
9.2 Gradient-Guided Multi-Objective Evolutionary Algorithm for Training Deep Neural Networks 260
9.3 Action Command Encoding-Based Surrogate-Assisted Evolutionary Algorithm for Neural Architecture Search 288
9.4 Summary 310
References 310
Index 319
Ran Cheng, PhD, is an Associate Professor in the Department of Computer Science and Engineering at the Southern University of Science and Technology, China. He is an Associate Editor for the IEEE Transactions on Evolutionary Computation, IEEE Transactions on Artificial Intelligence, IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Transactions on Cognitive and Developmental Systems, and ACM Transactions on Evolutionary Learning and Optimization.
Ye Tian, PhD, is an Associate Professor in School of Computer Science and Technology at Anhui University, Hefei, China. He also serves as an Associate Editor of the IEEE Transactions on Evolutionary Computation.
Yaochu Jin, PhD, is a Chair Professor of Artificial Intelligence, Head of the Trustworthy and General Artificial Intelligence Laboratory, Westlake University, China. He was an Alexander von Humboldt Professor of Artificial Intelligence at the Bielefeld University, Germany, and Distinguished Chair in Computational Intelligence at the University of Surrey, United Kingdom.
