Evolutionary Large-Scale Multi-Objective Optimization and Applications

Inbunden, Engelska, 2024

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Beskrivning

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 fieldEvolutionary Large-Scale Multi-Objective Optimization and Applications is ideal for advanced students, researchers, and scientists and engineers facing complex optimization problems.

Produktinformation

Utgivningsdatum:2024-07-17
Mått:152 x 229 x 21 mm
Vikt:794 g
Format:Inbunden
Språk:Engelska
Antal sidor:352
Förlag:John Wiley & Sons Inc
ISBN:9781394178414

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Mer om författaren

Xingyi Zhang, PhD, is a Professor in the School of Computer Science and Technology at Anhui University, Hefei, China. He serves as an Associate Editor of the IEEE Transactions on Evolutionary Computation, and a member of the editorial board for Complex and Intelligent Systems. 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.

Innehållsförteckning

About the Authors xiForeword xiiiPreface xvAcronyms xixSymbols xxiii1 Multi-Objective Evolutionary Algorithms and Evolutionary Large-Scale Optimization 11.1 Introduction 11.2 Multi-Objective Evolutionary Algorithms (MOEAs) 51.3 Evolutionary Large-Scale Optimization 211.4 Summary 242 Evolutionary Large-Scale Multi-Objective Optimization 312.1 Introduction 312.2 Test Problems for Large-Scale Multi-Objective Optimization 322.3 Performance Indicators 542.4 Test Problems for Sparse Large-Scale Multi-Objective Optimization 582.5 Performance Indicator for Sparse Large-Scale Multi-Objective Optimization 712.6 Summary 763 Evolutionary Algorithms for Large-Scale Multi-Objective Optimization 833.1 Introduction 833.2 Random Grouping-Based Evolutionary Algorithm 893.3 Decision Variable Clustering-Based Evolutionary Algorithm 933.4 Problem Reformulation-Based Evolutionary Algorithm 1013.5 Competitive Swarm Optimizer-Based Evolutionary Algorithm 1063.6 Experimental Comparisons 1103.7 Summary 1124 Evolutionary Algorithms for Sparse Large-Scale Multi-Objective Optimization 1194.1 Introduction 1194.2 Bi-Level Encoding-Based Evolutionary Algorithm 1214.3 Machine Learning-Assisted Evolutionary Algorithm 1274.4 Data Mining-Assisted Evolutionary Algorithm 1344.5 Experimental Comparisons 1434.6 Summary 1465 Evolutionary Large-Scale Multi-Objective Optimization for Community Detection in Complex Networks 1515.1 Introduction 1515.2 Network Reduction-Based Multi-Objective Evolutionary Algorithm for Community Detection 1525.3 Parallel Multi-Objective Evolutionary Algorithm for Community Detection 1655.4 Summary 1786 Evolutionary Large-Scale Multi-Objective Optimization in Logistics Scheduling 1836.1 Introduction 1836.2 Evolutionary Multi-Objective Route Grouping-Based Heuristic Algorithm for Large-Scale Capacitated Vehicle Routing Problems 1846.3 Clustering-Based Surrogate-Assisted Multi-Objective Evolutionary Algorithm for Shelter Location Problem Under Uncertainty of Road Networks 1956.4 Summary 2067 Evolutionary Large-Scale Multi-Objective Optimization in Power Systems 2117.1 Introduction 2117.2 Ratio Error Estimation of Voltage Transformers 2127.3 Problem Knowledge-Driven Coevolutionary Algorithm for Time-Varying Ratio Error Estimation 2217.4 Summary 2298 Evolutionary Large-Scale Multi-Objective Optimization in Radiotherapy Planning 2358.1 Introduction 2358.2 Problem Formulation 2378.3 Bi-Encoding Coevolutionary Algorithm for IMRT Planning 2408.4 Experimental Studies 2528.5 Summary 2559 Evolutionary Large-Scale Multi-Objective Optimization in Deep Learning 2599.1 Introduction 2599.2 Gradient-Guided Multi-Objective Evolutionary Algorithm for Training Deep Neural Networks 2609.3 Action Command Encoding-Based Surrogate-Assisted Evolutionary Algorithm for Neural Architecture Search 2889.4 Summary 310References 310Index 319