Multi-Objective Optimization in Theory and Practice is a simplified two-part approach to multi-objective optimization (MOO) problems.
This second part focuses on the use of metaheuristic algorithms in more challenging practical cases. The book includes ten chapters that cover several advanced MOO techniques. These include the determination of Pareto-optimal sets of solutions, metaheuristic algorithms, genetic search algorithms and evolution strategies, decomposition algorithms, hybridization of different metaheuristics, and many-objective (more than three objectives) optimization and parallel computation. The final section of the book presents information about the design and types of fifty test problems for which the Pareto-optimal front is approximated. For each of them, the package NSGA-II is used to approximate the Pareto-optimal front.
It is an essential handbook for students and teachers involved in advanced optimization courses in engineering, information science and mathematics degree programs.
Table of Contents
Chapter 1 Pareto-Optimal Front Determination
Chapter 2 Metaheuristic Optimization Algorithms
Chapter 3 Evolutionary Strategy Algorithms
Chapter 4 Genetic Search Algorithms
Chapter 5 Evolution Strategy Algorithms
Chapter 6 Swarm Intelligence And Co-Evolutionary Algorithms
Chapter 7 Decomposition-Based And Hybrid Evolutionary Algorithms
Chapter 8 Many-Objective Optimization And Parallel Computation
Chapter 9 Design of Test Problems
Chapter 10 Fifty Collected Test Functions
List of Abbreviations
List of Journal Abbreviations in the References
List of Symbols
Subject Index
Author
- André A. Keller
