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Multi-Objective Optimization in Theory and Practice II: Metaheuristic Algorithms

  • ID: 4763757
  • Book
  • March 2019
  • Region: Global
  • 310 Pages
  • Bentham Science Publishers Ltd
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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.

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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

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  • André A. Keller
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