Multiobjective Optimization Methodology

Multiobjective Optimization Methodology: A Jumping Gene Approach

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Features

  • Offers comprehensive coverage of state-of-the-art algorithms in multiobjective optimization, highlighting the recently developed jumping gene algorithms
  • Provides the technical know-how for obtaining trade-off solutions between solution spread and convergence
  • Applies jumping genes algorithms to engineering designs
  • Explains how jumping gene algorithms can be used to gain computational-efficient and fast-rate convergence solutions as well as extreme outlier solutions
  • Supplies detailed justification for the jumping gene algorithms with simulation and mathematical derivations
  • Includes numerous illustrations, tables, equations, and a 16-page color insert

Summary

The first book to focus on jumping genes outside bioscience and medicine, Multiobjective Optimization Methodology: A Jumping Gene Approach introduces jumping gene algorithms designed to supply adequate, viable solutions to multiobjective problems quickly and with low computational cost.

Better Convergence and a Wider Spread of Nondominated Solutions

The book begins with a thorough review of state-of-the-art multiobjective optimization techniques. For readers who may not be familiar with the bioscience behind the jumping gene, it then outlines the basic biological gene transposition process and explains the translation of the copy-and-paste and cut-and-paste operations into a computable language.

To justify the scientific standing of the jumping genes algorithms, the book provides rigorous mathematical derivations of the jumping genes operations based on schema theory. It also discusses a number of convergence and diversity performance metrics for measuring the usefulness of the algorithms.

Practical Applications of Jumping Gene Algorithms

Three practical engineering applications showcase the effectiveness of the jumping gene algorithms in terms of the crucial trade-off between convergence and diversity. The examples deal with the placement of radio-to-fiber repeaters in wireless local-loop systems, the management of resources in WCDMA systems, and the placement of base stations in wireless local-area networks.

Offering insight into multiobjective optimization, the authors show how jumping gene algorithms are a useful addition to existing evolutionary algorithms, particularly to obtain quick convergence solutions and solutions to outliers.

Table of Contents

Introduction
Background on Genetic Algorithms
Organization of Chapters
References

Overview of Multiobjective Optimization
Classification of Optimization Methods
Multiobjective Algorithms
References

Jumping Gene Computational Approach
Biological Background
Overview of Computational Gene Transposition
Jumping Gene Genetic Algorithms
Real-Coding Jumping Operations
Simulation Results
References

Theoretical Analysis of Jumping Gene Operations
Overview of Schema Models
Exact Schema Theorem for Jumping Gene Transposition
Theorems of Equilibrium and Dynamical Analysis
Simulation Results and Analysis
Discussion
References

Performance Measures on Jumping Gene
Convergence Metric: Generational Distance
Convergence Metric: Deb and Jain Convergence Metric
Diversity Metric: Spread
Diversity Metric: Extreme Nondominated Solution Generation
Binary ε-Indicator Statistical Test Using Performance Metrics Jumping Gene Verification and Results References

Radio-To-Fiber Repeater Placement in Wireless Local-Loop Systems
Introduction
Path Loss Model
Mathematical Formulation
Chromosome Representation
Jumping Gene Transposition
Chromosome Repairing
Results and Discussion
References

Resource Management in WCDMA
Introduction
Mathematical Formulation
Chromosome Representation
Initial Population
Jumping Gene Transposition
Mutation
Ranking Rule
Results and Discussion
Discussion of Real-Time Implementation
References

Base Station Placement in WLANs
Introduction
Path Loss Model
Mathematical Formulation
Chromosome Representation
Jumping Gene Transposition
Chromosome Repairing
Results and Discussion
References

Conclusions
Reference

Appendices
Appendix A: Proofs of Lemmas in Chapter 4
Appendix B: Benchmark Test Functions
Appendix C: Chromosome Representation
Appendix D: Design of the Fuzzy PID Controller

Author Bio(s)

Editorial Reviews

"This is an interesting and practical book. It is easy to read [and] provides good background information ... [and] cutting-edge technologies to solve the challenging multi-objective optimization problems."
—Mo-Yuen Chow, North Carolina State University, Raleigh, USA

"The authors describe the jumping gene approach to solve multiobjective optimization problems. It is quite [a] new approach and complements standard operations used in genetic algorithms."
—Marcin Anholcer (Poznan), Zentralblatt MATH 1273

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