Evolutionary Multi-Objective System Design-Theory and Applications

✍ Scribed by Nadia Nedjah (Editor); Luiza De Macedo Mourelle (Editor); Heitor Silverio Lopes (Editor)

• Publisher: Chapman and Hall/CRC
• Year: 2017
• Leaves: 242
• Edition: 1
• Category: Library

✦ Synopsis

Real-world engineering problems often require concurrent optimization of several design objectives, which are conflicting in cases. This type of optimization is generally called multi-objective or multi-criterion optimization. The area of research that applies evolutionary methodologies to multi-objective optimization is of special and growing interest. It brings a viable computational solution to many real-world problems.

Generally, multi-objective engineering problems do not have a straightforward optimal design. These kinds of problems usually inspire several solutions of equal efficiency, which achieve different trade-offs. Decision makers’ preferences are normally used to select the most adequate design. Such preferences may be dictated before or after the optimization takes place. They may also be introduced interactively at different levels of the optimization process. Multi-objective optimization methods can be subdivided into classical and evolutionary. The classical methods usually aim at a single solution while the evolutionary methods provide a whole set of so-called Pareto-optimal solutions.

Evolutionary Multi-Objective System Design: Theory and Applications

provides a representation of the state-of-the-art in evolutionary multi-objective optimization research area and related new trends. It reports many innovative designs yielded by the application of such optimization methods. It also presents the application of multi-objective optimization to the following problems:

• Embrittlement of stainless steel coated electrodes
• Learning fuzzy rules from imbalanced datasets
• Combining multi-objective evolutionary algorithms with collective intelligence
• Fuzzy gain scheduling control
• Smart placement of roadside units in vehicular networks
• Combining multi-objective evolutionary algorithms with quasi-simplex local search
• Design of robust substitution boxes
• Protein structure prediction problem
• Core assignment for efficient network-on-chip-based system design

✦ Table of Contents

Embrittlement of Stainless Steel Coated Electrodes

Diego Henrique A. Nascimento, Rogerio Martins Gomes, Elizabeth Fialho Wanner, and Mariana Presoti

Introduction

Manufacturing Process

Process Modeling

Process Optimization

Final Remarks

Learning Fuzzy Rules from Imbalanced Datasets using Multi-objective Evolutionary Algorithms

Edward Hinojosa C., Heloisa A. Camargo, and Yvan Tupac V.

Introduction

Imbalanced Dataset Problem

Fuzzy Rule-Based Systems

Genetic Fuzzy Systems

Proposed Method: IRL-ID-MOEA

Experimental Analysis

Final Remarks

Hybrid Multi-Objective Evolutionary Algorithms with Collective Intelligence

Daniel Cinalli, Luis Marti, Nayat Sanchez-Pi, and Ana Cristina Bicharra Garcia

Introduction

Foundations

Preferences and Interactive Methods

Collective Intelligence for MOEAs

Algorithms

Experimental Results

Final Remarks

Multiobjective Particle Swarm Optimization Fuzzy Gain Scheduling Control

Edson B. M. Costa and Ginalber L. O. Serra

Introduction

Takagi-Sugeno fuzzy modelling

Fuzzy gain scheduling control

Experimental results

Glossary

Multiobjective evolutionary algorithms for smart placement

Renzo Massobrio, Jamal Toutouh, and Sergio Nesmachnow

Introduction

Vehicular Communication Networks

Materials and methods: metaheuristics, evolutionary computation and multiobjective optimization

RSU deployment for VANETs

Multiobjective Evolutionary Algorithms for the RSU-DP

Experimental Analysis

Final Remarks

Solving Multi-Objective Problems with MOEA/D and Quasi-Simplex Local Search

Lucas Prestes, Carolina Almeida, and Richard Goncalves

Introduction

Multi-objective Optimization Problems

Multi-Objective Evolutionary Algorithm based on DecompositionDi_erential Evolution

Quasi-Simplex Local Search

Proposed Algorithm - MOEA/DQS

Experiments and Results

Final Remarks

Multi-objective Evolutionary Design of Robust Substitution Boxes

Nadia Nedjah and Luiza de Macedo Mourelle

Introduction

Preliminaries for Substitution Boxes

Evolutionary Algorithms: Nash Strategy and Evolvable Hardware

Evolutionary Coding of Resilient S-Boxes

Evolvable Hardware Implementation of S-Boxes

Performance Results

Final Remarks

Multi-objective approach to the Protein Structure Prediction Problem

Ricardo H. R. Lima, Vidal Fontoura, Aurora Pozo, and Roberto Santana

Introduction

Protein Structure Prediction

The HP Model

Multi-objective Optimization

A bi-objective optimization approach to HP protein folding

Experiments

Final Remarks

Multi-objective IP Assignment for E_cient NoC-based System Design

Maamar Bougherara, Rym Rahmoun, Amel Sadok, Nadia Nedjah, Mouloud Koudil, and Luiza de Macedo Mourelle

Introduction

Related Work NoC Internal Structure

Application and IP Repository Models

The IP Assignment Problem

Assignment with MOPSO Algorithm

Objective Functions

Results

Conclusions