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Multi-Objective Optimization Problems

Concepts and Self-Adaptive Parameters with Mathematical and Engineering Applications

  • Fran Sérgio Lobato
  • Valder Steffen Jr.
Book

Part of the SpringerBriefs in Mathematics book series (BRIEFSMATH)

Table of contents

  1. Front Matter
    Pages i-xx
  2. Fran Sérgio Lobato, Valder Steffen Jr.
    Pages 1-5
  3. Basic Concepts

    1. Front Matter
      Pages 7-7
    2. Fran Sérgio Lobato, Valder Steffen Jr.
      Pages 9-23
    3. Fran Sérgio Lobato, Valder Steffen Jr.
      Pages 25-44
  4. Methodology

    1. Front Matter
      Pages 45-45
    2. Fran Sérgio Lobato, Valder Steffen Jr.
      Pages 47-73
  5. Applications

    1. Front Matter
      Pages 75-75
    2. Fran Sérgio Lobato, Valder Steffen Jr.
      Pages 77-108
    3. Fran Sérgio Lobato, Valder Steffen Jr.
      Pages 109-152
  6. Final Considerations

    1. Front Matter
      Pages 153-153
    2. Fran Sérgio Lobato, Valder Steffen Jr.
      Pages 155-157
  7. Back Matter
    Pages 159-160

About this book

Introduction

This book is aimed at undergraduate and graduate students in applied mathematics or computer science, as a tool for solving real-world design problems. The present work covers fundamentals in multi-objective optimization and applications in mathematical and engineering system design using a new optimization strategy, namely the Self-Adaptive Multi-objective Optimization Differential Evolution (SA-MODE) algorithm. This strategy is proposed in order to reduce the number of evaluations of the objective function through dynamic update of canonical Differential Evolution parameters (population size, crossover probability and perturbation rate). The methodology is applied to solve mathematical functions considering test cases from the literature and various engineering systems design, such as cantilevered beam design, biochemical reactor, crystallization process, machine tool spindle design, rotary dryer design, among others.

Keywords

Multi-objective optimization Differential evolution Self-adaptive parameters MOOP MOP Evolutionary algorithms Genetic Algorithms SA-MODE SBMAC

Authors and affiliations

  • Fran Sérgio Lobato
    • 1
  • Valder Steffen Jr.
    • 2
  1. 1.School of Chemical EngineeringFederal University of UberlândiaUberlândiaBrazil
  2. 2.School of Mechanical EngineeringFederal University of UberlândiaUberlândiaBrazil

Bibliographic information