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Multi-objective optimization problem...
~
Lobato, Fran Sergio.
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Multi-objective optimization problems = concepts and self-adaptive parameters with mathematical and engineering applications /
Record Type:
Electronic resources : Monograph/item
Title/Author:
Multi-objective optimization problems/ by Fran Sergio Lobato, Valder Steffen Jr.
Reminder of title:
concepts and self-adaptive parameters with mathematical and engineering applications /
Author:
Lobato, Fran Sergio.
other author:
Steffen, Valder.
Published:
Cham :Springer International Publishing : : 2017.,
Description:
xx, 160 p. :ill., digital ;24 cm.
[NT 15003449]:
Chapter 1 Introduction -- Part 1 Basic Concepts -- Chapter 2 Multi-objective Optimization Problem -- Chapter 3 Treatment of multi-objective Optimization Problem -- Part 2 Methodology -- Chapter 4 Self-Adaptive Multi-objective Optimization Differential Evolution -- Part 3 Applications -- Chapter 5 Mathematical -- Chapter 6 Engineering -- Part 4 Final Considerations -- Chapter 7 Conclusions.
Contained By:
Springer eBooks
Subject:
Mathematical optimization. -
Online resource:
http://dx.doi.org/10.1007/978-3-319-58565-9
ISBN:
9783319585659
Multi-objective optimization problems = concepts and self-adaptive parameters with mathematical and engineering applications /
Lobato, Fran Sergio.
Multi-objective optimization problems
concepts and self-adaptive parameters with mathematical and engineering applications /[electronic resource] :by Fran Sergio Lobato, Valder Steffen Jr. - Cham :Springer International Publishing :2017. - xx, 160 p. :ill., digital ;24 cm. - SpringerBriefs in mathematics,2191-8198. - SpringerBriefs in mathematics..
Chapter 1 Introduction -- Part 1 Basic Concepts -- Chapter 2 Multi-objective Optimization Problem -- Chapter 3 Treatment of multi-objective Optimization Problem -- Part 2 Methodology -- Chapter 4 Self-Adaptive Multi-objective Optimization Differential Evolution -- Part 3 Applications -- Chapter 5 Mathematical -- Chapter 6 Engineering -- Part 4 Final Considerations -- Chapter 7 Conclusions.
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.
ISBN: 9783319585659
Standard No.: 10.1007/978-3-319-58565-9doiSubjects--Topical Terms:
517763
Mathematical optimization.
LC Class. No.: QA402.5
Dewey Class. No.: 519.6
Multi-objective optimization problems = concepts and self-adaptive parameters with mathematical and engineering applications /
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Chapter 1 Introduction -- Part 1 Basic Concepts -- Chapter 2 Multi-objective Optimization Problem -- Chapter 3 Treatment of multi-objective Optimization Problem -- Part 2 Methodology -- Chapter 4 Self-Adaptive Multi-objective Optimization Differential Evolution -- Part 3 Applications -- Chapter 5 Mathematical -- Chapter 6 Engineering -- Part 4 Final Considerations -- Chapter 7 Conclusions.
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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.
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Mathematics and Statistics (Springer-11649)
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