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Evolutionary Multi-Objective System Design

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advanced multi-objective optimisation strategies
Age Group_Uncategorized
Age Group_Uncategorized
Air Drying Stage
artificial intelligence
automatic-update
B01=Heitor Silverio Lopes
B01=Luiza De Macedo Mourelle
B01=Nadia Nedjah
Category1=Non-Fiction
Category=UNF
Category=UYQ
collective intelligence techniques
computational intelligence
COP=United States
decision support methods
Delivery_Delivery within 10-20 working days
engineering system modelling
eq_bestseller
eq_computing
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Fuzzy Gain Scheduling
fuzzy gain scheduling control
fuzzy logic
Fuzzy Rules
Hypervolume Indicator
IGA
Imbalanced Datasets
IP Block
Language_English
Local Search Step
machine learning
MOEA
MOPSO
Multi-objective Optimization
Multi-objective PSO
natural computing
network-on-chip design
neural networks
Non-dominated Sorting Genetic Algorithm
NSGA Ii Algorithm
optimisation algorithms
optimization
PA=Available
Pareto Front
Pareto Front Approximation
Pareto Optimal Front
Phase Margin Specifications
Power Consumption
Price_€100 and above
protein structure prediction problem
PS=Active
PSO Algorithm
robust substitution boxes design
Smite
softlaunch
stainless steel coated electrodes embrittlement
Strength Pareto Evolutionary Algorithm
Synthetic Minority Oversampling TEchnique
TS Fuzzy Model
VANET Application
vehicular networks roadside units

Product details

  • ISBN 9781498780285
  • Weight: 478g
  • Dimensions: 156 x 234mm
  • Publication Date: 19 Oct 2017
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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Description

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
About

Nadia Nedjah, Luiza De Macedo Mourelle, Heitor Silverio Lopes