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E-grāmata: Evolutionary Multi-Objective System Design: Theory and Applications

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Evolutionary Multi-Objective System Design: Theory and ApplicationsPalielināt
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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
Preface xiii
List of Figures
xvii
List of Tables
xxi
1 Embrittlement of Stainless Steel Coated Electrodes
1(18)
Diego Henrique A. Nascimento
Rogerio Martins Gomes
Elizabeth Fialho Wanner
Mariana Presoti
1.1 Introduction
1(2)
1.2 Manufacturing Process
3(2)
1.3 Process Modeling
5(5)
1.3.1 ANN Database
8(1)
1.3.2 MultiLayer Perceptron Network --- MLP
9(1)
1.4 Process Optimization
10(5)
1.4.1 Multi-objective Optimization
11(2)
1.4.2 Experimental Tests
13(2)
1.5 Final Remarks
15(4)
2 Learning Fuzzy Rules from Imbalanced Datasets using Multi-objective Evolutionary Algorithms
19(32)
Edward Hinojosa Cardenas
Heloisa A. Camargo
Yvan Jesus Tupac Valdivia
2.1 Introduction
20(2)
2.2 Imbalanced Dataset Problem
22(8)
2.2.1 Oversampling Methods
23(1)
2.2.1 A Synthetic Minority Over-sampling Technique (SMOTE)
24(1)
2.2.1.2 Borderline-Synthetic Minority Over-sampling TEchnique (Borderline-SMOTE)
25(1)
2.2.1.3 ADASYN (ADAptive SYNthetic Sampling)
25(1)
2.2.1.4 Safe-Level-SMOTE (Safe Level Synthetic Minority Over-sampling TEchnique)
25(1)
2.2.2 Undersampling Method
26(1)
2.2.2.1 TL (Tomek Link) Technique
27(1)
2.2.2.2 OSS (One Sided Selection) Technique
27(1)
2.2.2.3 NCL (Neighborhood CLeaning Rule) Technique
27(1)
2.2.2.4 SBC (underSampling Based on Clustering) Technique
28(1)
2.2.3 Hybrid Methods
28(1)
2.2.3.1 SMOTE + TL
28(1)
2.2.3.2 Synthetic Minority Over-sampling Technique + Edited Nearest Neighbor
29(1)
2.2.4 Evaluation Measure for Classification in Imbalanced Datasets
29(1)
2.2.4.1 Area under the ROC Curve
30(1)
2.3 Fuzzy Rule-Based Systems
30(3)
2.3.1 Fuzzy Rule-Based Classification Systems
31(1)
2.3.2 Classic Fuzzy Reasoning Method
32(1)
2.3.3 General Fuzzy Reasoning Method
32(1)
2.4 Genetic Fuzzy Systems
33(5)
2.4.1 Genetic Rule Learning
33(2)
2.4.2 Multi-Objective Evolutionary Fuzzy Systems
35(3)
2.5 Proposed Method: IRL-ID-MOEA
38(5)
2.5.1 A Predefined Dataset
38(1)
2.5.2 Fuzzy Classification Rule Learning Based on MOEA
39(2)
2.5.3 Select and Insert the Best Rule into the Rule Base
41(1)
2.5.4 Marked Examples Covered by the Best Rule
42(1)
2.6 Experimental Analysis
43(5)
2.7 Final Remarks
48(3)
3 Hybrid Multi-Objective Evolutionary Algorithms with Collective Intelligence
51(18)
Daniel Cinalli
Luis Marti
Nayat Sanchez-Pi
Ana Cristina Bicharra Garcia
3.1 Introduction
51(2)
3.2 Foundations
53(2)
3.2.1 Evolutionary Multi-Objective Optimization
53(1)
3.2.2 Collective Intelligence
54(1)
3.3 Preferences and Interactive Methods
55(2)
3.4 Collective Intelligence for MOEAs
57(1)
3.5 Algorithms
58(3)
3.5.1 CI-NSGA-II
58(2)
3.5.2 CI-SMS-EMOA
60(1)
3.6 Experimental Results
61(7)
3.6.1 Multi-Objective Test Problems
61(3)
3.6.2 Resource Distribution Problem
64(4)
3.7 Final Remarks
68(1)
4 Multi-Objective Particle Swarm Optimization Fuzzy Gain Scheduling Control
69(16)
Edson B. M. Costa
Ginalber L. O. Serra
4.1 Introduction
69(1)
4.2 Takagi--Sugeno Fuzzy Modeling
70(3)
4.2.1 Antecedent Parameters Estimation
71(1)
4.2.2 Consequent Parameters Estimation
72(1)
4.3 Fuzzy Gain Scheduling Control
73(3)
4.3.1 MOPSO Based Controller Tuning
74(2)
4.4 Experimental Results
76(6)
4.4.1 TS Fuzzy Modeling of the Thermal Plant
77(3)
4.4.2 Fuzzy Gain Scheduling Control of the Thermal Plant
80(1)
4.4.3 Final Remarks
81(1)
4.5 Glossary
82(3)
5 Multi-Objective Evolutionary Algorithms for Smart Placement of Roadside Units in Vehicular Networks
85(30)
Renzo Massobrio
Jamal Toutouh
Sergio Nesmachnow
5.1 Introduction
86(2)
5.2 Vehicular Communication Networks
88(3)
5.3 Materials and Methods: Metaheuristics, Evolutionary Computation and Multi-Objective Optimization
91(6)
5.3.1 Metaheuristics
91(1)
5.3.2 Evolutionary Algorithms
91(2)
5.3.3 Multi-Objective Optimization Problems
93(1)
5.3.4 Multi-Objective Evolutionary Algorithms
93(4)
5.4 RSU Deployment for VANETs
97(5)
5.4.1 The RSU Deployment Problem
97(1)
5.4.2 Related Work
98(1)
5.4.2.1 Exact Methods
99(1)
5.4.2.2 Heuristics
100(1)
5.4.2.3 Metaheuristics and Evolutionary Computation
101(1)
5.5 Multi-Objective Evolutionary Algorithms for the RSU-DP
102(4)
5.5.1 Problem Encoding
103(1)
5.5.2 Evolutionary Operators
103(2)
5.5.3 Evaluation of the Objective Functions
105(1)
5.6 Experimental Analysis
106(7)
5.6.1 Problem Instances
106(2)
5.6.2 Comparison Against Greedy Algorithms
108(1)
5.6.3 MOEAs' Parameter Settings
109(1)
5.6.4 Numerical Results
109(4)
5.7 Final Remarks
113(2)
6 Solving Multi-Objective Problems with MOEA/D and Quasi-Simplex Local Search
115(24)
Lucas Prestes
Carolina Almeida
Richard Goncalves
6.1 Introduction
116(1)
6.2 Multi-objective Optimization Problems
117(1)
6.3 Multi-Objective Evolutionary Algorithm Based on Decomposition
118(2)
6.4 Differential Evolution
120(1)
6.5 Quasi-Simplex Local Search
121(2)
6.6 Proposed Algorithm---MOEA/DQS
123(2)
6.7 Experiments and Results
125(11)
6.7.1 Addressed Problems
126(1)
6.7.2 Quality Indicators
126(1)
6.7.3 Effect of Different Local Search Configurations
127(1)
6.7.3.1 Effect of the Local Search Formulations
128(1)
6.7.3.2 Effect of the Local Search Scope
128(1)
6.7.3.3 Effect of the Selection of Solutions to Apply the Local Search
129(1)
6.7.3.4 Effect of the Number of Evaluations between Local Search Applications
129(1)
6.7.4 Comparison with Literature
130(1)
6.7.4.1 Benchmark CEC 2009
131(2)
6.7.4.2 Benchmark WFG
133(1)
6.7.4.3 Benchmark DTLZ
134(1)
6.7.4.4 Benchmark ZDT
135(1)
6.8 Final Remarks
136(3)
7 Multi-objective Evolutionary Design of Robust Substitution Boxes
139(12)
Nadia Nedjah
Luiza de Macedo Mourelle
7.1 Introduction
139(2)
7.2 Preliminaries for Substitution Boxes
141(1)
7.3 Evolutionary Algorithms: Nash Strategy and Evolvable Hardware
142(3)
7.3.1 Nash Equilibrium-based Evolutionary Algorithm
143(1)
7.3.2 Evolvable Hardware
143(1)
7.3.3 Crossover Operators for S-box Codings and Hardware Implementations
144(1)
7.4 Evolutionary Coding of Resilient S-boxes
145(1)
7.5 Evolvable Hardware Implementation of S-boxes
146(1)
7.6 Performance Results
147(2)
7.6.1 Performance of S-box Evolutionary Coding
148(1)
7.6.2 Performance of S-box Evolvable Hardware
149(1)
7.7 Final Remarks
149(2)
8 Multi-objective Approach to the Protein Structure Prediction Problem
151(20)
Ricardo H. R. Lima
Vidal Fontoura
Aurora Pozo
Roberto Santana
8.1 Introduction
151(3)
8.2 Protein Structure Prediction
154(2)
8.2.1 The HP Model
154(2)
8.3 Multi-objective Optimization
156(3)
8.3.1 Non-dominated Sorting Genetic Algorithm II
157(1)
8.3.2 IBEA (Indicator-Based Evolutionary Algorithm)
158(1)
8.4 A Bi-objective Optimization Approach to HP Protein Folding
159(5)
8.5 Experiments
164(4)
8.5.1 Comparison between the Modified and Traditional Versions of the MOEAs
165(2)
8.5.2 Comparison with Previous Single-objective Approaches
167(1)
8.6 Final Remarks
168(3)
9 Multi-objective IP Assignment for Efficient NoC-based System Design
171(18)
Maamar Bougherara
Rym Rahmoun
Amel Sadok
Nadia Nedjah
Mouloud Koudil
Luiza de Macedo Mourelle
9.1 Introduction
172(1)
9.2 Related Work
173(1)
9.3 NoC Internal Structure
174(1)
9.4 Application and IP Repository Models
175(2)
9.4.1 Task Graph Representation
176(1)
9.4.2 Repository Representation
177(1)
9.5 The IP Assignment Problem
177(1)
9.6 Assignment with MOPSO Algorithm
178(3)
9.7 Objective Functions
181(1)
9.7.1 Area
181(1)
9.7.2 Power Consumption
182(1)
9.7.3 Execution Time
182(1)
9.8 Results
182(6)
9.9 Conclusions
188(1)
Bibliography 189(24)
Index 213
Nadia Nedjah, Luiza De Macedo Mourelle, Heitor Silverio Lopes
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
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