Evolutionary Multi-Objective System Design Theory and Applications Chapman & Hall/CRC Computer and Information Science Series
Coordonnateurs : Nedjah Nadia, De Macedo Mourelle Luiza, Lopes Heitor Silverio
![Couverture de l’ouvrage Evolutionary Multi-Objective System Design](https://images.lavoisier.fr/couvertures/1317570171.jpg)
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
Embrittlement of Stainless Steel Coated Electrodes. Learning Fuzzy Rules from Imbalanced Datasets using Multi-objective Evolutionary Algorithms. Hybrid Multi-Objective Evolutionary Algorithms with Collective Intelligence. Multiobjective Particle Swarm Optimization Fuzzy Gain Scheduling Control. Multiobjective evolutionary algorithms for smart placement. Solving Multi-Objective Problems with MOEA/D and Quasi-Simplex Local Search. Multi-objective Evolutionary Design of Robust Substitution Boxes. Multi-objective approach to the Protein Structure Prediction Problem. Multi-objective IP Assignment for Efficient NoC-based System Design.
Nadia Nedjah, Luiza De Macedo Mourelle, Heitor Silverio Lopes
Date de parution : 06-2020
15.6x23.4 cm
Date de parution : 11-2017
15.6x23.4 cm
Thèmes d’Evolutionary Multi-Objective System Design :
Mots-clés :
Synthetic Minority Oversampling TEchnique; Multi-objective PSO; artificial intelligence; Pareto Front; neural networks; PSO Algorithm; natural computing; NSGA Ii Algorithm; machine learning; Hypervolume Indicator; fuzzy logic; MOPSO; optimization; Pareto Optimal Front; fuzzy gain scheduling control; Power Consumption; robust substitution boxes design; Pareto Front Approximation; protein structure prediction problem; MOEA; vehicular networks roadside units; Fuzzy Gain Scheduling; stainless steel coated electrodes embrittlement; Strength Pareto Evolutionary Algorithm; Smite; Local Search Step; Multi-objective Optimization; Phase Margin Specifications; Air Drying Stage; Imbalanced Datasets; TS Fuzzy Model; IGA; IP Block; VANET Application; Non-dominated Sorting Genetic Algorithm; Fuzzy Rules