Description
Book SynopsisReal-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 cl
Table of Contents
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.
