Aerodynamic design via control theory
Journal of Scientific Computing
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
A tutorial on support vector regression
Statistics and Computing
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Computational Intelligence: Concepts to Implementations
Computational Intelligence: Concepts to Implementations
Using OpenMP: Portable Shared Memory Parallel Programming (Scientific and Engineering Computation)
Using OpenMP: Portable Shared Memory Parallel Programming (Scientific and Engineering Computation)
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Improvements to the SMO algorithm for SVM regression
IEEE Transactions on Neural Networks
Adaptive heuristic search algorithm for discrete variables based multi-objective optimization
Structural and Multidisciplinary Optimization
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In this article, the optimization problem of designing transonic airfoil sections is solved using a framework based on a multi-objective optimizer and surrogate models for the objective functions and constraints. The computed Pareto-optimal set includes solutions that provide a trade-off between maximizing the lift-to-drag ratio during cruise and minimizing the trailing edge noise during the aircraft's approach to landing. The optimization problem was solved using a recently developed multi-objective optimizer, which is based on swarm intelligence. Additional computational intelligence tools, e.g., artificial neural networks, were utilized to create surrogate models of the objective functions and constraints. The results demonstrate the effectiveness and efficiency of the proposed optimization framework when applied to simulation-based engineering design optimization problems.