Paper Info
Title
Efficient Multi-Objective Optimization With Fitness Landscape - A Special Application To The Optimal Design Of Alloy-Steels
Abstract
This paper reports on an efficient algorithm for locating the 'optimal' solutions for multi-objective optimization problems by combining a state-of-the-art optimizer with a fitness model-estimate. This hybrid framework is introduced to illustrate how to make sufficient use of an approximate model, which includes a 'controlled' process and an 'uncontrolled' process during the search. With the inclusion of such approximate model in the optimization block, a global reseeding strategy based on previous data is also applied to improve the ability of the multi-objective optimizer to find global set of solutions ('pareto' solutions). To this effect, the popular algorithm, NSGA-II, and a Multi-Layer Perceptron Neural Network (MLP) are combined synergetically to show details of such processing. Furthermore, a simple (but no simpler) method for selecting the 'training' data necessary for eliciting the fitness landscape model is suggested to address what are now a common engineering problems, in particular those associated with sparse data distributions and objectives converging at significantly different speeds. To test the validity of the proposed multi-objective scheme, a series of simulation experiments, using well-know benchmark functions, are conducted and are compared to those carried-out while using the original NSGA-II and SPEA-2, under similar conditions. The proposed method is also applied to the 'optimal' design of alloy steels in terms of chemical compositions and processing conditions and is shown to perform very well.
Year
DOI
Venue
2010
10.1109/CEC.2010.5586010
2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
Keywords
Field
DocType
sparse data,chemical composition,fitness landscape,predictive models,convergence,multiobjective optimization,approximation algorithms,optimal design,simulation experiment,neural network,multi objective optimization,training data,multi layer perceptron,optimization
Approximation algorithm,Mathematical optimization,Fitness landscape,Computer science,Multi-objective optimization,Optimal design,Artificial intelligence,Artificial neural network,Perceptron,Optimization problem,Machine learning,Pareto principle
Conference
Citations 
PageRank 
References 
0
0.34
13
Authors
2
Name
Order
Citations
PageRank
Shen Wang1228.34
Mahdi Mahfouf223533.17