Accession Number:

ADA495422

Title:

Hybrid Robust Multi-Objective Evolutionary Optimization Algorithm

Descriptive Note:

Final performance rept. 1 Mar 2006-30 Nov 2008

Corporate Author:

FLORIDA INTERNATIONAL UNIV MIAMI DEPT OF MECHANICAL ENGINEERING

Personal Author(s):

• Dulikravich, George S.

Report Date:

2009-03-10

Pagination or Media Count:

38.0

Abstract:

A hybrid robust multi-objective optimization algorithm and accompanying software were developed that 1 utilize several evolutionary optimization algorithms, a set of rules for automatic switching among these algorithms in order to accelerate the overall convergence and avoid termination in a local minimum, 2 involve development of algorithms for multi-dimensional response surfaces metamodels that are fast, accurate and robust by utilizing wavelet-based artificial neural networks, polynomials of radial basis functions, and multi-layer self adapting maps, 3 involve an algorithm based on Bayesian statistics using Kalman filters and Monte Carlo Markov chains that will enhance robustness of the multi-objective optimization algorithm by accounting for uncertainties in the input data and in the accuracy of the evaluation methods for the multiple objective functions. The hybrid evolutionary multi-objective optimization algorithm was also thoroughly tested on a number of standard test problems with two and three simultaneous objectives where the Pareto surface could be continuous and discontinuous. The hybrid optimizer was programmed in such a way that it can be transportable to any single-processor or parallel processor.

Descriptors:

• *PARALLEL PROCESSORS
• *OPTIMIZATION
• *ALGORITHMS
• MARKOV PROCESSES
• STATISTICS
• KALMAN FILTERING
• MULTIPURPOSE
• COMPUTER PROGRAMS
• POLYNOMIALS
• CONVERGENCE
• NEURAL NETS
• SWITCHING
• MONTE CARLO METHOD

Subject Categories:

• Numerical Mathematics
• Statistics and Probability
• Computer Programming and Software

Distribution Statement:

APPROVED FOR PUBLIC RELEASE