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


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a495422.pdf


Report Date : 10 Mar 2009


Pagination or Media Count : 38


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