ABSTRACT
This dissertation presents a new multiple objective optimization
algorithm that is capable of solving for the entire Pareto set
in one single optimization run. The multi-objective complex
evolution (MOCOM-UA) procedure is based on the following three
concepts: (1) population, (2) rank-based selection, and (3)
competitive evolution. In the MOCOM-UA algorithm, a population
of candidate solutions is evolved in the feasible space to search
for the Pareto set. Ranking of the population is accomplished
through Pareto ranking, where all points are successfully placed
on different Pareto fronts. Competitive evolution consists of
selecting subsets of points (including all worst points in the
population) based on their ranks and moving the worst points
toward the Pareto set using the newly developed multiobjective
simplex (MOSIM) procedure. Test analysis on the MOCOM-UA algorithm
is accomplished on mathematical problems of increasing complexity
and based on a bi-criterion measure of performance. The two
performance criteria used are (1) efficiency, as measured by
the ability of the algorithm to converge quickly and (2) effectiveness,
as measured by the ability of the algorithm to locate the Pareto
set. Comparison of the MOCOM-UA algorithm against three multiobjective
genetic algorithms (MOGAs) favors the former. In a realistic
application, the MOCOM-UA algorithm is used to calibrate the Soil
Moisture Accounting model of the National Weather Service River
Forecasting Systems (NWSRFS-SMA). Multiobjective calibration
of this model is accomplished using two bi-criterion objective
functions, namely the Daily Root Mean Square-Heteroscedastic
Maximum Likelihood Estimator (DRMS, HMLE) and rising limb-falling
limb (RISE, FALL) objective functions. These two multiobjective
calibrations provide some interesting insights into the influence
of different objectives in the location of final parameter values
as well as limitations in the structures of the NWSRFS-SMA model.