MULTI-OBJECTIVE OPTIMIZATION OF BLAST SIMULATION USING SURROGATE MODEL Toshihiro Tsuga, M.S. George Mason University, 2007 Thesis Director: Dr. Rainald Löhner A multi objective optimization approach using a Kriging model coupled with a Multi Objective Genetic Algorithm (MOGA) is applied to a blast damage maximization problem composed of two objectives, namely number of casualties and damage to ...

In this paper a hybrid parallel multi-objective genetic algorithm is proposed for solving 0/1 knapsack problem. Multiobjective problems with non-convex and discrete Pareto front can take enormous computation time to converge to the true Pareto front. Hence, the classical multi-objective genetic algorithms (MOGAs) (i.e., nonParallel MOGAs) may fail to solve such intractable problem in a reasonab...

In this paper a hybrid parallel multi-objective genetic algorithm is proposed for solving 0/1 knapsack problem. Multi-objective problems with non-convex and discrete Pareto front can take enormous computation time to converge to the true Pareto front. Hence, the classical multi-objective genetic algorithms (MOGAs) (i.e., non-Parallel MOGAs) may fail to solve such intractable problem in a reason...

Finding a best designed experiment based on balancing several competing goodness measures of the design is becoming more important in many applications. The Pareto front approach allows the practitioner to understand trade-offs between alternatives and make more informed decisions. Efficient search for the front is a key to successful use and broad adoption of the method. A substantial computat...

Pareto front optimization has been commonly used for balancing trade-offs between different estimated responses. Using maximum likelihood or least squares point estimates or the worst case confidence bound values of the response surface, it is straightforward to find preferred locations in the input factor space that simultaneously perform well for the various responses. A new approach is propo...

This paper proposes a new multi-objective genetic algorithm, called GAME, to solve constrained optimization problems. GAME uses an elitist archive, but it ranks the population in several Pareto fronts. Then, three types of fitness assignment methods are defined: the fitness of individuals depends on the front they belong to. The crowding distance is also used to preserve diversity. Selection is...

Given a first set of observations from a design of experiments sampled randomly in the design space, the corresponding set of non-dominated points usually does not give a good approximation of the Pareto front. We propose here to study this problem from the point of view of multivariate analysis, introducing a probabilistic framework with the use of copulas. This approach enables the expression...

In multi-objective optimization the hypervolume indicator is a measure for the size of the space within a reference set that is dominated by a set of μ points. It is a common performance indicator for judging the quality of Pareto front approximations. As it does not require a-priori knowledge of the Pareto front it can also be used in a straightforward manner for guiding the search for finite ...