Genetic algorithms applied to real time multiobjective optimization problems

Abstract

Genetic algorithms (GAs) are often well-suited for multi-objective optimization problems. In this work, multiple objectives pertaining to the THUNDER software (a very large military campaign simulation model) were used to optimize the war results obtained from the software. It is a stochastic, two-sided, analytical Monte-Carlo simulation of military operations. The simulation is subject to internal unknown noises. Due to these noises and to the discreteness in the simulation program, a GA approach has been applied to this multi-objective optimization problem. This method is capable of searching for multiple solutions concurrently in a single run. Transforming this problem to a form that is suitable for the direct implementation of GA was the major challenge that was achieved. Three different kinds of fitness assignment methods were implemented, and the best one was chosen. The THUNDER software may be considered as a black box, since very little information about its internal dynamics was known. The problem with the THUNDER software is its expensive running time. In order to optimize the time involved with the THUNDER software, autocorrelation techniques were used to reduce the number of THUNDER runs. Furthermore, the GA parameters were set optimally to yield smoother and faster fitness convergence. From these results, the GA was shown to perform well for this multi-objective optimization problem and was effectively able to allocate force power for the THUNDER software.