Genetic Algorithms Applied to Real Time Multiobjective Optimization Problems

Genetic algorithms are often well suited for multiobjective optimization problems. In this work, multiple objectives pertaining to the THUNDER software were concerned to optimize the war results obtained from the software. It is a stochastic, two-sided, analytical simulation of military operations. The simulation is subject to internal unknown noises. Due to these noises and discreetness in the simulation program, GA approach has been applied to this multiobjective optimization problem. This method is capable of searching for multiple solutions concurrently in a single run. Transforming this multiobjective optimization problem to a form suitable for 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 less information about its internal dynamics was known. The problem with THUNDER software is expensive running time. In order to optimize the time involved with 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, GA was shown to perform well for this multi-objective optimization problem and was effectively able to allocate force power for the THUNDER software.