Dynamic multiobjective optimization of war resource allocation using adaptive genetic algorithms

Abstract

Genetic algorithms (GA) are often well suited for multiobjective optimization problems. The major objective of this research is to optimize the war resource allocations of sorties, for a given war scenario, using genetic algorithms. The war is simulated using THUNDER software. THUNDER software is a stochastic, two-sided, analytical simulation of campaign-level military operations. The simulation is subject to internal unknown noises similar to real war cases. Due to these noises and discreteness in the simulation, as well as in real wars, an adaptive GA approach has been applied to solve this multiobjective optimization problem. Transforming this multiobjective optimization problem to a form suitable for direct implementation of GA was a major accomplishment of this research. A suitable fitness function was chosen after careful research and testing on the GA. Furthermore, the GA parameters were adaptively set to yield smoother and faster fitness convergence. Two fuzzy logic mechanisms were used to adapt the GA parameters. In the first mechanism, the mutation and crossover rates were changed adaptively. In the second mechanism, the fitness function coefficients are changed dynamically in each run. Testing results showed that the adaptive GA outperforms the conventional GA search in this multiobjective optimization problem and was effectively able to allocate forces for war scenarios.