Chaotic Simulated Annealing for Task Allocation in a Multiprocessing System

Two different variations of chaotic simulated annealing were applied to combinatorial optimization problems in multiprocessor task allocation. Chaotic walks in the solution space were taken to search for the global optimum or "good enough" task-to-processor allocation solutions. Chaotic variables were generated to set the number of perturbations made in each iteration of a chaotic simulated annealing algorithm. In addition, parameters of a chaotic variable generator were adjusted to create different chaotic distributions with which to search the solution space. The results show a faster convergence time than conventional simulated annealing when the solutions are far apart in the solution space.