摘要：
We consider the assignment of program tasks to processors in distributed
computing systems such that system cost is minimized and resource
constraints are satisfied. Several formulations for this task assignment
problem (TAP) have been proposed in the literature. Most of these
TAP formulations, however, are NP-complete and thus finding exact
solutions is computationally intractable. Recently, some approximation
methods like simulated annealing have been proposed, and simulation
results exhibited the potential to solve the TAP using metaheuristics.
In order to better understand the strengths and weaknesses of various
metaheuristics applied to the TAP, we first propose two alternative
metaheuristics— one using genetic algorithm and the other
reinforcement learning algorithm—as well as their implementation
details. Extensive computational evidences of the two heuristic
algorithms against that of simulated annealing are presented, compared
and discussed. Based on these experimental results, a hybrid strategy
employing both metaheuristics is then proposed in order to solve
the TAP more effectively and efficiently.
