  In a distributed system, Task Assignment Problem (TAP) is a key factor for
obtaining efficiency. TAP illustrates the appropriate allocation of tasks to
the processor of each computer. In this problem, the proposed methods up to now
try to minimize Makespan and maximizing CPU utilization. Since this problem is
NP-complete, many genetic algorithms have been proposed to search optimal
solutions from the entire solution space. Disregarding the techniques which can
reduce the complexity of optimization, the existing approaches scan the entire
solution space. On the other hand, this approach is time-consuming in
scheduling which is considered a shortcoming. Therefore, in this paper, a
hybrid genetic algorithm has been proposed to overcome this shortcoming.
Particle Swarm Optimization (PSO) has been applied as local search in the
proposed genetic algorithm in this paper. The results obtained from simulation
can prove that, in terms of CPU utilization and Makespan, the proposed approach
outperforms the GA-based approach.
