Hybrid Metaheuristic Algorithm for Real Time Task Assignment Problem in Heterogeneous Multiprocessors

The assignments of real time tasks to heterogeneous multiprocessors in real time applications are very difficult in scenarios that require high performance. The main problem in the heterogeneous multiprocessor system is task assignment to the processors because the execution time for each task varies from one processor to another. Hence, the problem of finding a solution for task assignment to heterogeneous processor without exceeding the processors capacity in general is an NP hard problem. In order to meet the constraints in real time systems, a Hybrid Max-Min Ant colony optimization algorithm (HMMAS) is proposed in this paper. Max-Min Ant System (MMAS) is extended with a local search heuristic to improve task assignment solution. The Local Search has resulted in maximizing the number of tasks assigned as well as minimizing the energy consumption. The performance of the proposed algorithm H-MMAS is compared with the Modified Binary Particle Swarm Optimization algorithm (BPSO), Ant Colony Optimization (ACO), MMAS algorithms in terms of the average number of task assigned, normalized energy consumption, quality of solution and average Central Processing Unit (CPU) time. From the experimental results, the proposed algorithm has outperformed MMAS, Modified BPSO and ACO for consistency matrix. In case of inconsistency matrix H-MMAS performed better than Modified BPSO, similar to ACO and MMAS, but there is an improvement in the normalized energy consumption.