Modified Binary Particle Swarm optimization algorithm application to real-time task assignment in heterogeneous multiprocessor

• Three optimization techniques are compared for heterogeneous multiprocessor scheduling.
• Modified BPSO performs better than Novel BPSO and ACO.
• ACO performs better than Novel BPSO.
• Energy consumed is reduced for feasible solutions by using Local Energy algorithm.
• Energy consumed and CPU time taken for different techniques are discussed.

Task assignment in a heterogeneous multiprocessor is a NP-hard problem, so approximate methods are used to solve the problem. In this paper the Modified Binary Particle Swarm Optimization (Modified BPSO) algorithm and Novel Binary Particle Swarm (Novel BPSO) Optimization are applied to solve the real-time task assignment in heterogeneous multiprocessor. The problem consists of a set of independent periodic task, which has to be assigned to a heterogeneous multiprocessor without exceeding the utilization bound. The objective is to schedule maximum number of tasks with minimum energy consumption. The execution times and deadlines of the tasks are assumed to be known. Here Modified BPSO performance is compared with Novel BPSO and Ant Colony Optimization algorithm (ACO). Experimental results show that Modified BPSO performs better than Novel BPSO and ACO for consistent utilization matrix and ACO performs better than Modified BPSO and Novel BPSO for inconsistent utilization matrix.