Solving Multiprocessor Scheduling Problem Using Multi-objective Mean Field Annealing

Abstract

Multiprocessor scheduling problem is one of the most important issues regarding to parallel programming and distributed system environments. Multiprocessor scheduling is known as a NP-hard problem, hence, applying an exact solution method is not recommended at all. Single-objective type of multiprocessor scheduling problem has already been solved by evolutionary algorithms like genetic algorithms, ant colony optimization, particle swarm optimization, mean field annealing and so on. This paper presents a mean field annealing approach for solving the multi-objective type of this problem. We introduce multi-objective multiprocessor scheduling problem with three objectives and then solve it using mean field annealing approach. Finally, the proposed algorithm is tested over some benchmarks and its effectiveness is compared to NSGA2 and MOGA algorithms. Obtained results show that mean field annealing method leads better Pareto fronts within reasonable computation times.