Using particle swarm optimization to solve resource-constrained scheduling problems

Abstract

This investigation introduced a particle swarm optimization (PSO) approach to solve the multi-processor resource-constrained scheduling problems. There are two new rules are proposed and evaluated, named anti-inertia solution generation rule and bidirectional searching rule of PSO. The anti-inertia solution generation rule enables some jobs with anti-inertia velocity used to decide the start processing time, and escaping from local minimum. The bidirectional searching rule combines forward and backward scheduling to extend the search solution space. These two suggested rules applied in PSO scheme are capable of finding global minimum. The simulation results reveal that the proposed approach in this investigation can successfully solve scheduling problems.