Optimization of preventive maintenance period based on hybrid swarm intelligence

Abstract

It was analyzed that there were some problems such as parameters value settings etc when the ant colony optimization (ACO) was applied in the PM period optimization process. And it was put forward that the particle swarm optimization (PSO) was brought into the ACO algorithm to form a new hybrid swarm optimization: Particle Swarm and Ant Colony Optimization (PS_ACO). This new hybrid algorithm can modify the optimization rules and geographic division of ACO, and can partly solve some problems about the worse precision and inefficient optimization coming from unsuitable parameters values setting of ACO and random PM period solution. This PS_ACO algorithm was applied in the optimization process of series-parallel system PM period. The experimental data shows that: the PS_ACO can partly improve the optimization efficiency and precision, and relatively weaken the influence of parameters value settings to the optimization result.