OPTIMIZATION OF PREVENTIVE MAINTENANCE PERIOD BASED ON HYBRID SWARM INTELLIGENCE
It was analyzed that there would be some problems such as parameters value settings etc when the ant colony optimization (ACO) was applied in the PM period optimization process. Thus it was put forward that the particle swarm optimization (PSO) was brought into the ACO algorithm to form a new hybrid swarm optimization: PS_ACO (Particle Swarm and Ant Colony Optimization). This new hybrid algorithm can modify the optimization rules and the geographic division of ACO, and can partly solve some problems about the worse precision and inefficient optimization coming from unsuitable parameters values settings 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, precision, and relatively weaken the influence of parameters value settings to the optimization result.
Preventive Maintenance (PM) Swarm Intelligence Ant Colony Optimization (ACO) Particle Swarm Optimization (PSO) Maintenance Period
XUE Jia MA SaSa ZHANG Hong
Optics and Electronic Engineering Department, Ordnance Engineering College, Shijiazhuang 050003, Chi Xian Satellite Control Center, Xian, 710043, China
国际会议
北京
英文
1812-1817
2008-10-27（万方平台首次上网日期，不代表论文的发表时间）