摘要
研究离散机床制造系统加工节能优化问题,针对离散机床制造系统能耗大,能效低的现状,通过优化加工参数,减少加工时间从而减少能量消耗。传统的一些方法在解决加工参数优化问题时,出现收敛速度慢,求解精度低等缺陷,导致得到的加工参数不是最优。为此提出一种随机游走多目标粒子群算法,以切削速度、进给量、切削深度为优化变量,以能源效率为优化目标,经过迭代寻优计算得到Pareto前沿解,并采用层次分析决策方法从Pareto前沿解中选择最优加工参数。仿真结果表明,提出的算法可平衡全局和局部寻优能力,提高收敛精度,求解的加工参数使得加工时间更少,效率更高,能耗更少,并与教学算法相比节能15.8%。
A random walk muhi-objective particle swarm optimization algorithm is proposed. Taking the cutting speed, feed speed and cutting depth as the optimization variables, the energy efficiency as the optimization objective, the Pareto front solution is obtained by iterative optimization and the analytic hierarchy process is adopted to select the optimal processing parameters from the Pareto front. Simulation results show that the proposed algorithm can balance the global and local optimization ability, improve the convergence accuracy and solve the processing parameters to make less processing time, higher efficiency, less energy consumption, and energy saving 15.8% compared with the teaching - learning optimization algorithm.
出处
《计算机仿真》
CSCD
北大核心
2016年第9期219-224,共6页
Computer Simulation
基金
国家高技术研究发展计划(863计划)(2014AA041505)
国家自然科学基金资助项目(61572238)
关键词
多目标优化
粒子群算法
层次分析法
能效
Multi-objective optimization
Particle swarm algorithm
Analytic hierarchy process （AHP）
Energy efficiency
