【摘要】 传统多目标优化问题通常是以加权或约束方式将其转换为单一目标,未能反映多目标间的复杂关系,不利于随时根据需求作出有效的决策。为了更合理地确定切削用量,采用多目标粒子群算法首先求得问题的pareto最优前沿,经过后期多准则决策得到满足不同要求下的最优方案。采用这种先寻优后决策的方法,能有效弱化先验知识不足的影响,较传统多目标优化方法更为实用有效。并经与多目标遗传算法比较,多目标粒子群算法具有更优良的性能。更多还原

【Abstract】 Traditional multi-objective optimization,through simply converting to a single goal,often fails to reflect the more complex relationship between goals,and decision-make effectively at any time on demand.For selecting optimum cutting parameters,MOPSO(Multi-Objective Particle Swarm)is applied to obtain the pareto optimum front,and then the best answer is got according to multi-criteria decision.The method,making decision after searching optimum solutions,is more applicable and effective and can weak designer’s transcendental information deficiency problem.Compared with MOGA(Multi-Objective Genetic Algorithm),MOPSO showed better performance.更多还原