CAJ | 학술논문

针对随机性优化算法寻优结果不可重复的特点，为该类优化算法提供了一种定量对比评价算法有效性的方法。该方法针对单个或一组测试函数的多次优化结果进行统计分析，得到一个能够在概率意义上定量表征不同随机性算法求解单个或一组测试函数的有效性优劣关系的因子。利用该方法，对采用同步或异步全局最优粒子信息更新模式的两种标准粒子群优化算法（PSO）版本进行有效性对比评价，给出了同步和异步模式PSO算法求解无约束单目标连续变量优化问题的有效性优劣关系。
침대수궤성우화산법심우결과불가중복적특점，위해류우화산법제공료일충정량대비평개산법유효성적방법。해방법침대단개혹일조측시함수적다차우화결과진행통계분석，득도일개능구재개솔의의상정량표정불동수궤성산법구해단개혹일조측시함수적유효성우렬관계적인자。이용해방법，대채용동보혹이보전국최우입자신식경신모식적량충표준입자군우화산법（PSO）판본진행유효성대비평개，급출료동보화이보모식PSO산법구해무약속단목표련속변량우화문제적유효성우렬관계。
To compare effectiveness of different stochastic optimization algorithms quantitatively, a method is proposed, which is based on statistical analysis of multiple independent results of those algorithms solving a set of typical test samples. Probability quantifying the relative superiority of effectiveness of two algorithms is conducted. Using this method, effec-tiveness comparison of different stochastic optimization algorithms is easy to make. Further, this method is implemented on two patterns of standard Particle Swarm Optimization(PSO)algorithm, in which the global best particle’s information is updated synchronously as well as asynchronously. As a result, a relatively effective pattern of standard PSO algorithm solving unconstrained single-objective optimization problems is advised.