CAJ | 학술논문

传统的粒子群算法通过粒子的适应值大小来判断粒子好坏，作为智能体，粒子本身有决策能力，而这在粒子群算法中并没有体现出来。因此提出了一种新的粒子好坏的判断标准--适应值变化率。通过个体决策的方法和适应值变化率，利用粒子位置与对应的适应值信息对粒子群算法中的个体历史最优位置和认知系数进行决策。采用几个常用的测试函数进行仿真实验，与其他改进的粒子群算法相比，结果表明该算法具有更好的性能。
전통적입자군산법통과입자적괄응치대소래판단입자호배，작위지능체，입자본신유결책능력，이저재입자군산법중병몰유체현출래。인차제출료일충신적입자호배적판단표준--괄응치변화솔。통과개체결책적방법화괄응치변화솔，이용입자위치여대응적괄응치신식대입자군산법중적개체역사최우위치화인지계수진행결책。채용궤개상용적측시함수진행방진실험，여기타개진적입자군산법상비，결과표명해산법구유경호적성능。
Traditional particle swarm optimization can determine the quality of the particle by adaptive value. As an intelli-gent agent,each particle has the ability of decision-making,but it is not reflected in the PSO. Therefore,change rate of adap-tive value,a new judgement standard for particle evaluation is proposed. The particles position and corresponding information of the adaptive value are adopted to decide individual optimal position in history and cognitive coefficient in the PSO with the help of individual decision-making method and change rate of adaptive value. Several commonly-used test functions were used in the simulation experiments. The results shows that the algorithm has a better performance than other improved PSOs.