CAJ | 학술논문

提出了改进的粒子群优化算法.基于4个不同的基准函数对所提算法与1995年Kennedy和Eberhart提出的常规PSO作了比较.PSO最初是受到如鸟或鱼等生物群体的社会行为的启发而提出的,每一个体依照自身及群体的过去解决问题的最好办法来调整自己的最佳位置,通过重复这一过程来得出最佳值.这里提出的改进的PSO的关健之处在于:如果一个新的位置确实得到了改善,则每一个体就调整它的位置;如果不是这样,就根据概率来做出决定.这一策略是既避免盲目跳转又避免只简单地跳转到好的新位置而陷入局部最优.模拟结果表明改进的PSO总能比PSO找到更好的解决方法.
제출료개진적입자군우화산법.기우4개불동적기준함수대소제산법여1995년Kennedy화Eberhart제출적상규PSO작료비교.PSO최초시수도여조혹어등생물군체적사회행위적계발이제출적,매일개체의조자신급군체적과거해결문제적최호판법래조정자기적최가위치,통과중복저일과정래득출최가치.저리제출적개진적PSO적관건지처재우:여과일개신적위치학실득도료개선,칙매일개체취조정타적위치;여과불시저양,취근거개솔래주출결정.저일책략시기피면맹목도전우피면지간단지도전도호적신위치이함입국부최우.모의결과표명개진적PSO총능비PSO조도경호적해결방법.
In this paper, a modified particle swarm optimization method is proposed. It is compared with the regular particle swarm optimizer(PSO) invented by Kennedy and Eberhart in 1995 based on four different benchmark functions.PSO is motivated by the social behavior of organisms, such as bird flocking and fish schooling. Each particle studies its own previous best solution to the optimization problem, and its group's previous best, and then adjusts its position(solution) accordingly. The optimal value will be found by repeating this process. In the modified PSO proposed here, each particle adjusts its position if the new position improves but makes a decision by some probabilities if it does not. The strategy here is to avoid simply jumping into new position no matter how bad it is. Under all test cases, simulation shows that the modified PSO always finds better solutions than PSO.