CAJ | 학술논문

粒子群算法是一种基于群体的智能算法，具有较强的全局搜索能力，并能通过对一定数量粒子的迭代运算获得问题的全局最优解。将粒子群算法应用于多峰值函数优化中可以避免常规方法难以同时搜索出多个极值而陷于局部极值的问题。基于matlab平台的仿真实验中，引入粒子群初始化位置拥挤距离检测，并在peaks函数上进行测试，可以有效实现全局和局部搜索，并能较好地保持粒子的多样性，从而获得多峰值函数的最优解。
입자군산법시일충기우군체적지능산법，구유교강적전국수색능력，병능통과대일정수량입자적질대운산획득문제적전국최우해。장입자군산법응용우다봉치함수우화중가이피면상규방법난이동시수색출다개겁치이함우국부겁치적문제。기우matlab평태적방진실험중，인입입자군초시화위치옹제거리검측，병재peaks함수상진행측시，가이유효실현전국화국부수색，병능교호지보지입자적다양성，종이획득다봉치함수적최우해。
PSO (Particle Swarm Optimization) is an intelligence algorithm based on swam. It is characteristic of strong global searching ability and able to reach for globally optimal solution through iterative operations of a certain quantity of particles. The application of PSO in multiple hump function can avoid problems caused by conventional methods which are able to search no multiple extrema simultaneously but relative extrema. In simulation experiments based on matlab, it is appropriate to take in crowding distance detection of particle swam" initial position and peaks function testing, which are able to achieve global and local searching, maintain the diversity of particles, and there- fore to realize the optimal solution of multiple hump function.