CAJ | 학술논문

针对岛屿模型的并行粒子群算法没有根本改变粒子速度更新的问题，提出一种自适应惯性权重的分组并行粒子群优化算法。该算法在迭代过程中能自适应地选择加入分组的数量，同时对各组粒子的惯性权重按照组内最优位置的变化进行自适应调整。各组运用多线程技术并行处理，粒子间采用新的信息共享的方式。仿真结果证实，该算法具有较高的收敛速度和收敛精度。
침대도서모형적병행입자군산법몰유근본개변입자속도경신적문제，제출일충자괄응관성권중적분조병행입자군우화산법。해산법재질대과정중능자괄응지선택가입분조적수량，동시대각조입자적관성권중안조조내최우위치적변화진행자괄응조정。각조운용다선정기술병행처리，입자간채용신적신식공향적방식。방진결과증실，해산법구유교고적수렴속도화수렴정도。
No fundamental change in the particle velocity update for the island model parallel particle swarm optimiza-tion, a grouping parallel particle swarm optimization with adaptive inertia weight is proposed in this paper. This algorithm can adaptively choose the number of particles joining the group in an iterative process, besides, it can adjust the inertia weight of each group adaptively in accordance with the change of the optimal position. Each grouping uses multithreading technology to parallel processing and new information sharing mode is used between particles. The simulation results show that the algorithm has higher convergence speed and convergence precision.