%A YI Yunfei, MIAO Jian, LIN Guolong, YIN Zhi
%T Particle network optimization algorithm based on Newtonian mechanics and game theory model
%0 Journal Article
%D 2017
%J Journal of Shandong University(Engineering Science)
%R 10.6040/j.issn.1672-3961.1.2016.070
%P 28-36
%V 47
%N 1
%U {http://gxbwk.njournal.sdu.edu.cn/CN/abstract/article_1495.shtml}
%8 2017-02-20
%X In order to overcome the bad convergence and accuracy of the standard particle swarm optimization algorithm in solving the high dimensional TSP problem, each particle was given their own character, such as mass and acceleration was given, and Newton second law with a Poisson distribution was introduced to dynamic control the particle acceleration. In addition, the particle dimensions were divided into advantages and disadvantages section based on its similar to reduce the dimension of update, normally update would only change the disadvantage parts to keep and extend their advantage parts so that it could improve the convergence speed, when the disturbance it would update its advantage parts to away from the current network so that it could jump out of local optimum,when particles collide, opposition-based learning strategy was used to deal with disadvantage section, and a better model of slow convergence was selected. Finally, via numerous simulations of TSPLIB and comparison with other classical algorithms, the results showed that the improved algorithm had the feature of high efficiency, low computational complexity and strong convergence, which were especially crucial for the functioning of large-scale distribution problems. Research results could provide a reference for the study on intelligent algorithms in solving optimization problems, such as how to improve the accuracy and speed up the convergence. 山 东 大 学 学 报 (工 学 版)第47卷 - 第1期易云飞,等:基于牛顿力学和博弈论模型的粒子网络优化算法 \=-