计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2010年
4期
52-53,75
,共3页
粒子群优化算法%旅行商问题%贪婪算法%交叉%变异
粒子群優化算法%旅行商問題%貪婪算法%交扠%變異
입자군우화산법%여행상문제%탐람산법%교차%변이
Particle Swarm Optimization(PSO)%Traveling Salesman Problem(TSP)%greedy algorithm%crossover%mutation
针对粒子群优化算法易陷入局部极值的缺点,提出一种改进粒子群算法,该算法借鉴贪婪算法的思想初始化种群,利用两个种群同时寻优,并将遗传算法中交叉和变异操作引入其中,实现种群间的信息共享.用14点TSP标准数据对算法性能进行了测试,结果表明该算法能够较早跳出局部最优,具有较高的收敛速度和收敛率.
針對粒子群優化算法易陷入跼部極值的缺點,提齣一種改進粒子群算法,該算法藉鑒貪婪算法的思想初始化種群,利用兩箇種群同時尋優,併將遺傳算法中交扠和變異操作引入其中,實現種群間的信息共享.用14點TSP標準數據對算法性能進行瞭測試,結果錶明該算法能夠較早跳齣跼部最優,具有較高的收斂速度和收斂率.
침대입자군우화산법역함입국부겁치적결점,제출일충개진입자군산법,해산법차감탐람산법적사상초시화충군,이용량개충군동시심우,병장유전산법중교차화변이조작인입기중,실현충군간적신식공향.용14점TSP표준수거대산법성능진행료측시,결과표명해산법능구교조도출국부최우,구유교고적수렴속도화수렴솔.
In allusion to particle swarm optimization being prone to get into local minimum,an improved particle swarm optimization algorithm is proposed.The algorithm draws on the thinking of the greedy algorithm to initialize the particle swarm.Two swarms are used to optimize synchronously,and crossover and mutation operators in genetic algorithm are introduced into the new algorithm to realize the sharing of information among swarms.This paper tests the algorithm with a Travehng Salesman Problem with 14 nodes.The result shows that the algorithm can break away from local minimum earlier and it has high convergence speed and convergence ratio.
