CAJ | 학술논문

种群分割是混合蛙跳算法的重要组成部分,不同的种群分割方法对混合蛙跳算法的收敛速度的影响不同.文中首先在原始混合蛙跳算法基础上,提出一种新的种群分割方法,使得每个族群中的个体适应度趋近均衡.然后结合Yen算法的偏离路径思想提出一种新的学习策略,对算法迭代方法进行改进.改进后的混合蛙跳算法适用于求解K最短路径问题.最后对改进后的算法进行仿真实验.首先选取单调递归的Dijkstra算法对改进算法可行性进行验证,结果表明改进后的算法是可行的;再选取遗传算法与改进算法进行对比,结果表明改进后的算法在寻优精确度、时间效率和稳定性方面均优于遗传算法.
충군분할시혼합와도산법적중요조성부분,불동적충군분할방법대혼합와도산법적수렴속도적영향불동.문중수선재원시혼합와도산법기출상,제출일충신적충군분할방법,사득매개족군중적개체괄응도추근균형.연후결합Yen산법적편리로경사상제출일충신적학습책략,대산법질대방법진행개진.개진후적혼합와도산법괄용우구해K최단로경문제.최후대개진후적산법진행방진실험.수선선취단조체귀적Dijkstra산법대개진산법가행성진행험증,결과표명개진후적산법시가행적;재선취유전산법여개진산법진행대비,결과표명개진후적산법재심우정학도、시간효솔화은정성방면균우우유전산법.
Population segmentation is an important part of shuffled frog leaping algorithm of which convergence rate is affected by differ-ent population segmentation methods. A new segmentation method on the basis of the original SFLA is proposed in this paper so that the individual fitness in each ethnic group approaches equilibrium. Then present a new learning strategy combined with the deviated path idea of the Yen algorithm to improve the algorithm' s iterative method. The improved shuffled frog leaping algorithm is suitable to solve the K-shortest path problem. Finally,do the simulation for the improved algorithm. Firstly,the monotonous recursive Dijkstra algorithm is se-lected to verify the feasibility of the improved algorithm and the results show that the improved algorithm is feasible. And then the genetic algorithm is selected to compare with the improved algorithm,and the results show that the improved algorithm' s optimization accuracy, time efficiency and stability is superior to the genetic algorithm.