摘要： 针对混合蛙跳算法(SFLA)求解复杂问题时收敛速度慢、优化精度低的缺点，提出一种基于模糊阈值补偿的混合蛙跳算法(FTCSFLA)。在SFLA的基础上，采用模糊分组方法对青蛙分组并改进局部搜索的扰动策略。在族群中定义模糊隶属度、隶属度阈值和补偿系数，利用邻域青蛙之间的分布程度衡量某一青蛙的模糊隶属度。在一次局部搜索中，对族群最差个体按模糊隶属度和隶属度阈值关系给出2种更新方法，设置相应的补偿系数。实验结果表明，隶属度阈值为0.9的FTCSFLA其收敛精度、速度均优于SFLA和隶属度阈值为0.5的FTCSFLA，当隶属度阈值取值在(0.5,0.9]之间时，FTCSFLA的性能达到最优。
关键词:
混合蛙跳算法,
模糊隶属度,
隶属度阈值,
补偿系数,
模糊分组,
扰动策略,
优化性能
Abstract: To solve the problem of slow convergence speed and low optimization precision of Shuffled Frog Leaping Algorithm (SFLA) in solving complex problems, a Shuffled Frog Leaping Algorithm Based on Fuzzy Threshold Compensation(FTCSFLA) is proposed. The fuzzy grouping idea is introduced to divide different frogs into fuzzy groups, and disturbance strategy in a local search is improved based on the basic SFLA. Each fuzzy group is defined with a total membership threshold and a total compensation coefficient, and each frog is defined with a fuzzy membership, which is scaled with the distribution degree of neighborhood frogs. In a local search, the worst individual is updated by two methods in each group, which is partitioned according to the relation between fuzzy membership and membership threshold. In two methods, a compensation coefficient is set to give a unify expression. Experimental results show that the convergence precision and speed of FTCSFLA which membership threshold is 0.9 is better than SFLA and FTCSFLA which membership threshold is 0.5. The evolution curve shows that the convergence precision and speed of FTCSFLA is the optimum when its membership threshold is between (0.5, 0.9].
Key words:
Shuffled Frog Leaping Algorithm(SFLA),
fuzzy membership,
membership threshold,
compensation coefficient,
fuzzy grouping,
disturbance strateg,
optimization performance
中图分类号: