Shuffled Frog Leaping Algorithm Based on Fuzzy Threshold Compensation
Liu Liqu · Jisuanji gongcheng · 2014
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].