The Research On Swarm Intelligent Algorithms And Its Application To Determine Fuzzy Measures

Inspired by natural animal group behavior, swarm intelligent algorithms used to simulate animal behavior are presented. Swarm intelligent algorithms are referred to a class of algorithms. The representative algorithms are called particle swarm algorithm, ant colony algorithm and artificial fish warm algorithm. Since the emergence of swarm intelligence algorithms, many scholars have began to pay attention and study. These algorithms are applied to particular problems and achieve good results. How to determine fuzzy measure is a basic problem when applying fuzzy measure and fuzzy integral. This problem can be translated to an optimization, so it can be solved by soft computing techniques. In this paper, we use swarm intelligent algorithm to identify fuzzy measures from data. We verify the feasibility and the advantages: easy implement, good robustness, etc. what is more, through the analysis and comparison of the algorithms on theory and experiment, we conclude the performance differences of particle swarm algorithm, ant colony algorithm and artificial fish warm algorithm: particle swarm algorithm owns fast convergence; ant colony algorithm can obtain more accurate solutions; the performance of artificial fish warm algorithm is between particle swarm algorithm and ant colony algorithm.