引入Logistic混沌映射的连续蟑螂算法应用于函数优化问题

Continuous Cockroach Swarm Optimization with Logistic Chaotic Map for Solving Function Optimization Problems

通过模拟蟑螂的觅食行为,提出用于解决函数优化问题的连续蟑螂算法(continuous cockroach swarm optimization,CC-SO).算法模拟了蟑螂的群居、巢穴不固定、爬行轨迹杂乱无章等生物特性.通过食物车在解空间内抛洒食物,吸引蟑螂向食物爬行完成搜索.在巢穴分配和食物抛洒环节引入了Logistic混沌映射,增强了巢穴和食物在解空间内分布的随机性和遍历性.仿真实验显示,与API和PPBO算法相比,CCSO算法在求解精度、收敛速度、寻优率等方面均提高显著.

For solving function optimization problems,a new continuous cockroach swarm optimization（CCSO）is put forward in this paper.Some biological characteristics of cockroach has been simulated,such as gregarious colony,non-fixing nest,disorderly crawling path and so on.The argorithm have truck throwing food in solution space.The cockroaches could crawl to these food and search for optimal solutions.Logistic chaotic map is used in nest distribution and throwing food.The experimental results show that CCSO is surpior to API and PPBO in Solving Precision,convergent rates and optimization rate.