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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.
By simulating the foraging behavior of cockroaches, a continuous cockroach swarm optimization (CC-SO) is proposed to solve the function optimization problem.The algorithm simulates the biological characteristics of the cockroaches, such as the non-fixed nest and the crawling path, The car spilled food in the solution space to attract the cockroaches to crawl to complete the search. Logistic chaos mapping was introduced in the distribution and food spilling of the nest to enhance the randomness and ergodicity of the nest and food distribution in the solution space. Compared with the API and PPBO algorithms, the CCSO algorithm improves significantly in terms of solution accuracy, convergence speed and search efficiency.