广义菌群优化算法

Generalized Bacterial Foraging Optimization

为提高菌群优化算法的性能,将群体聚集机制和自适应策略集成到趋药性操作中,取消聚集操作,构造出新的趋化操作,在趋化循环中引入自适应扩散机制,提高其克服"早熟"的能力,重新定义健康度,减少计算复杂性,得到了一种新的群体智能优化方法——广义菌群优化算法(GBFO,Generalized Bacterial Foraging Optimization)。通过10个复杂Benchmark函数的计算进行算法性能测试,并与几个典型的算法进行了实验比较,结果表明,GBFO算法在搜索能力和稳定性、求解质量和效率等方面优于其他典型算法的比率分别达到80%～90%,70%～80%,验证了该算法的优越性能。

In order to improve the performance of Bacterial Foraging Optimization （BFO）, a new swarm intelligence op- timization algorithm, called the generalized bacterial foraging optimization （GBFO） was proposed,which has new cherno- tactic operation, is only composed of chemotaxis with group aggregation mechanism and adaptive strategy, and swar- ming is cancelled. The chemotactic loop with adaptive diffusion mechanism can improve ability of overcoming the ＂pre- mature＂, and healthiness is redefined to reduce the computational complexity. 10 complex Benchmark functions were tested. The simulation shows that the GBFO has better search ability and stability, solution quality and efficiency than other typical algorithm up to 80%-90 %, 70% -80%among test functions. The comparisons also show GBFO has ex- cellent performance.