基于Predator-Prey行为的萤火虫优化算法

Artificial glowworm swarm optimization algorithm based on biological predator-prey behavior

针对基本萤火虫优化(GSO)算法在求解函数全局最优值时,存在着易陷入局部最优、收敛速度慢和求解精度低等问题,提出了1种基于生物捕食-被捕食(Predator-Prey)行为的双种群GSO算法(GSOPP)。该算法通过引入种群间的追逐与逃跑以及变异等策略加快了收敛速度,且能获得精度更高的解。最后,通过对8个标准测试函数进行测试,结果表明,改进后的GSOPP算法比基本GSO算法有更优的性能。

According to the basic glowworm swarm optimization （GSO） algorithm in solving the function of global optimal value existing some problems, such as easy to fall into local optimum, slow convergence and low precision, an artificial glowworm swarm optimization algorithm based biological predator-prey behavior （GSOPP） is proposed. The algorithm through populations chase and escape, and the mutation strategy to speed up the convergence rate, and can obtain a more accurate solution. Finally, the test results of 8 standard test functions show that, the improved GSOPP algorithm than the basic GSO algorithm has Better performance.