一种粒子群-萤火虫算法的参数优化方法

A parameter optimization of particle swarm-firefly algorithm

针对传统萤火虫算法(Firefly Algorithm,FA)的参数设置无法解决不同优化问题的实时要求,算法寻优效率降低等问题,提出一种通过粒子群优化(Particle Swarm Optimization,PSO)的粒子搜索,对不同优化问题自适应选取萤火虫算法参数值的优质组合的方法,并选取8个不同的测试函数进行实验.测试结果表明,该粒子群-萤火虫算法参数优化策略具有较强的灵活性和适应性,验证了优化方案的可行性和有效性,并为其他启发式算法的参数优化提供思路.

Due to the fact that of the parameter setting of the traditional firefly algorithm（FA） cannot meet the real-time requirement of solving different optimization problems,and the effi-ciency of the algorithm is reduced,a kind of optimized parameter method is put forward by Particle Swarm Optimization （PSO）particles search,combining adaptive selection of firefly al-gorithm according to different optimization parameter value of high quality,with eight differ-ent test functions,chosen to test the optimized algorithm.The results show that the particle swarm-firefly algorithm parameter optimization strategy has strong flexibility and adaptability, with the feasibility and effectiveness of optimization scheme proved,and gives an idea for other heuristic algorithm of parameter optimization.