带有自适应合并策略和导向算子的增强型烟花算法

针对传统烟花算法（FWA）在寻优过程中爆炸半径限制搜索范围、粒子间缺少有效交互的缺点，提出带有自适应合并策略和导向算子的增强型烟花算法（EFWA-GM）。首先根据烟花粒子间的位置关系，对寻优空间中重叠的爆炸范围进行自适应合并；其次通过对火花粒子进行分层来充分利用优质粒子的位置信息，从而设计导向算子引导次优粒子进化，以提高算法的寻优精度和收敛速度。在12个标准测试函数上的实验结果表明，所提出的EFWA-GM相较于标准粒子群（SPSO）算法、增强型烟花算法（EFWA）、自适应烟花算法（AFWA）、动态烟花算法（dynFWA）、有导烟花算法（GFWA）在寻优精度和收敛速度方面具有更好的优化性能，并在9个测试函数上取得最优的求解精度。

Enhanced fireworks algorithm with adaptive merging strategy and guidance operator

In order to overcome the shortcomings of traditional FireWorks Algorithm(FWA)in the process of optimization,such as the search range limited by explosion radius and the lack of effective interaction between particles,an Enhanced FireWork Algorithm with adaptive Merging strategy and Guidance operator(EFWA-GM)was proposed.Firstly,according to the position relationship between fireworks particles,the overlapping explosion ranges in the optimization space were adaptively merged.Secondly,by making full use of the position information of high-quality particles through layering the spark particles,the guiding operator was designed to guide the evolution of suboptimal particles,so as to improve the accuracy and convergence speed of the algorithm.Experimental results on 12 benchmark functions show that compared with Standard Particle Swarm Optimization(SPSO)algorithm,Enhanced FireWorks Algorithm(EFWA),Adaptive FireWorks Algorithm(AFWA),dynamic FireWorks Algorithm(dynFWA),and Guided FireWorks Algorithm(GFWA),the proposed EFWA-GM has better optimization performance in optimization accuracy and convergence speed,and obtains optimal solution accuracy on 9 benchmark functions.