多策略融合的改进萤火虫算法

针对传统萤火虫算法（FA）中存在的易陷入局部最优及收敛速度慢等问题，把莱维飞行和精英参与的交叉算子及精英反向学习机制融入到萤火虫优化算法中，提出了一种多策略融合的改进萤火虫算法——LEEFA。首先，在传统萤火虫算法的基础上引入莱维飞行，从而提升算法的全局搜索能力；其次，提出精英参与的交叉算子以提升算法的收敛速度和精度，并增强算法迭代过程中解的多样性和质量；最后，结合精英反向学习机制进行最优解的搜索，从而提高FA跳出局部最优的能力和收敛性能，并实现对于解搜索空间的迅速勘探。为验证所提出的算法的有效性，在基准测试函数上进行了仿真实验，结果表明相较于粒子群优化（PSO）算法、传统FA、莱维飞行萤火虫算法（LFFA）、基于莱维飞行和变异算子的萤火虫算法（LMFA）和自适应对数螺旋-莱维飞行萤火虫优化算法（ADIFA）等算法，所提算法在收敛速度和精度上均表现得更为优异。

Improved firefly algorithm based on multi-strategy fusion

In order to solve the problems that the traditional Firefly Algorithm （FA） is easy to fall into local optimum and has low convergence speed， an improved FA based on multi-strategy fusion， named LEEFA （Levy flight-Elite participated crossover-Elite opposition-based learning Firefly Algorithm） was proposed after integrating Levy flight， elite participated crossover operator and elite opposition-based learning mechanism in the firefly optimization algorithm. Firstly， Levy flight was introduced based on the traditional FA， so that the global search ability of the algorithm was improved. Secondly， an elite participated crossover operator was proposed to improve the convergence speed and accuracy of the algorithm， as well as to enhance the diversity and quality of solutions in the iterative process. Finally， the elite opposition-based learning mechanism was combined to search for the optimal solution， which improved the ability of jumping out of local optimum and convergence performance of FA， and realized the rapid exploration of solution search space. In order to verify the effectiveness of the proposed algorithm， simulation experiments were carried out on the benchmark functions. The results show that compared with algorithms such as Particle Swarm Optimization （PSO） algorithm， traditional FA， Levy Flight Firefly Algorithm （LFFA）， Levy flight and Mutation operator based Firefly Algorithm （LMFA） and ADaptive logarithmic spiral-Levy Improved Firefly Algorithm （ADIFA）， the proposed algorithm performs better in both convergence speed and accuracy.