论文部分内容阅读
萤火虫算法是群智能领域近年出现的一个新的研究方向,该算法虽已在复杂函数优化方面取得了成功,但也存在着易于陷入局部最优且进化后期收敛速度慢等问题,而模拟退火机制具有很强的全局搜索能力,结合两者的优缺点,提出一种融合模拟退火策略的萤火虫优化算法。改进后的算法在萤火虫算法全局搜索过程中融入模拟退火搜索机制,在局部搜索过程中采用了回火策略,改善寻优精度,改进了萤火虫算法的全局搜索性能和局部搜索性能。仿真实验结果表明:改进后的算法在收敛速度和解的精度方面有了显著地提高,证明了算法改进的可行性和有效性。
Firefly algorithm is a new research direction emerging in the field of swarm intelligence. Although this algorithm has been successful in the optimization of complex functions, it also has some problems such as easy to get stuck in local optima and slow convergence in late evolutionary stage. Simulated annealing It has a strong global search ability. Combining the advantages and disadvantages of both, a firefly optimization algorithm based on simulated annealing strategy is proposed. The improved algorithm integrates the simulated annealing search mechanism in the global search of firefly algorithm, and adopts the tempering strategy in the local search process to improve the optimization accuracy and improve the global search performance and local search performance of the firefly algorithm. Simulation results show that the improved algorithm has significantly improved the convergence speed and the accuracy of the solution, and proves the feasibility and effectiveness of the improved algorithm.