基于连续空间的萤火虫算法改进

doi:10.16180/j.cnki.issn1007-7820.2022.02.007

摘要/Abstract

摘要：

针对萤火虫算法在全局寻优过程中求解精度差,且容易陷入局部最优的问题,文中提出了一种优化的萤火虫算法。采用离散-连续的方法将传统萤火虫算法的空间连续化,在传统萤火虫算法的基础上定义新的吸引度计算式以及相应的更新策略,实现待求的离散问题的空间连续化,改善萤火虫单体相应的移动方式。实验仿真结果证明了该改进算法的有效性。文中对改进的萤火虫算法及其适用范围作了总结,并指出了今后研究方向。

关键词: 萤火虫算法, 算法原理, 连续空间, 函数优化, 参数辨识, 全局最优, MATLAB, 优化算法

Abstract:

In view of the problem that the firefly algorithm has poor accuracy in global optimization and is easy to fall into local optimum, an optimized firefly algorithm is proposed in this study. The discrete continuous method is used to make the traditional firefly algorithm space continuous. On the basis of the traditional firefly algorithm, a new attractiveness calculation formula and corresponding update strategy are defined to realize the spatial continuity of the discrete problems to be sought and improve the corresponding movement mode of the firefly monomer. Experimental simulation results prove the effectiveness of the improved algorithm. Besides, the improved firefly algorithm and its application scope are summarized, and the future research direction is pointed out in the proposed study.

Key words: firefly algorithm, algorithm principle, continuous space, function optimization, parameter identification, the global optimal, MATLAB, optimization algorith

中图分类号:

TP301.6

刘晨旻,王亚刚. 基于连续空间的萤火虫算法改进[J]. 电子科技, 2022, 35(2): 40-45.

LIU Chenmin,WANG Yagang. Optimization of Firefly Algorithm Based on Continuous Space[J]. Electronic Science and Technology, 2022, 35(2): 40-45.

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寻优方法	区间范围	萤火虫数	最大迭代次数	寻优结果分布区间
传统	X:[-5,5] Y:[-5,5]	100	60	X:[-3.100 0,-2.901 5] Y:[2.897 3,3.098 7]
优化	X:[-5,5] Y:[-5,5]	100	60	X:[-3.004 9,-2.994 9] Y:[2.994 9,3.005 0]

测试函数	传统算法	本文算法	近年优化算法
Michalewicz	1.781 4±0.015 0	1.793 8±0.061 0	1.779 0±0.097 2
Rosenbrock	21 601±9 358.9	25 442±176.6	25 237±841.2
Four Peak	2.425 9±1.6×10^-4	2.426 0±0.8×10^-4	2.425 9±1.0×10^-4
Mishra	163.971 3±74.8	195.572 9±0.6	185.691 7±1.6
McCormick	18.011 5±6.510 5	24.162 7±0.063 3	22.233 1±3.115 1