A New Self-changing Step Fruit Fly Optimization Algorithm

SHENG Chao; ZOU Hai; ZHU Fu-zhan

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A New Self-changing Step Fruit Fly Optimization Algorithm

更新时间：2026-09-04

- A New Self-changing Step Fruit Fly Optimization Algorithm
- Vol. 36, Issue 2, Pages: 62-67(2019)
- 作者机构：
  
  安徽大学计算机科学与技术学院,安徽,合肥,230601
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2019
- 稿件说明：
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盛超, 邹海, 朱富占. 一种新型的自调节步长果蝇优化算法[J]. 微电子学与计算机, 2019, 36(2): 62-67.

SHENG Chao, ZOU Hai, ZHU Fu-zhan. A New Self-changing Step Fruit Fly Optimization Algorithm[J]. 2019, 36(2): 62-67.
盛超, 邹海, 朱富占. 一种新型的自调节步长果蝇优化算法[J]. 微电子学与计算机, 2019, 36(2): 62-67. DOI：

SHENG Chao, ZOU Hai, ZHU Fu-zhan. A New Self-changing Step Fruit Fly Optimization Algorithm[J]. 2019, 36(2): 62-67. DOI：

摘要

为了解决传统果蝇算法在全局优化过程中存在的问题

将传统果蝇算法的步长进行改进

该算法在寻优过程中用当前迭代果蝇群中最佳浓度值与最差浓度值之差与前一次迭代果蝇群中最佳浓度值与最差浓度值之差作对比

得到浓度差值变化率.根据浓度差值变化率的大小相对应动态改变果蝇寻优步长

能够有效的权衡全局寻优能力和局部探索能力.本文改良的果蝇算法在收敛速率和寻优精度上明显优异于其他三种果蝇算法.

Abstract

In order to solve the problems of the traditional fruit fly algorithm in the global optimization process

the step size of the traditional fruit fly algorithm is improved. In the process of optimization

the algorithm uses the difference between the best concentration value and the worst concentration value in the current iterative fruit fly group and the previous one. The difference between the best concentration of the Drosophila melanogaster group and the worst concentration value was compared

and the change rate of the concentration difference was obtained. According to the rate of change of concentration difference

the optimal step size of fruit flies should be changed dynamically

which can effectively balance global optimization capability and local exploration ability. The results show that the improved fruit fly algorithm is obviously superior to the other three kinds of fruit fly algorithms in the convergence rate and optimization accuracy.

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references

杨帆, 王小兵, 邵阳.改进型果蝇算法优化的灰色神经网络变形预测[J].测绘科学, 2018(2):64-69. DOI: 64

Huang Q Z, Wang G H, Wang Z, et al. An improved fruit fly optimization algorithm for solving traveling salesman problem[J]. Frontiers of Information Technology % Electronic Engineering, 2017, 18(10):1525-1533. DOI: 1525

丁国绅, 邹海.新型改进果蝇优化算法[J].计算机工程与应用, 2016, 52(21):168-174. DOI: 168

桂龙, 王爱平, 丁国绅.改进步长与策略的果蝇优化算法[J].计算机工程与应用, 2018, 54(4):148-154. DOI: 148

李龙澍, 郭紫梦.应用混沌果蝇算法的路径覆盖测试用例优化技术研究[J].小型微型计算机系统, 2018, 2(39):362-366. DOI: 362

张超, 魏三强, 罗颖.一种基于随机数扰动变异的果蝇优化算法[J].西华大学学报, 2017, 5(36):36-43. DOI: 36

Yuan X, Dai X, Zhao J, et al. On a novel multi-swarm fruit fly optimization algorithm and its application[J]. Applied Mathematics % Computation, 2014, 233(3):260-271. DOI: 260

潘文超.应用果蝇优化算法优化广义回归神经网络进行企业经营绩效评估[J].太原理工大学学报(社会科学版), 2011, 29(7):1-5. DOI: 1

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