基于组合变异的果蝇优化算法

王颖; 蔡忱

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基于组合变异的果蝇优化算法

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- 基于组合变异的果蝇优化算法
- Fruit Fly Optimization Algorithm Based on Combined Mutation
- 微电子学与计算机 2018年35卷第5期页码：100-104
- 作者机构：
  
  华北电力大学控制与计算机工程学院
- 作者简介：
- 基金信息：
  
  国家高技术研究发展计划(“八九三”计划)项目 2012AA121005-3
- DOI：
  中图分类号：
- 纸质出版：2018
- 稿件说明：
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王颖, 蔡忱. 基于组合变异的果蝇优化算法[J]. 微电子学与计算机, 2018, 35(5): 100-104.

WANG Ying, CAI Chen. Fruit Fly Optimization Algorithm Based on Combined Mutation[J]. 2018, 35(5): 100-104.
王颖, 蔡忱. 基于组合变异的果蝇优化算法[J]. 微电子学与计算机, 2018, 35(5): 100-104. DOI：

WANG Ying, CAI Chen. Fruit Fly Optimization Algorithm Based on Combined Mutation[J]. 2018, 35(5): 100-104. DOI：

摘要

针对原始果蝇优化算法在寻优过程中存在易于陷入局部极值的不足

提出一种基于组合变异的果蝇优化算法.在算法陷入局部极值时引入组合变异策略

更新种群飞行方向

增强种群逃逸能力

使算法继续迭代寻找全局极值.选取6个基准函数进行实验仿真

实验结果表明: 基于组合变异的果蝇优化算法较好平衡了算法局部搜索能力和全局开采能力

使算法在收敛速度和收敛精度方面均获得较大提高.

Abstract

Aiming at solving the problems of trapping into local extremes easily in the process of optimization

there is a fruit fly optimization algorithm based on combinatorial variation proposed.In this paper the combined mutation strategy is introduced when the algorithm falls into the local extreme.In the basis of this

the flight direction is updated and the population escape ability is enhanced

Thus

the algorithm continues to iterate to find the global extreme value.The experimental results show that the fruit fly optimization algorithm based on combinatorial variation is a good way to balance the algorithms local search ability and global mining ability

and the algorithm is improved in terms of convergence speed and convergence accuracy.

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references

Pan Q K, Sang H Y, Duan J H, et al., An improved fruit fly optimization algorithm for continuous function optimization problems[J]. Knowledge-Based System, 2014, 62: 69-83. DOI: 69

Li H, Guo S, Li C, et al. A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm[J]. Knowl. -Based Syst., 2013, 37: 378-387. DOI: 378

Pan W T, Pan, WT. A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example. Knowledge-Based Systems[J]. KnowledgeBased Systems, 2012, 26 (2): 69-74. DOI: 69

Pan W T. Mixed modified fruit fly optimization algorithm with general regression neural network to build oil and gold prices forecasting model[J]. Kybernetes, 2014, 43 (7): 1053-1063. DOI: 1053

杜晓昕, 张剑飞, 郭媛, 等. 基于柯西-高斯动态消减变异的果蝇优化算法研究[J]. 计算机工程与科学. 2016, 38 (6): 1171-1176. DOI: 1171

Li H, Guo S, Zhao H, et al. Annual electric load forecasting by a least squares support vector machine with a fruit fly optimization algorithm[J]. Energies, 2012, 5 (12): 4430-4445. DOI: 4430

Yao X, Liu Y. Fast evolutionary programming. [C]//Evolutionary Programming VI. Berlin: Springer, 1997: 151-161.

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