The Dissimilated Krill Herd Algorithm with Aggregation Degree

LIU Kai; SHEN Ju-hong; WAN Ren-xia; LI Yan-yan

Chinese

您当前的位置：

首页 >

文章列表页 >

The Dissimilated Krill Herd Algorithm with Aggregation Degree

更新时间：2026-09-04

- The Dissimilated Krill Herd Algorithm with Aggregation Degree
- Vol. 34, Issue 7, Pages: 11-17(2017)
- 作者机构：
  
  北方民族大学机构数学与信息科学学院,宁夏,银川,750021
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2017
- 稿件说明：
移动端阅览
刘楷, 沈菊红, 万仁霞, 李延延. 基于聚集度的异化磷虾群算法[J]. 微电子学与计算机, 2017, 34(7): 11-17.

LIU Kai, SHEN Ju-hong, WAN Ren-xia, et al. The Dissimilated Krill Herd Algorithm with Aggregation Degree[J]. 2017, 34(7): 11-17.
刘楷, 沈菊红, 万仁霞, 李延延. 基于聚集度的异化磷虾群算法[J]. 微电子学与计算机, 2017, 34(7): 11-17. DOI：

LIU Kai, SHEN Ju-hong, WAN Ren-xia, et al. The Dissimilated Krill Herd Algorithm with Aggregation Degree[J]. 2017, 34(7): 11-17. DOI：

摘要

针对标准磷虾群算法存在着不易跳出局部寻优、搜索精度低等问题

提出了一种基于聚集度的异化磷虾群算法.本算法根据种群多样性指标聚集度的变化

通过在两个相反位置移动方向的选择策略来增加磷虾进化多样性

同时引入了随机数策略来模拟磷虾的外部扰动

从而取代原磷虾群算法中的随机扩散运动的影响.再引入平均距离指标来增加局部搜索的变异概率

同时将背向最优位置的速度方向作为搜索变异方向

从而扩大了群体的搜索空间

保证算法的全局搜索能力.通过对九个典型不同类型的基准函数进行实验测试

结果表明了改进策略对算法的全局搜索能力和优化精度有很大的提高

能达到更好的收敛速度和寻优精度.

Abstract

In view of shortcomings of the basic krill herd algorithm (KH)——easily trapping in local optimization and low search accuracy

a dissimilated krill herd algorithm with aggregation degree is proposed in this paper. According to the variation of concentration (which plays as the diversity criterion of population)

the algorithm increases the krills evolving diversity by a selection strategy on two opposite directions

moreover

a random number strategy is introduced to simulate the external disturbance of krill

which actually takes the place of the influence of stochastic diffusion movement of original krill herd algorithm

and then the average distance index is used to increase the dissimilation probability of local search. Also the proposed algorithm takes the reverse of optimal position as the dissimilation search direction to expand the search space of the group

so that it can ensure a satisfied the global search ability. The experimental results show that the dissimilation strategy can gain the promising availabilities of the optimization accuracy and the global search ability.

关键词

Keywords

references

Li J P, Tang Y G, Hua C C, et al. An improved krill herd algorithm: Krill herd with linear decreasing step[J]. Applied mathematics and computation, 2014, 234(10): 356-367. DOI: 356

Mandal B, Roy P K, Mandal S. Economic load dispatch using krill herd algorithm[J]. International Journal of Electrical Power%Energy Systems, 2014, 57(5): 1-10. DOI: 1

Saremi S, Mirjalili S M, Mirjalili S. Chaotic krill herd optimization algorithm[J]. Procedia Technology, 2014, 12(1): 180-185. DOI: 180

Wang G G, Guo L H, Wang H Q, et al. Incorporating mutation scheme into krill herd algorithm for globalnumerical optimization[J]. Neural Computing and Applications, 2014, 24 (3/5): 853-871. DOI: 853

王维博. 粒子群优化算法研究及其应用[D]. 成都: 西南交通大学, 2012.

Hofmann E E, HaskellA G E, Klinck J M, et al. Lagrangian modeling studies of antarctic krill (Euphasia superba) swarm formation[J]. ICES J Mar Sci, 2004(61): 617-31. DOI: 617

Kennedy J, Eberhart R C. Particle swarm optimization[C]//Proc. IEEE Int. Gonf. Neural Netw. Perth, Australia, 1995(4): 1942-1948.

Gandomi A H, Alavi A H. Krill herd: a new bioinspired optimization algorithm[J]. Communications in Nonlinear Science and Numerical Simulation, 2012, 17(12): 4831-4845. DOI: 4831

王磊, 张汉鹏. 基于混沌搜索与精英交叉算子的磷虾觅食算法[J]. 计算机工程, 2015, 41(3): 156-161. DOI: 156

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Aerial vehicle detection algorithm based on multi-scale fusion and small-object enhancement

High gain stability high temperature operational amplifier based on reverse body bias

Implementation and verification of a dual-chain power gating strategy for low power design

A feature-oriented context understanding and slice analysis network for tuberculosis detection

A high-efficiency charge pump for ultra-low voltage boost systems

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰