FICA: fuzzy imperialist competitive algorithm

Saeid ARISH; Ali AMIRI; Khadije NOORI

doi:10.1631/jzus.C1300088

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FICA: fuzzy imperialist competitive algorithm

计算机与自动化 | Updated：2022-05-19

- FICA: fuzzy imperialist competitive algorithm
- FICA: fuzzy imperialist competitive algorithm
- 信息与电子工程前沿（英文） 2014年15卷第5期页码：363-371
- Affiliations：
  
  Department of Computer Engineering, University of Zanjan, Zanjan, Iran
- Author bio：
  
  [ "Saeid ARISH, saeed.aiproject@gmail.com" ]
  Ali AMIRI, a_amiri@znu.ac.ir
  [ "Khadije NOORI, kh.noori90@gmail.com" ]
- Funds：
- DOI：10.1631/jzus.C1300088
  中图分类号： TP301.6
- 收稿：2013-04-11，
  
  修回：2014-;4-11，
  
  纸质出版：2014-05
- Accepted：
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FICA: fuzzy imperialist competitive algorithm[J]. 信息与电子工程前沿（英文）, 2014,15(5):363-371.

Saeid ARISH, Ali AMIRI, Khadije NOORI. FICA: fuzzy imperialist competitive algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2014, 15(5): 363-371.
FICA: fuzzy imperialist competitive algorithm[J]. 信息与电子工程前沿（英文）, 2014,15(5):363-371. DOI： 10.1631/jzus.C1300088.

Saeid ARISH, Ali AMIRI, Khadije NOORI. FICA: fuzzy imperialist competitive algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2014, 15(5): 363-371. DOI： 10.1631/jzus.C1300088.

摘要

Abstract

Despite the success of the imperialist competitive algorithm (ICA) in solving optimization problems

it still suffers from frequently falling into local minima and low convergence speed. In this paper

a fuzzy version of this algorithm is proposed to address these issues. In contrast to the standard version of ICA

in the proposed algorithm

powerful countries are chosen as imperialists in each step; according to a fuzzy membership function

other countries become colonies of all the empires. In absorption policy

based on the fuzzy membership function

colonies move toward the resulting vector of all imperialists. In this algorithm

no empire will be eliminated; instead

during the execution of the algorithm

empires move toward one point. Other steps of the algorithm are similar to the standard ICA. In experiments

the proposed algorithm has been used to solve the real world optimization problems presented for IEEE-CEC 2011 evolutionary algorithm competition. Results of experiments confirm the performance of the algorithm.

关键词

Keywords

references

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