Verifying election campaign optimization algorithm by several benchmarking functions

  • Authors:

  • Wenge Lv;Qinghua Xie;Zhiyong Liu;Deyuan Li;Siyuan Cheng;Shaoming Luo;Xiangwei Zhang

  • Affiliations:

  • Faculty of Electro-mechanics Engineering, Guangdong University of Technology, Guangzhou, China;Faculty of Electro-mechanics Engineering, Guangdong University of Technology, Guangzhou, China;Faculty of Electro-mechanics Engineering, Guangdong University of Technology, Guangzhou, China;Faculty of Electro-mechanics Engineering, Guangdong University of Technology, Guangzhou, China;Faculty of Electro-mechanics Engineering, Guangdong University of Technology, Guangzhou, China;Faculty of Electro-mechanics Engineering, Guangdong University of Technology, Guangzhou, China;Faculty of Electro-mechanics Engineering, Guangdong University of Technology, Guangzhou, China

  • Venue:
  • ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

Election Campaign Optimization (ECO) algorithm is a new heuristic algorithm, it works by simulating the behavior that the election candidates pursue the highest support in campaign. The candidates can influence the voters round them. The higher prestige a candidate comports, the larger effect he has. Voters have to allot their support proportionally according to the effects imposed by the candidates. Global and local survey-sample to voters are done to investigate the support of candidates. The proportion of the support to a candidate from a voter to the sum of the support of the candidate from all voters is the contribution of a voter to the candidate. The sum of location coordinates of every voters powered by its contribution is a new location coordinates, it is the next position of the candidate. Such cycle is done continually until a candidate finds the position of the highest support. In this paper, several benchmarking functions are used to verify ECO algorithm.