Verifying election campaign optimization algorithm by several benchmarking functions

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.