An Experimental Study of Benchmarking Functions for Election Campaign Algorithm

Election campaign algorithm (ECA) is a new heuristic optimization algorithm, it acts by simulating the behavior that the election candidates pursue the highest support in campaign all along. In ECA, the whole searching space is imagined to be assembly of voters, and the current solutions are imagined to be the election candidates. The candidates can influence the voters round them, the effects from candidates to voters will decrease gradually with the increase of distances between the candidates and the voters. The higher prestige a candidate comports, the larger investigated range he has. The support from the different voters are discrepant obviously, voters have to allot their support proportionally according to the effects imposed by the candidates. Sampling investigate to voters is done to investigate the support of candidates. In order to obtain an exact and global investigated, local investigated voters are generated in the probability determined by a normal-distribution function that the mean is the location coordinates of candidates, and global investigated voters is generated in the probability determined by a uniform distribution function. 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, which is named support focus, it is the next position of the candidate. Such computational cycle is done continually until a candidate finds the position of the highest support, which is the global solution of the optimization problems. Finally, test functions of optimization algorithm are applied to verify the ECA. The computational results show that ECA is able to find out the global solution entirely.