Election Campaign Algorithm for Multimodal Function Optimization

In this paper, we present a new algorithm named Election campaign algorithm (ECA) for the multimodal function optimization. It acts by simulating the behavior that the election candidates pursue the highest support in election campaign. The proposed approaches are validated using test functions taken from the specialized literature, and our results are compared with those obtained by genetic algorithm (GA) and particle swarm optimization algorithm (PSO). Our comparative study indicates that ECA verifies its good performance when dealing with multimodal functions.