Chaotic Election Algorithm

Abstract

A novel Chaotic Election Algorithm (CEA) is presented for numerical function optimization. CEA is a powerful enhancement of election algorithm. The election algorithm is a socio-politically inspired strategy that mimics the behavior of candidates and voters in presidential election process. In election algorithm, individuals are organized as electoral parties. Advertising campaign forms the basis of the algorithm in which individuals interact or compete with one other using three operators: positive advertisement, negative advertisement and coalition. Advertising campaign hopefully causes the individuals converge to the global optimum point in solution space. However, election algorithm suffers from a fundamental challenge: gets stuck at local optima due to the inability of advertising campaign in searching solution space. CEA enhances the election algorithm through modifying party formation step, introducing chaotic positive advertisement and migration operator. By chaotic positive advertisement, CEA exploits the entire solution space, which increases the probability of obtaining global optimum point. By migration, CEA increases the diversity of the population and prevents early convergence of the individuals. The proposed CEA algorithm is tested on 28 well-known standard boundary-constrained test functions, and the results are verified by a comparative study with several well-known meta-heuristics. The results demonstrate that CEA is able to provide significant improvement over canonical election algorithm and other comparable algorithms.