A Voting Approach for Comparing Several Swarm Intelligence Algorithms

In the last decades, social choice theory has gained a significant popularity. Its main application areas are social sciences, political sciences, economic sciences and computer science. Computational social choice is a new research area situated at the intersection of social choice theory and computer science. Another popular and relatively new research area is swarm intelligence that aims to propose and use bio-inspired algorithms for solving optimization problems. In this paper we propose a methodology of comparing various swarm intelligence algorithms using voting methods (an important topic in social choice theory). Also, as a case study, we use our methodology to compare three swarm intelligence algorithms (Particle Swarm Optimization, Cat Swarm Optimization, and Artificial Bee Colony) on several minimization functions. For the interpretation of our comparison results, we use two important theorems: No Free Lunch Theorem (from optimization theory) and Arrow´s Impossibility Theorem (from voting theory).