Extending Collective Intelligence Evolutionary Algorithms: A Facility Location Problem Application

Daniel Cinalli, Luis Martí, Nayat Sánchez Pi, Ana Cristina Bicharra Garcia

Abstract: This work extends current collective intelligence evolutionary algorithms by incorporating a collective-based variation operator. As part of this work, the proposals are compared with state-of-the-art reference-point-based MOEAs: NSGA-II and RNSGA-II. Another primary objective of the work is to deal with a real-world multi-objective instance of the facility location problem. The experimental results validate the proposal. The new collective intelligence MOEA outperformed NSGA-II and R-NSGA-II for complex scenarios.