Data Structures in Multi-Objective Evolutionary Algorithms


Data structures used for an algorithm can have a great impact on its performance, particularly for the solution of large and complex problems, such as multi-objective optimization problems (MOPs). Multi-objective evolutionary algorithms (MOEAs) are considered an attractive approach for solving MOPs, since they are able to explore several parts of the Pareto front simultaneously. The data structures for storing and updating populations and non-dominated solutions (archives) may affect the efficiency of the search process. This article describes data structures used in MOEAs for realizing populations and archives in a comparative way, emphasizing their computational requirements and general applicability reported in the original work.