Tutorial - External Archivers for Multi-objective Evolutionary Algorithms

Oliver Schütze,CINVESTAV-IPN,Mexico; Carlos Hernández, IIMAS-UNAM, Mexico ABSTRACT: This tutorial aims to present an overview of several archiving strategies developed over the last years dealing with approximations of the solution sets of multi-objective optimization problems by evolutionary algorithms. More precisely, we will present and analyze several archiving strategies that aim for different finite size approximations either of the set of optimal solutions (Pareto set and front) as well as the set of approximate solutions of a given optimization problem. The convergence analysis will be done for a very broad framework that includes all existing evolutionary algorithms (along with other metaheuristics) and that will only use minimal assumptions on the process to generate new candidate solutions. As will be seen, already small changes in the design of the archivers can have significant effects on the respective limit archives. It is important to note that all of the archivers presented here can be coupled with any set-based multi-objective search algorithm, and that the resulting hybrid method takes over the convergence properties of the used archiver. This tutorial hence targets all algorithm designers and practitioners in the field of multi-objective optimization. We hope that the archivers can either be used to enhance their preferred search method or that they may be used as a starting point for the design of further archiving strategies that aim for different approximations of the solution set.