A New Framework of Evolutionary Multi-Objective Algorithms with an
Unbounded External Archive
Abstract
This paper proposes a new framework for the design of evolutionary
multi-objective optimization (EMO) algorithms. The main characteristic
feature of the proposed framework is that the optimization result of an
EMO algorithm is not the final population but a subset of the examined
solutions during its execution. As a post-processing procedure, a
pre-specified number of solutions are selected from an unbounded
external archive where all the examined solutions are stored. In the
proposed framework, the final population does not have to be a good
solution set. The point of the algorithm design is to examine a wide
variety of solutions over the entire Pareto front and to select
well-distributed solutions from the archive. In this paper, first we
explain difficulties in the design of EMO algorithms in the existing two
frameworks: non-elitist and elitist. Next, we propose the new framework
of EMO algorithms. Then we demonstrate advantages of the proposed
framework over the existing ones through computational experiments.
Finally we suggest some interesting and promising future research
topics.