Dynamic Weighted Aggregation for evolutionary multi-objective optimization: why does it work and how?

Published 7 July 2001

Yaochu Jin, Markus Olhofer, Bernhard Sendhoff

Citations214

TL;DR

A theory on why CWA fails for multi-objective problems with a concave Pareto front is provided schematically and it can easily be explained why EDWA has worked well for both convex and concave multi- objective problems.

Abstract

Evolutionary Dynamic Weighted Aggregation (EDWA) has shown to be both effective and computationally efficient [1] for multiobjective optimization (MOO). Besides, it was also found empirically and surprisingly that EDWA was able to deal with multiobjective optimization problems with a concave Pareto front, which has proved to be beyond the capability of the Conventional Weighted Aggregation (CWA) methods [2]. In this paper, a theory on why CWA fails for multi-objective problems with a concave Pareto front is provided schematically. According to this theory, it can easily be explained why EDWA has worked well for both convex and concave multi-objective problems. Simulation examples are conducted on various test functions to support our theory. It is concluded that EDWA is an effective and efficient method for solving multi-objective optimization problems.

Keywords

Computer ScienceDecision Sciences

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