Service Incident: New DOI registrations are working again. Re-registration of failed DOI registrations (~500) are still affected by the service incident at DataCite (our DOI registration agency).
Published April 15, 2022 | Version 1.0
Software Open

An Improved Pareto Front Modeling Algorithm for Large-scale Many-Objective Optimization

  • 1. Delft University of Technology

Description

Source code of the AGE-MOEA-II, a novel variant of the Adaptive GEometry-based Many-Objective Evolutionary Algorithm. This new variant uses (1) the Newton-Raphson algorithm to compute the curvature of the front formed by the non-dominated solutions, and (2) geodesic distance.

 

Abstract:

A key idea in many-objective optimization is to approximate the optimal Pareto front using a set of representative non-dominated solutions. The produced solution set should be close to the optimal front (convergence) and well-diversified (diversity). Recent studies have shown that measuring both convergence and diversity depends on the shape (or curvature) of the Pareto front. In recent years, researchers have proposed evolutionary algorithms that model the shape of the non-dominated front to define environmental selection strategies that adapt to the underlying geometry. This paper proposes a novel method for non-dominated front modeling using the Newton-Raphson iterative method for roots finding. Second, we compute the distance (diversity) between each pair of non-dominated solutions using geodesics, which are generalizations of the distance on Riemann manifolds (curved topological spaces). We have introduced an evolutionary algorithm within the Adaptive Geometry Estimation based MOEA (AGE-MOEA) framework, which we called AGE-MOEA-II. Computational experiments with 17 problems from the WFG and SMOP benchmarks show that AGE-MOEA-II outperforms its predecessor AGE-MOEA as well as other state-of-the-art many-objective algorithms, i.e., NSGA-III, MOEA/D, VaEA, and LMEA.

Files

source-code.zip

Files (11.4 kB)

Name Size Download all
md5:c30891449f27d0ec36c808bd1b0807be
11.4 kB Preview Download