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An Enhanced Adaptive Geometry Evolutionary Algorithm Using Stochastic Diversity Mechanism by Fodil Benali; Damien Bodénès; Cyril De Runz; Nicolas Labroche is a scholarly article available to read on EtoBox.

What is An Enhanced Adaptive Geometry Evolutionary Algorithm Using Stochastic Diversity Mechanism about?

Evolutionary Algorithms have been regularly used for solving multi and many objectives optimization problems. The effectiveness of such methods is determined generally by their ability to generate a well-distributed front (diversity) that is as close as possible to the optimal Pareto front (proximity). Analysis of current multiobjective evolutionary frameworks shows that they are still suboptimal and present poor versatility on different geometries and dimensionalities. For that, in this paper, we present AGE-MOEA++, a new Multi and Many Objective Evolutionary Algorithm that:(1) incorporates the principle of Pareto Front (PF) shape fitting to enhance the convergence in different shaped high dimensional objective spaces, and (2) adapts K-means ++ fundamentals in order to best manage the diversity in non-uniform distributed PF. The empirical study shows that our proposal has better results than the state-of-the-art approaches in terms of IGD and is competitive in terms of GD. CCS CONCEPTS • Theory of computation → Evolutionary algorithms.

Author

Fodil Benali; Damien Bodénès; Cyril De Runz; Nicolas Labroche

Publisher

ACM

Published

2022

Language

EN