CMA-PAES : Pareto archived evolution strategy using covariance matrix adaptation for Multi-Objective Optimisation

ROSTAMI, Shahin and SHENFIELD, Alex (2012). CMA-PAES : Pareto archived evolution strategy using covariance matrix adaptation for Multi-Objective Optimisation. In: 12th UK Workshop on Computational Intelligence (UKCI) 2012. IEEE, 1-8.

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Abstract

The quality of Evolutionary Multi-Objective Optimisation (EMO) approximation sets can be measured by their proximity, diversity and pertinence. In this paper we introduce a modular and extensible Multi-Objective Evolutionary Algorithm (MOEA) capable of converging to the Pareto-optimal front in a minimal number of function evaluations and producing a diverse approximation set. This algorithm, called the Covariance Matrix Adaptation Pareto Archived Evolution Strategy (CMA-PAES), is a form of (μ + λ) Evolution Strategy which uses an online archive of previously found Pareto-optimal solutions (maintained by a bounded Pareto-archiving scheme) as well as a population of solutions which are subjected to variation using Covariance Matrix Adaptation. The performance of CMA-PAES is compared to NSGA-II (currently considered the benchmark MOEA in the literature) on the ZDT test suite of bi-objective optimisation problems and the significance of the results are analysed using randomisation testing.

Item Type:	Book Section
Research Institute, Centre or Group - Does NOT include content added after October 2018:	Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
Identification Number:	https://doi.org/10.1109/UKCI.2012.6335782
Page Range:	1-8
Depositing User:	Alex Shenfield
Date Deposited:	07 Aug 2014 09:26
Last Modified:	18 Mar 2021 14:17
URI:	https://shura.shu.ac.uk/id/eprint/8312

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