A multi-tier adaptive grid algorithm for the evolutionary multi-objective optimisation of complex problems

ROSTAMI, Shahin and SHENFIELD, Alex (2016). A multi-tier adaptive grid algorithm for the evolutionary multi-objective optimisation of complex problems. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 21 (17), 4963-4979.

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Official URL: http://link.springer.com/article/10.1007/s00500-01...
Link to published version:: https://doi.org/10.1007/s00500-016-2227-6


The multi-tier Covariance Matrix Adaptation Pareto Archived Evolution Strategy (m-CMA-PAES) is an evolutionary multi-objective optimisation (EMO) algorithm for real-valued optimisation problems. It combines a non-elitist adaptive grid based selection scheme with the efficient strategy parameter adaptation of the elitist Covariance Matrix Adaptation Evolution Strategy (CMA-ES). In the original CMA-PAES, a solution is selected as a parent for the next population using an elitist adaptive grid archiving (AGA) scheme derived from the Pareto Archived Evolution Strategy (PAES). In contrast, a multi-tiered AGA scheme to populate the archive using an adaptive grid for each level of non-dominated solutions in the considered candidate population is proposed. The new selection scheme improves the performance of the CMA-PAES as shown using benchmark functions from the ZDT, CEC09, and DTLZ test suite in a comparison against the $(\mu + \lambda)$ Multi-Objective Covariance Matrix Adaptation Evolution Strategy (MO-CMA-ES). In comparison to MO-CMA-ES, the experimental results show that the proposed algorithm offers up to a 69\% performance increase according to the Inverse Generational Distance (IGD) metric.

Item Type: Article
Uncontrolled Keywords: Foundations, Multi-objective optimisation, Evolutionary algorithms, Evolution strategies, Covariance matrix adaptation, Adaptive grid archiving
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/10.1007/s00500-016-2227-6
Page Range: 4963-4979
Depositing User: Alex Shenfield
Date Deposited: 29 Jul 2016 10:18
Last Modified: 18 Mar 2021 01:04
URI: https://shura.shu.ac.uk/id/eprint/12686

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