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. (In Press)
PDF (Acceptance email)
Restricted to Repository staff only
Download (38kB) | Contact the author
Shenfield-Multi-tierAdaptiveGridAlgorith-(Published).pdf - Published Version
Available under License Creative Commons Attribution.
Download (1MB) | Preview
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.
|Research Institute, Centre or Group:||Cultural Communication and Computing Research Institute > Communication and Computing Research Centre|
|Depositing User:||Alex Shenfield|
|Date Deposited:||29 Jul 2016 10:18|
|Last Modified:||20 Feb 2017 11:18|
Actions (login required)
Downloads per month over past year