Rostami, S. and Shenfield, A., 2016. A multi-tier adaptive grid algorithm for the evolutionary multi-objective optimisation of complex problems. Soft Computing. (In Press)
Full text available as:
Soft_Computing___m_CMA_PAES.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.
PDF (OPEN ACCESS- SPRINGER LINK)
art_10.1007_s00500-016-2227-6-1.pdf - Published Version
Available under License Creative Commons Attribution.
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 (μ+λ) μ λ Multi-Objective Covariance Matrix Adaptation Evolution Strategy (MO-CMA-ES). In comparison with 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.
|Group:||Faculty of Science & Technology|
|Deposited By:||Unnamed user with email symplectic@symplectic|
|Deposited On:||27 Jun 2016 15:11|
|Last Modified:||27 Jun 2016 15:11|
Downloads per month over past year
|Repository Staff Only -|