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

Rostami, S. and Shenfield, A., 2017. A multi-tier adaptive grid algorithm for the evolutionary multi-objective optimisation of complex problems. Soft Computing, 21 (17), pp. 4963-4979.

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DOI: 10.1007/s00500-016-2227-6

Abstract

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.

Item Type:Article
ISSN:1432-7643
Uncontrolled Keywords:Multi-objective optimisation; Evolutionary algorithms; Evolution strategies; Covariance matrix adaptation; Adaptive grid archiving
Group:Faculty of Science & Technology
ID Code:24261
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:27 Jun 2016 15:11
Last Modified:13 Sep 2017 15:45

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