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Solving dynamic optimisation problems by combining evolutionary algorithms with KD-tree

Nguyen, TT, Jenkinson, I and Yang, Z (2015) Solving dynamic optimisation problems by combining evolutionary algorithms with KD-tree. Soft Computing and Pattern Recognition (SoCPaR), 2013 International Conference of. pp. 247-252. ISSN 978-1-4799-3399-0

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In this paper we propose a novel evolutionary algorithm that is able to adaptively separate the explored and unexplored areas to facilitate detecting changes and tracking the moving optima. The algorithm divides the search space into multiple regions, each covers one basin of attraction in the search space and tracks the corresponding moving optimum. A simple mechanism was used to estimate the basin of attraction for each found optimum, and a special data structure named KD-Tree was used to memorise the searched areas to speed up the search process. Experimental results show that the algorithm is competitive, especially against those that consider change detection an important task in dynamic optimisation. Compared to existing multi-population algorithms, the new algorithm also offers less computational complexity in term of identifying the appropriate sub-population/region for each individual.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Maritime & Mechanical Engineering (merged with Engineering 10 Aug 20)
Publisher: IEEE
Date Deposited: 28 May 2015 10:00
Last Modified: 13 Apr 2022 15:13
DOI or ID number: 10.1109/SOCPAR.2013.7054136
URI: https://researchonline.ljmu.ac.uk/id/eprint/1321
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