A Hooke-Jeeves based Memetic Algorithm for solving dynamic optimisation problems

Title: A Hooke-Jeeves based Memetic Algorithm for solving dynamic optimisation problems
Creator: Moser, Irene; Chiong, Raymond
Relation: 4th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2009). Hybrid Artificial Intelligence Systems: Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems (Salamanca, Spain June 10-12, 2009) p. 301-309
Publisher Link: http://dx.doi.org/10.1007/978-3-642-02319-4_36
Publisher: Springer
Resource Type: conference paper
Date: 2009
Description: Dynamic optimisation problems are difficult to solve because they involve variables that change over time. In this paper, we present a new Hooke-Jeeves based Memetic Algorithm (HJMA) for dynamic function optimisation, and use the Moving Peaks (MP) problem as a test bed for experimentation. The results show that HJMA outperforms all previously published approaches on the three standardised benchmark scenarios of the MP problem. Some observations on the behaviour of the algorithm suggest that the original Hooke-Jeeves algorithm is surprisingly similar to the simple local search employed for this task in previous work.
Subject: Hooke-Jeeves pattern search; extremal optimisation; dynamic function optimisation; moving peaks problem
Identifier: http://hdl.handle.net/1959.13/1057536
Identifier: uon:16203
Identifier: ISBN:9783642023187
Language: eng
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