A Hooke-Jeeves Based Memetic Algorithm for Solving Dynamic Optimisation Problems

Authors:
Irene Moser;Raymond Chiong
Affiliations:
Faculty of ICT, Swinburne University of Technology, Melbourne, Australia;School of Computing & Design, Swinburne University of Technology (Sarawak Campus), Kuching, Sarawak, Malaysia 93350
Venue:
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Year:
2009

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Abstract

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.