Abstract
Dynamic constrained optimisation problems (DCOPs) have specific characteristics that do not exist in dynamic optimisation problems with bounded constraints or without constraints. This poses difficulties for some existing dynamic optimisation strategies. The maintaining/introducing diversity approaches might become less effective due to the presence of infeasible areas, and thus might not well handle with the switch of global optima between disconnected feasible regions. In this paper, a speciation-based approach was firstly proposed to overcome this, which utilizes deterministic crowding to maintain diversity, assortative mating and local search to promote exploitation, as well as feasibility rules to deal with constraints. The experimental studies demonstrate that the newly proposed method generally outperforms the state-of-the-art algorithms on a benchmark set of DCOPs.
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Acknowledgments
This work was partially supported by NSFC (Grant No. 61329302), EPSRC (Grant No. EP/K001523/1), and Royal Society Newton Advanced Fellowship (Ref. no. NA150123). The authors thank Stefan Menzel for giving the valuable advice.
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Lu, X., Tang, K., Yao, X. (2016). Speciated Evolutionary Algorithm for Dynamic Constrained Optimisation. In: Handl, J., Hart, E., Lewis, P., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds) Parallel Problem Solving from Nature – PPSN XIV. PPSN 2016. Lecture Notes in Computer Science(), vol 9921. Springer, Cham. https://doi.org/10.1007/978-3-319-45823-6_19
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