Differential evolution and differential ant-stigmergy on dynamic optimisation problems

Janez Brest, Peter Korošec, Jurij Šilc, Aleš Zamuda, Borko Bošković and Mirjam Maučec

International Journal of Systems Science, 2013, vol. 44, issue 4, 663-679

Abstract: Many real-world optimisation problems are of dynamic nature, requiring an optimisation algorithm which is able to continuously track a changing optimum over time. To achieve this, we propose two population-based algorithms for solving dynamic optimisation problems (DOPs) with continuous variables: the self-adaptive differential evolution algorithm (jDE) and the differential ant-stigmergy algorithm (DASA). The performances of the jDE and the DASA are evaluated on the set of well-known benchmark problems provided for the special session on Evolutionary Computation in Dynamic and Uncertain Environments. We analyse the results for five algorithms presented by using the non-parametric statistical test procedure. The two proposed algorithms show a consistently superior performance over other recently proposed methods. The results show that both algorithms are appropriate candidates for DOPs.

Date: 2013
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DOI: 10.1080/00207721.2011.617899

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