Differential evolution and differential ant-stigmergy on dynamic optimisation problems

Authors:
Janez Brest;Peter Koroš/ec;Jurij Š/ilc;Aleš/ Zamuda;Borko Boš/ković/;MirjamSepesy Mauč/ec
Affiliations:
Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova ulica 17, 2000, Maribor, Slovenia;Computer Systems Department, Jo$#x17E/ef Stefan Institute, Jamova cesta 39, 1000, Ljubljana, Slovenia;Computer Systems Department, Jo$#x17E/ef Stefan Institute, Jamova cesta 39, 1000, Ljubljana, Slovenia;Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova ulica 17, 2000, Maribor, Slovenia;Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova ulica 17, 2000, Maribor, Slovenia;Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova ulica 17, 2000, Maribor, Slovenia
Venue:
International Journal of Systems Science
Year:
2013

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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.