@inproceedings{05274bb66f1543c8bb1e49a625f2cb16,
title = "Multi-strategy Differential Evolution",
abstract = "{\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.We propose the Multi-strategy Differential Evolution (MsDE) algorithm to construct and maintain a self-adaptive ensemble of search strategies while solving an optimization problem. The ensemble of strategies is represented as agents that interact with the candidate solutions to improve their fitness. In the proposed algorithm, the performance of each agent is measured so that successful strategies are promoted within the ensemble. We propose two performance measures, and show their effectiveness in selecting successful strategies. We then present three population adaptation mechanisms, based on sampling, clone-best and clone-multiple adaptation schemes. The MsDE with different performance measures and population adaptation schemes is tested on the CEC2013 benchmark functions and compared with basic DE and with Self-Adaptive DE (SaDE). Our results show that MsDE is capable of efficiently adapting the strategies and parameters of DE and providing competitive results with respect to the state-of-the-art.",
author = "A. Yaman and G. Iacca and M. Coler and G. Fletcher and M. Pechenizkiy",
year = "2018",
doi = "10.1007/978-3-319-77538-8_42",
language = "English",
isbn = "9783319775371",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "617--633",
editor = "K. Sim and P. Kaufmann",
booktitle = "Applications of Evolutionary Computation - 21st International Conference, EvoApplications 2018, Proceedings",
note = "21st International Conference on Applications of Evolutionary Computation, EvoApplications 2018 ; Conference date: 04-04-2018 Through 06-04-2018",
}