Affiliations: [a] College of Software, Sungkyunkwan University (SKKU), 2066 Seobu-ro Jangan-gu Suwon-si Gyeonggi-do, Republic of Korea. {gry17, ljh08375, youn7147}@skku.edu | [b] School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), 123 Cheomdangwagi-ro Buk-gu Gwangju, Republic of Korea. [email protected]
Correspondence:
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Address for correspondence: School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), 123 Cheomdangwagi-ro Buk-gu Gwangju, Republic of Korea
Abstract: Differential Evolution (DE) algorithm is one of the popular evolutionary algorithms that is designed to find a global optimum on multi-dimensional continuous problems. In this paper, we propose a new variant of DE algorithm by combining a self-adaptive DE algorithm called dynNP-DE with Elite Opposition-Based Learning (EOBL) scheme. Since dynNP-DE algorithm uses a small number of population size in the later of the search process, the population diversity becomes low, and therefore premature convergence may occur. We have therefore extended an OBL scheme to dynNP-DE algorithm to overcome this shortcoming and improve the optimization performance. By combining EOBL scheme to dynNP-DE algorithm, the population diversity can be supplemented because not only the information of individuals but also their opposition information can be utilized. We measured the optimization performance of the proposed algorithm on CEC 2005 benchmark problems and breast cancer detection, which is a research field that has recently attracted a lot of attention. It was verified that the proposed algorithm could find better solutions than five state-of-the-art DE algorithms.
