Сибирский федеральный университет

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: IEEE Congress on Evolutionary Computation (CEC); Rio de Janeiro, Brazil; Rio de Janeiro, Brazil

Год издания: 2018

Ключевые слова: LSHADE, selective pressure, covariance matrix, optimization, crossover, mutation

Аннотация: Solving single-objective real-parameter optimization problems can still cause difficulties, for example if the optimized function is multimodal or has rotated trap problems. Such optimization problems can be found in various areas in real-world applications. Usually, these problems are very complex and computationally expensive. A new algorithm, which is a modification of the LSHADE algorithm with a rank-based selective pressure strategy, called LSHADE-RSP, is presented in this paper. The proposed algorithm is a new variant of the LSHADE algorithm, the basic idea of which consists in the adaptation of its mutation strategy using selective pressure. The experiments were performed on CEC 2018 benchmark functions. A comparison of the proposed LSHADE-RSP algorithm and the algorithm-participants of the CEC 2017 competition is presented. From the obtained results it can be concluded that LSHADE-RSP performs better in comparison with most alternative algorithms: using the CEC 2018 evaluation method, LSHADE-RSP obtained one of the best final scores among the algorithms that were winners of the previous competition.

Журнал: 2018 IEEE Congress on Evolutionary Computation (CEC)

Номера страниц: 210-219

Издатель: IEEE Computer Society

• Stanovov Vladimir (Reshetnev Siberian State University of Science and Technology)
• Akhmedova Sh.A. (Reshetnev Siberian State University of Science and Technology)
• Semenkin E.S. (Reshetnev Siberian State University of Science and Technology)

• РИНЦ (eLIBRARY.RU)