@inproceedings{038484be4e3740918a5db8749f5ecec3,
title = "Improved differential evolution based on mutation strategies",
abstract = "Differential Evolution (DE) has been regarded as one of the excellent optimization algorithm in the science, computing and engineering field since its introduction by Storm and Price in 1995. Robustness, simplicity and easiness to implement are the key factors for DE{\textquoteright}s success in optimization of engineering problems. However, DE experiences convergence and stagnation problems. This paper focuses on DE convergence speed improvement based on introduction of newly developed mutation schemes strategies with reference to DE/rand/1 on account and tuning of control parameters. Simulations are conducted using benchmark functions such as Rastrigin, Ackley and Sphere, Griewank and Schwefel function. The results are tabled in order to compare the improved DE with the traditional DE.",
keywords = "Control parameters, Convergence speed, Differential Evolution, Mutation scheme",
author = "John Saveca and Zenghui Wang and Yanxia Sun",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 9th International Conference on Swarm Intelligence, ICSI 2018 ; Conference date: 17-06-2018 Through 22-06-2018",
year = "2018",
doi = "10.1007/978-3-319-93815-8_23",
language = "English",
isbn = "9783319938141",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "233--242",
editor = "Ying Tan and Yuhui Shi and Qirong Tang",
booktitle = "Advances in Swarm Intelligence - 9th International Conference, ICSI 2018, Proceedings",
address = "Germany",
}