Sung Tien-Wen, Zhao Baohua, Zhang Xin
Fujian Provincial Key Laboratory of Big Data Mining and Applications, College of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, Fujian, China.
PeerJ Comput Sci. 2022 Jun 17;8:e1007. doi: 10.7717/peerj-cs.1007. eCollection 2022.
In recent years, evolutionary algorithms based on swarm intelligence have drawn much attention from researchers. This kind of artificial intelligent algorithms can be utilized for various applications, including the ones of big data information processing in nowadays modern world with heterogeneous sensor and IoT systems. Differential evolution (DE) algorithm is one of the important algorithms in the field of optimization because of its powerful and simple characteristics. The DE has excellent development performance and can approach global optimal solution quickly. At the same time, it is also easy to get into local optimal, so it could converge prematurely. In the view of these shortcomings, this article focuses on the improvement of the algorithm of DE and proposes an adaptive dimension differential evolution (ADDE) algorithm that can adapt to dimension updating properly and balance the search and the development better. In addition, this article uses the elitism to improve the location update strategy to improve the efficiency and accuracy of the search. In order to verify the performance of the new ADDE, this study carried out experiments with other famous algorithms on the CEC2014 test suite. The comparison results show that the ADDE is more competitive.
近年来，基于群体智能的进化算法引起了研究人员的广泛关注。这种人工智能算法可用于各种应用，包括当今具有异构传感器和物联网系统的现代世界中的大数据信息处理。差分进化（DE）算法因其强大且简单的特性，是优化领域的重要算法之一。DE具有出色的发展性能，能快速逼近全局最优解。同时，它也容易陷入局部最优，可能会过早收敛。针对这些缺点，本文重点对DE算法进行改进，提出了一种自适应维度差分进化（ADDE）算法，该算法能适当地适应维度更新，更好地平衡搜索和开发。此外，本文采用精英策略改进位置更新策略，以提高搜索的效率和准确性。为了验证新的ADDE的性能，本研究在CEC2014测试套件上与其他著名算法进行了实验。比较结果表明，ADDE更具竞争力。
