Speaker:

王祖玲(杭州师范大学硕士研究生)

Time:

• 19:30 – 20:30 (Time in Beijing)
• December 3, 2021 (Friday)

Venue:

报告形式：线上会议

报告平台：腾讯会议（会议ID：335-281-241）

Abstract:

How to effectively solve the global optimization problem is always an inevitable problem in scientific research and engineering practice. Evolutionary algorithm is a random search algorithm based on population and independent of derivative, which can effectively solve complex optimization problems. Differential evolution algorithm is a branch of evolutionary algorithm, which has attracted extensive attention due to its good convergence and easy implementation. It has become one of the most popular random search algorithms and has been successfully applied to solve various optimization problems. We study the global search and local search ability and the selection of its mutation strategy in differential evolution algorithm, and evaluate it on the benchmark set of global optimization problem, and prove that the improved differential evolution algorithm has better performance compared with related algorithms.

Speaker Bio:

王祖玲，杭州师范大学硕士研究生，主要研究方向为智能进化算法。通过改进相关进化算法，在公认的基准函数(单模态，多模态)上进行实验，结果表明所提出的算法的有效性和可行性。目前已在Information Sciences（SCI 一区）以第一作者发表论文一篇。