Searching for global optima is challenging for complex optimization problems since multi-modality commonly exists. To address this issue, researchers have made many attempts, including high-performance initializing methods, novel variant operators and multimodal-based environmental selection strategies. In this talk, we will introduce a helper problem assisted evolutionary algorithms for multi-modal optimization. The algorithm adopts the multitask optimization framework  to transfer knowledge between the helper and the original problems, which then leads the  multi-modal optimization problems to be solved more effectively.

Rui Wang（M’14-SM’22）received his B.S. degree from the National University of Defense Technology (NUDT), P.R. China, in 2008, and the Ph.D. degree from the University of Sheffield, U.K in 2013. Currently, he is with the National University of Defense Technology. His current research interest includes evolutionary computation, multiobjective optimization and the development of algorithms applicable in practice. He has authored more than 40 referred papers including those published in IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, Information Sciences. 

Dr. Wang serves as an Associate Editors of the IEEE Trans. on Evolutionary Computation，Swarm and Evolutionary Computation, Expert System with Applications, etc.. He is the recipients of The Operational Research Society Ph.D. Prize in 2014, of the Funds for Distinguished Young Scientists from the Natural Science Foundation of Hunan province at 2016, of the Wu Wen-Jun Artificial Intelligence Outstanding Young Scholar at 2017, of the National Science Fund for Outstanding Young Scholars at 2021.