An Efficient and Universal Conical Hypervolume Evolutionary Algorithm in Three or Higher Dimensional Objective Space
Weiqin Ying, Yuehong Xie, Xing Xu, Yu Wu, An Ping Xu, Zhenyu Wang · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · 2015
The conical area evolutionary algorithm (CAEA) has a very high run-time efficiency for bi-objective optimization, but it can not tackle problems with more than two objectives. In this letter, a conical hypervolume evolutionary algorithm (CHEA) is proposed to extend the CAEA to a higher dimensional objective space. CHEA partitions objective spaces into a series of conical subregions and retains only one elitist individual for every subregion within a compact elitist archive. Additionally, each offspring needs to be compared only with the elitist individual in the same subregion in terms of the local hypervolume scalar indicator. Experimental results on 5-objective test problems have revealed that CHEA can obtain the satisfactory overall performance on both run-time efficiency and solution quality.