Effects of Initialization Methods on the Performance of Surrogate-Based Multiobjective Evolutionary Algorithms

Jinyuan Zhang; Hisao Ishibuchi; Linjun He; Yang Nan

10.1109/SSCI52147.2023.10371806

2023

2770-0097

978-1-6654-3064-7

2023 IEEE Symposium Series on Computational Intelligence (SSCI)

933-940

5-8 Dec. 2023

Mexico City, Mexico

Initialization plays a crucial role in surrogate-based multiobjective evolutionary algorithms (MOEAs) when tackling computationally expensive multiobjective optimization problems. During the initialization process, solutions are generated to train surrogate models. Consequently, the accuracy of these surrogate models depends on the quality of the initial solutions, which in turn directly impacts the performance of surrogate-based MOEAs. Despite the widespread use of Latin hypercube sampling as an initialization method in surrogate-based MOEAs, there is a lack of comprehensive research examining the effectiveness of different initialization methods. Additionally, the impact of the number of initial solutions on the performance of surrogate-based MOEAs remains largely unexplored. This paper aims to bridge these research gaps by comparing the usefulness of two commonly employed initialization methods (i.e., random sampling and Latin hypercube sampling) in surrogate-based MOEAs. Furthermore, it investigates how varying the number of initial solutions influences the performance of surrogate-based MOEAs.

Multiobjective evolutionary algorithms initialization surrogate models

第一

EI

20240415441879

IEEE

Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, China

Jinyuan Zhang,Hisao Ishibuchi,Linjun He,et al. Effects of Initialization Methods on the Performance of Surrogate-Based Multiobjective Evolutionary Algorithms[C],2023:933-940.