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

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 thes...

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Vydáno v:IEEE Symposium on Computational Intelligence in Multi-Criteria Decision Making s. 933 - 940
Hlavní autoři: Zhang, Jinyuan, Ishibuchi, Hisao, He, Linjun, Nan, Yang
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 05.12.2023
Témata:
Bridges
Classification algorithms
Computational modeling
Evolutionary computation
Hypercubes
initialization
Measurement
Multiobjective evolutionary algorithms
Optimization
surrogate models
ISSN:2472-8322
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Shrnutí: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.
ISSN:2472-8322
DOI:10.1109/SSCI52147.2023.10371806