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Surrogate-assisted evolutionary optimization using on-demand helper task for high-dimensional expensive multi-objective problems
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DOI：10.1016/j.swevo.2025.102273.png)
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Surrogate-assisted evolutionary algorithms (SAEAs) have been recognized as well-suited for solving expensive multi-objective optimization problems (EMOPs). However, most existing SAEAs show promising performance on low-dimensional expensive multi-objective optimization, and rarely pay attention to solving high-dimensional EMOPs. Thus, this work proposes an SAEA using on-demand helper task, termed SAEA-DHT, to efficiently address high-dimensional EMOPs. In SAEA-DHT, an on-demand helper task construction mechanism is proposed to create a low-dimensional helper task tailored to the optimization stage. During the convergence urgent stage, the helper task targets convergence-critical decision variables, whereas in the diversity-demand stage, it shifts the focus to the remaining decision variables. Furthermore, a cross-dimensional knowledge transfer approach is incorporated to efficiently transfer high-quality solutions discovered by the helper task to the original task, thereby accelerating convergence towards the true Pareto front. Finally, an adaptive infill selection scheme is proposed. This scheme dynamically selects infill solutions for exact fitness evaluations based on either convergence-driven or diversity-driven criteria. Extensive experiments are conducted on three well-known benchmarks and time-varying ratio error estimation problems containing up to 200 decision variables. The experimental results demonstrate the advantages of SAEA-DHT over six state-of-the-art SAEAs in solving high-dimensional EMOPs.
Keyword:
High-dimensional EMOPs
SAEAs
On-demand helper task
Knowledge transfer
Infill selection
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8.5
论文数:
2.2K
被引数:
1.0W
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