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Heterogeneous approximation-assisted search for expensive multi-objective optimization

Year: 2025
Type: article
Abstract: The cheap surrogate model is commonly used to guide the multi-objective optimization algorithm in the search for the optimum of the expensive optimization problem. However, modeling diversity and its quality are the keys that affect the performance of approximating the original problem. Using multiple heterogeneous models can provide more diverse approximations for complicated optimization problems. Meanwhile, the location relationship between individuals and training samples is a potential benefit for selecting infill individuals to update the model. Therefore, this paper proposes to train two heterogeneous models for each expensive objection function, with the update of the models using the promising individuals based on the approximated domination relationship and the crowding distance between individuals and evaluated samples. Differently, the function estimation of each individual is the sum of two predicted values in a probability-weighted way together with its uncertainty. Besides, the promising individuals are selected from those candidates that dominate training samples according to the domination numbers and the crowding distance to the neighbors, otherwise adopting the distance difference based on the convergence and crowding between all candidates and the training data to select the individual for expensive function evaluations. Experimental studies analyze the effectiveness of the heterogeneous approximation-based guiding search and examine the superiority of the proposed algorithm based on the comparison with five recent epidemic optimization algorithms on DTLZ, WFG benchmark problems, and a practical application.
DOI: 10.1016/j.swevo.2025.101926
Language: English
Open Access status: hybrid
Estimated APC paid: $3,690
OpenAlex ID: W4409152908
FWCI: 0.953
Cites: 52
Cited by: 2
Related to: 10