<p>Surrogate-assisted evolutionary algorithms (SAEAs) are a promising approach for solving expensive multiobjective optimization problems, but they often cannot address high-dimensional problems. Although one common approach to this challenge is to construct reliable surrogates, their accuracy inevitably deteriorates in a high-dimensional search space. Thus, this paper presents a novel SAEA based on scalarization function approximation, which is designed to strengthen its robustness against this deterioration. The proposed algorithm constructs an approximation model for each scalarization function defined in a decomposition-based framework. Each decomposed problem is then solved using multiple independent models trained for its neighbor problems. The intent is to decrease the risk of search performance degradations caused by unreliable approximations and retain the redundancy of the surrogate-assisted search to hedge the risk of over-fitting. Furthermore, each approximation model is adapted to a promising region of its corresponding decomposed problem to reduce the complexity of model fitting given a limited number of training samples. Experimental results show that the proposed algorithm is competitive with state-of-the-art SAEAs adapted for high-dimensional problems.</p>
The design of a freight loading pattern is often conducted by skilled workers, who handle unquantifiable objectives and/or preferences. Our previous study presented an automatic construction technique for loading algorithms using genetic programming-based hyper-heuristics; however, this technique is only applicable to fully quantifiable loading problems. Thus, the approach described in this paper integrates an interactive framework with users into our previous technique to automatically construct algorithms that derive loading patterns adapted to user objectives and/or preferences. Thus, once a loading algorithm has been derived with user interactions, it can be reused to obtain the preferred loading patterns on other problems without any additional interactions. Experimental results show that the proposed algorithm can produce loading algorithms adapted for user preferences under a limit of 50 human interactions. Further, we also show that the derived loading algorithms can be applicable to different loading situations without any additional user interactions. Thus, these observations suggest the benefit of our approaches in reducing the burden placed on skilled workers for practical LPD tasks.
<p>The approximation of objective functions is a major strategy in surrogate-assisted multi-objective evolutionary algorithms, but it tends to underperform on high-dimensional problems. We hypothesize that this is because the above strategy is vulnerable to unreliable approximations and even a single unreliable approximation model may mislead the entire search process. Therefore, an alternative strategy is to approximate each scalarization function, whereby candidate solutions for a decomposed problem can be evaluated using a single approximation model, which prevents the negative propagation of unreliable approximations to the entire search process. Accordingly, this study aims to confirm our hypothesis by introducing a basic surrogate-assisted algorithm, in which each approximated scalarization function is independently optimized by a differential evolution algorithm. Despite its methodological simplicity, the significant impact of approximating scalarization functions on high-dimensional problems is revealed for the first time. The presented algorithm is competitive with state-of-the-art algorithms that are adapted for high-dimensional problems, while exhibiting a reduced computational time. This computational efficiency is theoretically confirmed by our complexity analysis.</p>
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