TL;DR
Compared with the state-of-the-art algorithms for HEPs, AEO shows extraordinarily high efficiency for these challenging problems while SAEO can greatly improve the performance of AEO in most cases, thus opening new directions for various swarm optimization and evolutionary algorithms under both AEO and SAEO.
High-dimensional black-box optimization is a highly challenging problem, especially when each function evaluation is expensive, the objective is non-differentiable, and we know almost nothing about its structure. In this case, traditional gradient-based methods are not available, and most existing metaheuristics either...
Neural Architecture Search (NAS), often formulated as a bilevel optimization problem, presents significant challenges characterized by discrete search spaces and computationally expensive performance evaluations. Standard evolutionary metaheuristics frequently encounter difficulties in such landscapes due to ineffici...
An objective-wise variable analysis method that first evaluates the sensitivity of each objective to all decision variables, and then comprehensively aggregates the sensitivity information across multiple objectives to estimate the overall importance of decision variables is proposed.
A comparative analysis of two metaheuristic algorithms — Particle Swarm Optimization and Dwarf Mongoose Optimization — as advanced alternatives for hyperparameter tuning in deep learning models trained on the CIFAR-10 dataset reinforces the potential of metaheuristic-based optimization as a robust framework for hyperpa...
Results show that integrating local search significantly enhances performance, while a principled method for setting hybrid parameters ensures robustness and reproducibility, highlighting the potential of combining mathematical programming techniques with evolutionary algorithms for high-dimensional many-objective opti...
A heterogeneous population co-evolutionary algorithm (HPCEA) tailored for sparse LSMOPs is proposed, and extensive experiments against six state-of-the-art algorithms across eight benchmark suites and three real-world scenarios demonstrate HPCEA’s superiority.
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