Deep reinforcement learning assisted surrogate model management …
Feb 1, 2025 · In this paper, we have proposed a deep reinforcement learning assisted evolutionary algorithm for expensive constrained multi-objective optimization. By leveraging deep neural networks …
Deep Reinforcement Learning for Multiobjective Optimization
Mar 18, 2020 · This article proposes an end-to-end framework for solving multiobjective optimization problems (MOPs) using deep reinforcement learning (DRL), that we call DRL-based multiobjective …
Surrogate-assisted neural learning and evolutionary optimization for ...
Aug 1, 2025 · To address this issue, we propose an efficient surrogate-assisted constrained multi-objective evolutionary algorithm, named LEMO. LEMO integrates neural learning with a novel …
Physics-informed deep learning for simultaneous surrogate modeling …
Jun 1, 2023 · With multiple nonlinear layers, a deep learning model can approximate complicated functions [23] and, with proper training, any universal function [24]. The optimal parameters of a deep …
Energy-efficient deep learning inference on edge devices
The straightforward solution to these issues is to perform deep learning inference at the edge. However, cost and power-constrained embedded processors with limited processing and memory capabilities …
Multi-objective optimisation of machining process parameters using deep …
Jul 1, 2022 · Therefore, this paper proposes a deep learning based data-driven genetic algorithm and TOPSIS for multi objective optimisation of machining process parameters and searching the final …
A novel intrusion detection framework for optimizing IoT security
Sep 18, 2024 · For example, researchers in 38 introduced an intrusion detection framework for IoT networks employing three deep learning approaches: CNN, LSTM, and a hybrid model combining …
Surrogate modeling for fluid flows based on physics-constrained deep ...
Apr 1, 2020 · Therefore, developing a cost-effective surrogate model is of great practical significance. Deep learning (DL) has shown new promises for surrogate modeling due to its capability of handling …
Constrained multi-objective optimization problems: Methodologies ...
Sep 5, 2024 · Researchers have developed a variety of constrained multi-objective optimization algorithms (CMOAs) to find a set of optimal solutions, including evolutionary algorithms and machine …
Optuna - A hyperparameter optimization framework
Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, define-by-run style user API.