Multi-objective optimisation using evolutionary algorithms constitutes a powerful computational framework that addresses complex problems involving conflicting objectives. By emulating natural ...

Abstract: With the rapid development of integrated energy systems (IESs), the system structure is becoming increasingly complex, which brings challenges to the reliable operation of IESs. In this ...

Abstract: This paper proposes a real-world cardboard production planning optimization system based on multi-objective evolutionary algorithms. The cardboard production planning is a kind of ...

Neural network pruning is a key technique for deploying artificial intelligence (AI) models based on deep neural networks (DNNs) on resource-constrained platforms, such as mobile devices. However, ...

We study the multiple objective discrete optimization (MODO) problem and propose two-stage optimization problems as subproblems to be solved to obtain efficient solutions. The mathematical structure ...

Pairing artificial intelligence techniques called Q-learning and advantage actor-critic provides new way to optimize hybrid photovoltaic-thermoelectric systems.

In the fast-evolving field of electronic systems design, engineers are under increasing pressure to deliver innovative, high-performance products within ever ...

This repository contains the implementation of a novel multi-agent reinforcement learning approach for molecular optimization and drug discovery, combining genetic algorithms with deep reinforcement ...

1. Effective decision-making in environmental management requires the consideration of multiple objectives that may conflict. Common optimization methods use weights on the multiple objectives to ...