Different power generation technologies have different advantages and disadvantages.
However, if compared to traditional energy sources, renewable energy sources provide a …

Evolutionary multi-objective optimization aims to provide a representative subset of the
Pareto front to decision makers. In practice, however, decision makers are usually interested …

Evolutionary algorithms have been shown to be very successful in solving multi-objective
optimization problems (MOPs). However, their performance often deteriorates when solving …

This article proposes an end-to-end framework for solving multiobjective optimization
problems (MOPs) using deep reinforcement learning (DRL), that we call DRL-based …

The application of multiobjective evolutionary algorithms to many-objective optimization
problems often faces challenges in terms of diversity and convergence. On the one hand …

Traditional multi-objective optimization evolutionary algorithms (MOEAs) do not usually meet
the requirements for online data processing because of their high computational costs. This …

This paper considers a Green Vehicle Routing Problem (GVRP), which includes heavy-duty
electric and conventional trucks. We develop a new bi-objective programming model …

Multimodal multiobjective problems (MMOPs) arise frequently in the real world, in which
multiple Pareto-optimal solution (PS) sets correspond to the same point on the Pareto front …

This article suggests a multimodal multiobjective evolutionary algorithm with dual clustering
in decision and objective spaces. One clustering is run in decision space to gather nearby …

There has been a recent surge of success in optimizing deep reinforcement learning (DRL)
models with neural evolutionary algorithms. This type of method is inspired by biological …