Evolutionary algorithms (EAs) possess strong problem-solving abilities and have been
applied in a wide range of applications. However, they still suffer from a high computational …

Evolutionary multi-task optimization (EMTO) is an optimization algorithm designed to
optimize multiple tasks simultaneously. In real life, different tasks often correlate to each …

When addressing constrained multiobjective optimization problems (CMOPs) via
evolutionary algorithms, various constraints and multiple objectives need to be satisfied and …

The evolutionary algorithm (EA) is a nature-inspired population-based search method that
works on Darwinian principles of natural selection. Due to its strong search capability and …

Knowledge transfer plays a vastly important role in solving multitask optimization problems
(MTOPs). Many existing methods transfer task-specific knowledge, such as the high-quality …

Feature selection (FS) is an important preprocessing technique for improving the quality of
feature sets in many practical applications. Particle swarm optimization (PSO) has been …

This article proposes utilizing a single deep reinforcement learning model to solve
combinatorial multiobjective optimization problems. We use the well-known multiobjective …

Evolutionary multitask optimization is an emerging research topic that aims to solve multiple
tasks simultaneously. A general challenge in solving multitask optimization problems …

Until recently, the potential to transfer evolved skills across distinct optimization problem
instances (or tasks) was seldom explored in evolutionary computation. The concept of …

As transportation system plays a vastly important role in combatting newly-emerging and
severe epidemics like the coronavirus disease 2019 (COVID-19), the vehicle routing …