QLOA: A Self-Adaptive Q-Learning-Based Optimization Algorithm with Dynamic Mathematical Formulation Selection

Adaptive operator selection via deep reinforcement learning for decomposition-based multi-objective multi-task optimization

Multi-objective multi-task optimization (MO-MTO), which aims to simultaneously solve multiple related multi-objective problems by leveraging knowledge transfer across tasks to enhance optimization performance on each task, has emerged as a new research focus in the field of evolutionary computation. Although a variet...

Competitive learning-based atom search optimization with parameter adaptation for numerical optimization and multi-robot path planning

Autoencoder-and-Surrogate Model-based Evolutionary Algorithms for Complex Optimization Problems and Their Applications

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.

A hybrid dual-population cooperative immune algorithm for efficient high-dimensional feature selection

High-dimensional data analysis is fundamental to numerous domains, yet it is frequently impeded by the curse of dimensionality, which complicates pattern recognition and model generalization. However, existing high-dimensional feature selection methods are often limited by slow convergence, insufficient population di...

Constraint handling technique and search strategy selection based on deep reinforcement learning for constrained multiobjective optimization

An Improved Escape Algorithm with Reinforcement Learning and Multi-Strategy Synergy for Global Optimization and Real Problems

To address the limitations of the standard Escape Algorithm (ESC), including its tendency to become trapped in local optima and its slow convergence speed in complex optimization scenarios, this paper proposes an Improved Escape Algorithm (IESC) for global optimization and robot path planning. First, a reinforcement le...