Multi-population based nature-inspired optimization algorithms have attracted wide research
interests in the last decade, and become one of the frequently used methods to handle real …

The grey wolf optimizer (GWO) is a new efficient population-based optimizer. The GWO
algorithm can reveal an efficient performance compared to other well-established …

Multiagent reinforcement learning (MARL) has been extensively used in many applications
for its tractable implementation and task distribution. Learning automata, which can be …

Abstract Particle Swarm Optimization (PSO) algorithm is a simple approach with premature
convergence and stagnation prone. The loss of efficiency and sub-optimal solution occur …

This study focuses on an unrelated parallel machine scheduling problem with machine and
job sequence-dependent setup times (UPMST) aiming to minimise the makespan. Recently …

Green machine scheduling consists in the allocation of jobs in order to maximize production,
in view of the sustainable use of energy. This work addresses the unrelated parallel …

This book is written for computer engineers, scientists, and students studying/working in
reinforcement learning and artificial intelligence domains. The book collects recent …

This paper presents a multi-population differential evolution algorithm to address dynamic
optimization problems. In the proposed approach, a cellular learning automaton adjusts the …

One of the major drawbacks of the global particle swarm optimization (GPSO) algorithm is
zigzagging of the direction of search that leads to premature convergence by falling into …

Learning automaton (LA) as one of artificial intelligence techniques is a stochastic model
operating in the framework of the reinforcement learning. LA has been found to be a useful …