Metaheuristic Algorithm
Metaheuristic algorithms are problem-solving techniques inspired by natural processes, aiming to find near-optimal solutions for complex optimization problems where traditional methods fall short. Current research emphasizes improving algorithm efficiency and robustness, focusing on enhancements like incorporating machine learning for guidance, developing novel algorithms inspired by diverse natural phenomena (e.g., animal behavior, water dynamics), and employing advanced techniques such as quantum computing and chaos theory. These advancements are significant for tackling computationally challenging problems across various fields, including engineering design, logistics, and healthcare, leading to improved solutions and more efficient resource allocation.
Papers
Combining Genetic Programming and Particle Swarm Optimization to Simplify Rugged Landscapes Exploration
Gloria Pietropolli, Giuliamaria Menara, Mauro Castelli
A new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problem
Erick Rodríguez-Esparza, Antonio D Masegosa, Diego Oliva, Enrique Onieva