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
An Adaptive Simulated Annealing-Based Machine Learning Approach for Developing an E-Triage Tool for Hospital Emergency Operations
Abdulaziz Ahmed, Mohammed Al-Maamari, Mohammad Firouz, Dursun Delen
A Study of Left Before Treatment Complete Emergency Department Patients: An Optimized Explanatory Machine Learning Framework
Abdulaziz Ahmed, Khalid Y. Aram, Salih Tutun
A machine learning framework for neighbor generation in metaheuristic search
Defeng Liu, Vincent Perreault, Alain Hertz, Andrea Lodi