Global optimization using Lévy flights
This work addresses optimization problems for researchers and practitioners, but it appears incremental as it builds on existing methods like Simulated Annealing.
The paper tackles global optimization by developing four algorithms based on Lévy flights to balance exploitation and exploration, achieving promising results compared to Simulated Annealing on hard test functions.
This paper studies a class of enhanced diffusion processes in which random walkers perform Lévy flights and apply it for global optimization. Lévy flights offer controlled balance between exploitation and exploration. We develop four optimization algorithms based on such properties. We compare new algorithms with the well-known Simulated Annealing on hard test functions and the results are very promising.