Abstract
Ant Colony Optimization (ACO) is a widely applied meta-heuristic algorithm. Little researches focused on the candidate selection mechanism, which was developed based on the simple uniform distribution. This paper employs the Levy flight mechanism based on Levy distribution to the candidate selection process and takes advantage of Levy flight that not only guarantees the search speed but also extends the searching space to improve the performance of ACO. Levy ACO incorporating with Levy flight developed on the top of Max-min ACO. According to the computational experiments, the performance of Levy ACO is significantly better than the original Max-min ACO and some latest Traveling Salesman Problem (TSP) solvers.
Highlights
A meta-heuristic is a high-level problem-independent algorithmic framework that provides a set of guidelines or strategies to develop heuristic optimization algorithms
Most of them based on a metaphor of some natural or man-made process, i.e, Particle Swarm Optimization (PSO) [1], [2] [3], Simulated Annealing [4]–[6], Whale Optimization Algorithm [7], [8], Moth-flame optimization [9], and Salps algorithm [10]
When solving a Traveling Salesman Problem (TSP), Ant Colony Optimization (ACO) utilizes positive feedback to increase the pheromone to focus on better solutions and negative feedback to decrease the pheromone to reduce the impacts of historical solutions [12]
Summary
A meta-heuristic is a high-level problem-independent algorithmic framework that provides a set of guidelines or strategies to develop heuristic optimization algorithms. Levy flight mechanism has been applied to improve some meta-heuristics such as Particle Swarm Optimization (PSO) [1]–[3], Artificial Bee Colony algorithm [42], Cuckoo Search algorithm [43], [44]. It can be directly employed in spatial search approaches, for instance, PSO and Cuckoo Search algorithm, but cannot be directly applied in ACO without appropriate design
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call