AS-PSO , Ant Supervised by PSO Metaheuristic with Application to TSP

Optimization problems occupy a prominent place in engineering, management, process control, it consist in finding a fair solution of a problem in accordance with specific environment, and respecting a given list of constraints. Whether it is to determine the best chemical balance of a product, or to predict future market trends, we need optimization methods. Bio-inspired techniques and swarm intelligence as well as various numerical techniques, are used to solve problems increasingly difficult. This paper investigates a new Meta-heuristic, called ASPSO, Ant Supervised by Particle Swarm Optimization, based on the famous ant colony, ACO, and particle swarm optimization. AS-PSO is an adaptive heuristic, since the user is not in need to fit any parameter of the search strategy. In AS-PSO, the ACO algorithm is in charge with the problem solving, while the PSO is managing the optimality of the ACO parameters. The paper includes also an application of AS-PSO to the travelling Salesman Problem (TSP). Keywords— PSO, ACO, AS-PSO, Optimization, Heuristics, TSP