A comparison of particle swarm optimization and differential ant stigmergy algorithm

Differential Ant Stigmergy Algorithm (DASA) is a recent meta-heuristic method which represents an adaptation of Ant Colony Optimization (ACO) to continuous optimization problems. Other adaptations of ACO to continuous optimization exist but DASA seems to be very efficient for the class of highdimension real-parameters optimization problems, becoming a competitor to more classical methods such as Particle Swarm Optimization (PSO). The PSO is a meta-heuristic which is also inspired from insects´ life as ACO. Even both methods use a population of entities, the memory of PSO is larger as those of DASA. DASA uses just one current solution around which the population search and the difference that generated last improvement. PSO stores the previous best position and velocity of every particle. This paper attempts to examine if an elitist version of DASA has at least the same effectiveness (finding the true global solution) as PSO, but with significantly better efficiency (less function evaluation). The performance comparison of DASA and PSO is implemented using a set of six test functions well known for their difficulty.