This paper describes and analyzes the aggregation pheromone system (APS) algorithm, which extends ant colony optimization (AGO) to continuous domains. APS uses the collective behavior of individuals that communicate using aggregation of pheromones. Two variants of APS are considered: the existing generational APS and the proposed steady-state APS. Both variants of APS are tested on several common unimodal and multimodal problems and their performance on these problems is analyzed with different parameter settings. The results indicate that using a steady-state evolutionary model improves the performance of APS on both unimodal as well as multimodal problems and that the performance of APS is relatively robust with respect to its parameter settings.