In recent years, the collective behavior of ants has inspired wide-ranging research in the field of sociobiology. This is due to the fact that a swarm (ant colony) formed by a group of simple agents (ants) exhibits autonomous behavior that both solves problems with a high degree of reliability and displays a high degree of adaptability. This paper aims to advance two ant-inspired collective problem-solving systems which are based upon the principles and algorithms of ant foraging and object gathering behavior. The ant-inspired algorithm was compared against a genetic algorithm and a simulated annealing algorithm. These experiments revealed that the ant algorithm provided high quality and stable problem-solving mechanisms. Additionally, the results show that these emergent collective problem-solving mechanisms do not require prior planning, central supervision or control. Therefore, these ant-inspired collective problem-solving systems provide problem-solving capabilities which are both autonomous and robust.