Thesis (BS Computer Science) -- University of the Philippines Mindanao, 2010

Time cost optimization (TCO) may be define as a process to identify suitable construction activities for speeding up, and for deciding "by how much" so as to attain the best possible savings in both time and cost. It is generally realized that when project duration is compressed, the project will call for an increase in labor and more productive equipment, and require more demanding procurement and construction management, resulting to increase of cost. On the other hand, using fewer resources will result in extended duration of activities. In this papare, we have proposed a discrete adaptation of the artificial bee colony (ABC) algorithm for the time-cost trade-off problem. The ABC algorithm is a new metaheuristic approach inspired by intelligent foraging behavior of honeybee swarm.We have compared the performance of our discretely adapted ABC against three algorithms: Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and genetic algorithm (GA). Computational results demonstrate the superiority of the discrete ABC over the three algorithms. It obtained better qualtiy solutions in shorter time.