A hybrid genetic algorithm for the discrete timecost trade-off problem
In this paper we present a hybrid strategy developed using genetic algorithms (GAs), simulated annealing (SA), and quantum simulated annealing techniques (QSA) for the discrete timecost trade-off problem (DTCTP). In the hybrid algorithm (HA), SA is used to improve hill-climbing ability of GA. In addition to SA, the hybrid strategy includes QSA to achieve enhanced local search capability. The HA and a sole GA have been coded in Visual C++ on a personal computer. Ten benchmark test problems with a range of 18 to 630 activities are used to evaluate performance of the HA. The benchmark problems are solved to optimality using mixed integer programming technique. The results of the performance analysis indicate that the hybrid strategy improves convergence of GA significantly and HA provides a powerful alternative for the DTCTP.
genetic algorithms discrete timecost trade-off problem optimization project management