An improved ant colony optimization (ACO) was developed based on the characteristics of resource-constrained project scheduling problem (RCPSP). In the algorithm, a new permutation of priorities-based encoding scheme is employed, and the summation evaluation is applied to direct the moving of ants. The priority rule pool, in which lots of priority rules can be managed, is presented to strengthen the learning ability and adaptability of ACO. Taking full advantage of ACO, each ant is provided a single thread and the algorithm is realized in the multithreading architecture. A full factorial computational experiment is set up using the well-known standard instances in PSPLIB, and the algorithm given in this paper is compared to the existing swarm intelligence optimization algorithms, the results reveal that the algorithm is effective for the RCPSP.
['Li Zhou', 'Dong Wang', 'Wuliang Peng']
An ACO for Solving RCPSP