A hybrid cultural algorithm based on clonal selection principle for optimal generation scheduling of cascaded hydropower stations

To solve optimal generation scheduling problem of cascaded hydropower stations, a hybrid cultural algorithm based on clonal selection principle (HCA-CSA) is presented. HCA-CSA uses cultural algorithm (CA) as its framework and clonal selection algorithm (CSA) in population space. Considering the characteristics of CSA, three knowledge structures are redefined in belief space to improve the search purposefulness and directivity of CSA, so as to improve the searching convergence rate and precision. In addition, a recombination and a chaos search operation are adopted in belief space to accelerate convergence rate and precision of the proposed algorithm. HCA-CSA is first tested by several benchmark problems and then it is applied to a case study of optimal generation scheduling of the Three Gorges Cascaded Hydropower Stations. The results obtained show its efficiency on solving complex optimization problems, and it can be an alternative for optimal generation scheduling of cascaded hydropower stations.