Application of the dynamic ant colony algorithm on the optimal operation of cascade reservoirs

International Conference on Water Resource and Environment 2016

地球科学;生态环境科学

Tong, X.X.^1 ; Xu, W.S.^1 ; Wang, Y.F.^1 ; Zhang, Y.W.^1 ; Zhang, P.C.^1

Changjiang River Scientific Research Institute of Changjiang Water Resources Commission, Wuhan, China^1

关键词: Ant colony algorithms;    Cascade reservoirs;    Dynamic adjustment;    Joint operations;    Local optimizations;    Optimal operation of cascade reservoirs;    Optimization method;    Pre-mature convergences;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/39/1/012042/pdf
DOI  :  10.1088/1755-1315/39/1/012042

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Due to the lack of dynamic adjustments between global searches and local optimization, it is difficult to maintain high diversity and overcome local optimum problems for Ant Colony Algorithms (ACA). Therefore, this paper proposes an improved ACA, Dynamic Ant Colony Algorithm (DACA). DACA applies dynamic adjustments on heuristic factor changes to balance global searches and local optimization in ACA, which decreases cosines. At the same time, by utilizing the randomness and ergodicity of the chaotic search, DACA implements the chaos disturbance on the path found in each ACA iteration to improve the algorithm's ability to jump out of the local optimum and avoid premature convergence. We conducted a case study with DACA for optimal joint operation of the Dadu River cascade reservoirs. The simulation results were compared with the results of the gradual optimization method and the standard ACA, which demonstrated the advantages of DACA in speed and precision.