Improved Genetic Algorithm-Simulated Annealing Hybrid Algorithm for the Optimization of Multiple Reservoirs

• Published in: Water resources management Vol. 22; no. 8; pp. 1031 - 1049
• Main Authors: Li, Xun-Gui, Wei, Xia
• Format: Journal Article
• Language: English
• Published: Dordrecht Dordrecht : Springer Netherlands 01.08.2008; Springer Netherlands; Springer; Springer Nature B.V
• Subjects: Algorithms; Annealing; Atmospheric Sciences; case studies; China; Civil Engineering; Cooling; dynamic programming; Earth and Environmental Science; Earth Sciences; Earth, ocean, space; electricity; Environment; Exact sciences and technology; Genetic algorithms; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Hydrology; Hydrology. Hydrogeology; Hydrology/Water Resources; Mutation; Optimization; Optimization of multiple reservoirs; Population; Production planning; Reservoirs; Rivers; Sensitivity analysis; Simulated annealing; Studies; Water resources; Yangtze River; Far East; Asia; China; Yangtze River; China, People's Rep., Changjiang R; Sensitivity analysis; Optimization of multiple reservoirs; Simulated annealing; Genetic algorithms; surface water; algorithms; cost; hydroelectric energy; sensitivity analysis; optimization; Simulated annealing .Sensitivity analysis; water resource management; reservoirs; Optimization of multiple reservoirs .Genetic algorithms; electricity; case studies
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-007-9209-5

A hybrid evolutionary search algorithm is developed to optimize the classical single-criterion operation of multi-reservoir systems. The proposed improved genetic algorithm-simulated annealing (IGA-SA) which combines genetic algorithms (GAs) and the simulated annealing (SA) is a new global optimization algorithm. The algorithm is capable of overcoming the premature convergence of GAs and escaping from local optimal solutions. In addition, it is faster than a traditional unimproved GA-SA algorithm. A case study of optimization operation on generation electricity of a 3-reservoir system in series over 41-year (from May 1940 to April 1981) time periods in Wujiang River, one branch of Yangtze River in China, was performed. The objective is to maximize generation output from the system over each 12-month operating periods. Trade-off analyses on binary coding representation and real-value coding representation of GAs are performed. Sensitivity to some parameters of the GA, the SA and the IGA-SA is analyzed, respectively, and the appropriate values of parameters are suggested. The performance of the proposed algorithm is compared with that of the existing genetic algorithm, the simulated annealing and the dynamic programming (DP). Results demonstrate that the GA is better than the DP, the SA performs better than the GA and the IGA-SA is more efficient than SA. The IGA-SA produces higher quality solutions and costs less computation time compared with the traditional GA-SA. The results obtained from these applications have proved that the IGA-SA has the ability of addressing large and complex problems and is a new promising search algorithm for multi-reservoir optimization problems.