Abstract
An improved multi-objective grey wolf algorithm with simulated annealing (MOGWO/SA) is proposed in this paper. Compared with origin algorithm, the new multi-objective grey wolf algorithm has combined with simulated annealing algorithm optimization and the new leading wolf selection mechanism, which makes the algorithm with stronger global searching ability and faster rate of convergence. The diversity of non-dominated solutions and ductility of MOGWO/SA are also improved. Finally, MOGWO/SA are applied to the ecological optimal operation of small hydropower stations for both the maximum output of generated energy and the maximum assurance rate of ecological water requirement.
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Wang, Y., Wang, W., Ren, Q., & Zhao, Y. (2018). Ecological scheduling for small hydropower groups based on grey wolf algorithm with simulated annealing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11151 LNCS, pp. 326–334). Springer Verlag. https://doi.org/10.1007/978-3-030-00560-3_48
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