Comprehensive learning cuckoo search with chaos-lambda method for solving economic dispatch problems

Abstract

Economic dispatch (ED) is an important part in the economic operation of power systems. It is an NP-hard problem with multiple practical constraints. This paper proposes a novel approach that combines a swarm intelligence algorithm with a constraint-handling mechanism to solve the ED problem. First, we design a comprehensive learning cuckoo search algorithm with two strengthen strategies. A comprehensive learning strategy is designed to give the algorithm advanced learning ability in high-dimensional and multi-modal environment and thus enhance the search ability. A duplicate elimination strategy is utilized as an elite strategy to improve the evolving efficiency of the algorithm. Then, we propose a constraint-based population generation method named chaos-lambda method to reduce the searching complexity, and a solution repair method to repair unfeasible solutions that violate the constraints. The proposed approach is tested on 5 systems with different benchmarks and compared with the state-of-the-art algorithms. Our approach achieves the best performance on every test.

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Acknowledgements

This work is supported in part by NSFC (61472229, 616-02279, and 71704096), Sci. & Tech. Development Fund of Shandong Province of China (ZR2017BF015 and ZR2017M-F027), the Humanities and Social Science Research Project of the Ministry of Education (18YJAZH017), the Taishan Scholar Climbing Program of Shandong Province, and SDUST Research Fund (2015TDJH102).

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Correspondence to Liang Qi or Zhengzhong Gao or Hua Duan.

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Huang, Z., Zhao, J., Qi, L. et al. Comprehensive learning cuckoo search with chaos-lambda method for solving economic dispatch problems. Appl Intell 50, 2779–2799 (2020). https://doi.org/10.1007/s10489-020-01654-y

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Keywords

  • Economic dispatch
  • Swarm intelligence
  • Cuckoo search
  • Power systems