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Comprehensive learning cuckoo search with chaos-lambda method for solving economic dispatch problems
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DOI：10.1007/s10489-020-01654-y.png)
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Abstract
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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.
Keywords:
Economic dispatch
Swarm intelligence
Cuckoo search
Power systems
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