Modified teaching learning based optimization with hummingbird flight strategy to solve the single and multi-area dynamic economic dispatch.

This paper introduces an enhanced variant of the teaching-learning-based optimization (TLBO) algorithm improved with the hummingbird flight strategy (HBF) (TLBO-HBF), together with more efficient step sizes and adjustment coefficients for addressing diverse optimization challenges. TLBO-HBF integrates TLBO's optimization method with HBF's operations, to obtain improved efficacy over previous methods. Extensive comparison is conducted, to analysis the efficacy of the TLBO-HBF against 8 variants of TLBO and 10 other optimizers including 7 state-of-the-art algorithms using the IEEE CEC-2014 and CEC-2013 functions across dimensions of 30 and 1000, respectively. Results demonstrate that the new improvements could enhance the model of the TLBO in solution quality and performance is boosted compared to other methods. Furthermore, TLBO-HBF is applied to real-world scenarios, notably addressing the reserve-constrained dynamic economic dispatch (DED) problem in 10-unit and 30-unit systems. Also, two multi-area cases, including the 2-area and 4-area with 40-unit systems, have been investigated. The proposed algorithm achieved a rank of 1 across all test cases for the economic dispatch problem, with the maximum observed cost advantage amounting to $27,064. This research underscores TLBO-HBF's practical model and potential for high-quality optimization applications.