Solution of ECO for Unit Commitment Problem

Versione 1.0.0 (6,68 KB) da praveen kumar

This program implements Unit commitment problem by Ecological cycle Optimizer

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Aggiornato 2 set 2025

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10 unit system with 24 hrs sheduling is tested

the sample output

=== ECO UC with Spinning Reserve ===

Best fitness (objective): $563642.50

Final Total Cost (Fuel + Startup): $563102.50

Fuel Cost (approx.): $559552.50

Total Startup Cost: $3550.00

Generator commitment (rows: generators 1..10, cols: hours 1..24)

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0

0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 0 0

0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 0

0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

Cita come

Ma, Boyu, et al. Ecological Cycle Optimizer: A Novel Nature-Inspired Metaheuristic Algorithm for Global Optimization. arXiv, 2025, https://doi.org/10.48550/ARXIV.2508.20458.

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Compatibilità della release di MATLAB

Creato con R2025a

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ECO_UCP

runecoucp.m

Versione	Pubblicato	Note della release
1.0.0	2 set 2025		Scarica

MLA	Ma, Boyu, et al. Ecological Cycle Optimizer: A Novel Nature-Inspired Metaheuristic Algorithm for Global Optimization. arXiv, 2025, https://doi.org/10.48550/ARXIV.2508.20458.
APA	Ma, B., Shi, J., Ji, Y., & Wang, Z. (2025). Ecological Cycle Optimizer: A novel nature-inspired metaheuristic algorithm for global optimization. arXiv. Retrieved from https://arxiv.org/abs/2508.20458
BibTeX	@misc{https://doi.org/10.48550/arxiv.2508.20458, doi = {10.48550/ARXIV.2508.20458}, url = {https://arxiv.org/abs/2508.20458}, author = {Ma, Boyu and Shi, Jiaxiao and Ji, Yiming and Wang, Zhengpu}, keywords = {Neural and Evolutionary Computing (cs.NE), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Ecological Cycle Optimizer: A novel nature-inspired metaheuristic algorithm for global optimization}, publisher = {arXiv}, year = {2025}, copyright = {arXiv.org perpetual, non-exclusive license} }