Работы, цитирующие эту работу

Работ: 5

Работа: Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm

1. Optimal Planning of Distribution Network with DG Units and ESS Considering Costs

   Mansur Khasanov, Salah Kamel, Abror Kurbanov +3

   СтатьяOptimal Power Flow Distribution2023Цитирований: 1

   ABIABI:AkademIndex/openalex/2023.article.003353

2. Improve carbon dioxide emission prediction in the Asia and Oceania (OECD): nature-inspired optimisation algorithms versus conventional machine learning

   Loke Kok Foong, Vojtěch Blažek, Lukáš Prokop +3

   СтатьяAir Quality Monitoring and ForecastingEngineering Applications of Computational Fluid Mechanics2024Цитирований: 0

   ABIABI:AkademIndex/openalex/2024.article.000820

3. Optimal sizing of hybrid energy system using an improved gazelle optimization algorithm

   Priyanka Anand, Vikram Kumar Kamboj, M. Rizwan

   СтатьяHybrid Renewable Energy SystemsElectrical Engineering2024Цитирований: 0

   ABIABI:AkademIndex/openalex/2024.article.003136

4. Salp Navigation and Competitive based Parrot Optimizer (SNCPO) for efficient extreme learning machine training and global numerical optimization

   Oluwatayomi Rereloluwa Adegboye, Afi Kekeli Feda, Ghanshyam G. Tejani +3

   СтатьяMachine Learning and ELMScientific Reports2025Цитирований: 0

   ABIABI:AkademIndex/openalex/2025.article.000504

5. Enhanced global optimization using quadratically interpolated hybrid pathfinder algorithm

   Oluwatayomi Rereloluwa Adegboye, Afi Kekeli Feda, Abosede Omowumi Tibetan +1

   СтатьяMetaheuristic Optimization Algorithms ResearchCluster Computing2025Цитирований: 0

   ABIABI:AkademIndex/openalex/2025.article.000597