Enhancing the Puma Optimizer Algorithm for Optimization Problems

This paper proposes an enhanced Puma Optimizer Algorithm for solving optimization problems. The Rao Algorithm is incorporated and refined within the exploration phase of the Puma Optimizer, incorporating novel procedures to improve position updates. The proposed algorithm is evaluated on six benchma...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (Online) s. 583 - 587
Hlavní autoři: Pravesjit, Sakkayaphop, Kantawong, Krittika
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 29.01.2025
Témata:
Benchmark testing
Optimization
optimization functions
Particle swarm optimization
puma optimizer algorithm
RAO algorithm
Whale optimization algorithms
ISSN:2768-4644
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This paper proposes an enhanced Puma Optimizer Algorithm for solving optimization problems. The Rao Algorithm is incorporated and refined within the exploration phase of the Puma Optimizer, incorporating novel procedures to improve position updates. The proposed algorithm is evaluated on six benchmark functions. An evaluation results show in comparison to Differential Evolution (DE), Intersection Mutation Differential Evolution (IMDE), the Whale Optimization Algorithm (WOA), and the Sand Cat Swarm Optimization Algorithm (SCSO). Computational results demonstrate that the proposed algorithm achieves optimal solutions for two of the six benchmark functions. Furthermore, it consistently outperforms the other four algorithms across the test suite. These findings highlight the potential for further advancements in the Puma Optimizer and demonstrate its effectiveness in addressing continuous step functions, multimodal functions, and discontinuous step functions, achieving performance that is competitive with or surpasses existing state-of-the-art methods.
ISSN:2768-4644
DOI:10.1109/ECTIDAMTNCON64748.2025.10962095