Worker Ant Optimization: An Algorithm for Complex Problems
Year: 2024
Type: article
Abstract: — The article introduces a novel metaheuristic algorithm called the Worker Ant Optimization (WAO) algorithm. This algorithm is mathematically modeled based on five natural behaviors of worker ants: avoiding danger, foraging, approaching food, decomposing food, and transporting food. The performance of WAO was evaluated using 23 classical test functions and compared with results from seven well-known metaheuristic algorithms. Simulation results demonstrate that the WAO algorithm exhibits significant advantages in terms of convergence speed, avoidance of local optima, and optimization accuracy. To assess the effectiveness of WAO in practical applications, the method was applied to three classical engineering design problems, validating the engineering applicability of the WAO optimization algorithm. WAO effectively explores the decision space and performs well across various evaluation metrics, demonstrating its capability to effectively address challenges in practical applications.
Author: Tian Naiyue
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6949144592
FWCI:
Cites:
Cited by:
Related to: