Is this project an undergraduate, graduate, or faculty project?
Graduate
individual
What campus are you from?
Daytona Beach
Authors' Class Standing
Jessica Christa Wira Hadipoernomo, Graduate Student
Lead Presenter's Name
Jessica Christa Wira Hadipoernomo
Faculty Mentor Name
Dr. Bryan Watson
Abstract
Optimization plays a crucial role in refining complex system designs, improving performance, and maximizing efficiency across various applications. Traditional methods like convex optimization and the Newton-Raphson method are often insufficient for today’s increasingly complex challenges. To address this challenge, numerous metaheuristic algorithms, often inspired by nature, have emerged including Particle Swarm Optimization, Genetic Algorithm, Bat Algorithm, and Firefly Algorithm. However, most of these existing algorithms have global knowledge, which is unrealistic for some real-world complex systems such as underground mining and spacecraft trajectory. To bridge this gap, this article develops and presents a new biologically inspired optimization algorithm, the Sandpiper Food Search Algorithm. This algorithm is inspired by the food search behavior of sandpipers at the beach. Each sandpiper explores the problem space locally, with wave actions that force shifts, enhancing exploration. Our previous work shows that the Sandpiper Food Search Algorithm outperforms the Firefly Algorithm in three of the four benchmark functions with at least 3% improvement in mean best solution and is on average 38% more reliable at finding a solution at least 95% of the optimal. In this work, we tested SFSA on a real-world data-based function in comparison to Firefly Algorithm and Particle Swarm Optimization. Our results show that our algorithm does not perform better than the other two algorithms tested, prompting us extend our study to include more extensive parameter tuning to ensure fair comparison.
Did this research project receive funding support from the Office of Undergraduate Research.
Yes, Spark Grant
Sandpiper Food Search Algorithm: A New Optimization Approach for Agents with Limited Knowledge
Optimization plays a crucial role in refining complex system designs, improving performance, and maximizing efficiency across various applications. Traditional methods like convex optimization and the Newton-Raphson method are often insufficient for today’s increasingly complex challenges. To address this challenge, numerous metaheuristic algorithms, often inspired by nature, have emerged including Particle Swarm Optimization, Genetic Algorithm, Bat Algorithm, and Firefly Algorithm. However, most of these existing algorithms have global knowledge, which is unrealistic for some real-world complex systems such as underground mining and spacecraft trajectory. To bridge this gap, this article develops and presents a new biologically inspired optimization algorithm, the Sandpiper Food Search Algorithm. This algorithm is inspired by the food search behavior of sandpipers at the beach. Each sandpiper explores the problem space locally, with wave actions that force shifts, enhancing exploration. Our previous work shows that the Sandpiper Food Search Algorithm outperforms the Firefly Algorithm in three of the four benchmark functions with at least 3% improvement in mean best solution and is on average 38% more reliable at finding a solution at least 95% of the optimal. In this work, we tested SFSA on a real-world data-based function in comparison to Firefly Algorithm and Particle Swarm Optimization. Our results show that our algorithm does not perform better than the other two algorithms tested, prompting us extend our study to include more extensive parameter tuning to ensure fair comparison.