Philippine Eagle Optimization Algorithm

TL;DR

The Philippine Eagle Optimization Algorithm (PEOA) is a new meta-heuristic inspired by eagle hunting behavior, demonstrating high accuracy and low computational cost across various optimization problems and real-world applications.

Contribution

This paper introduces PEOA, a novel population-based meta-heuristic inspired by Philippine Eagle hunting, combining exploration and exploitation strategies for effective optimization.

Findings

PEOA outperforms 11 existing algorithms on 20 benchmark functions.

PEOA achieves accurate solutions in image reconstruction and parameter identification.

PEOA is the most computationally efficient among tested algorithms.

Abstract

We propose the Philippine Eagle Optimization Algorithm (PEOA), which is a meta-heuristic and population-based search algorithm inspired by the territorial hunting behavior of the Philippine Eagle. From an initial random population of eagles in a given search space, the best eagle is selected and undergoes a local food search using the interior point method as its means of exploitation. The population is then divided into three subpopulations, and each subpopulation is assigned an operator which aids in the exploration. Once the respective operators are applied, the new eagles with improved function values replace the older ones. The best eagle of the population is then updated and conducts a local food search again. These steps are done iteratively, and the food searched by the final best eagle is the optimal solution of the search space. PEOA is tested on 20 optimization test functions with different modality, separability, and dimension properties. The performance of PEOA is compared to 11 other optimization algorithms. To further validate the effectiveness of PEOA, it is also applied to image reconstruction in electrical impedance tomography and parameter identification in a neutral delay differential equation model. Numerical results show that PEOA can obtain accurate solutions to various functions and problems. PEOA proves to be the most computationally inexpensive algorithm relative to the others examined, while also helping promote the critically endangered Philippine Eagle.