  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.
