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.
翻译:我们提议菲律宾鹰优化算法(PEOA),这是一个由菲律宾鹰的地域狩猎行为所启发的超湿度和基于人口的搜索算法(PEOA),这是一种由菲律宾鹰的地域狩猎行为所启发的元值和基于人口的搜索算法。从最初随机的在特定搜索空间的鹰群中,选择了最好的鹰群,并以内点方法作为剥削手段进行当地食品搜索。然后将人口分为三个亚群群,每个亚群群群被指派为协助勘探的操作员。一旦应用了各自的操作员,功能改进后的功能值新鹰就取代了较老的。随后,对人口的最佳鹰群进行了更新,并再次进行了当地食品搜索。这些步骤是同步进行的,最后最佳鹰群群搜索是搜索空间的最佳解决办法。PEOA测试了20种最优化测试功能,以不同的方式、可分离性和尺寸特性进行。PEOA的性能与11种其他优化算法相比,为了进一步验证PEOA的效能,它还用于在中性延迟延迟方程式和参数识别模型中性延迟等方程式模型模型中进行图像识别。AIA 最精确的计算结果,也证明PIA可以使其他的精确地算法进行其他的精度分析。