Northern Bald Ibis Optimization Algorithm: Theory and Application

In this work, an in-distinguish migratory bird swarm based algorithm is presented and the same applied to the challenging optimal data clustering problems. Many algorithms that are inspired by the nature are giving super-efficient solutions. The migratory behavior of Northern Bald Ibises (Geronticus eremita) for food paves the path to devise a new optimization algorithm termed as NOA. To examine the performance of NOA algorithm, benchmarking is done in two different phases. In the first phase, 23 standard mathematical testing functions are employed to examine the optimization characteristics of NOA. Secondly, solved 10 well-known data cluster problems to test the numerical efficiency of NOA. The obtained experimental results and statistical analysis of these two phases are portrayed in graphical and tabular form. The comparisons have been made with other futuristic algorithms and it proves that the devised NOA optimization algorithm is good at benchmark function optimization problems as well as optimal data clustering problems.