Performance Analysis of Relatively New Nature Inspired Computing Algorithms

Optimization algorithms have been developed for optimization problems that can be found in all areas of life. They have been influenced by various fields of science such as mathematics, biology, and chemistry. Nature Inspired Computing (NIC) algorithms that are inspired by the intuitive behavior of nature and creatures recently emerge among the optimization algorithms. It is the aim of this study is to compare the performances of three relatively new NIC algorithms that are called as Flower Pollination Algorithm (FPA), Forest Optimization Algorithm (FOA) and Artificial Algae Algorithm (AAA). The comparisons are carried out on ten of the well-known benchmark test functions that are divided into two groups as multi-modal and uni-modal. The comparison results revealed that the optimization performance of Flower Pollination Algorithm is superior to the other algorithms.