Performance analysis of algorithms based on intelligence of plants

Artificial intelligence (AI) is a branch of computer science that studies the intelligent behavior of living beings, and mimics this intelligence by deploying it in computer programs, machines and systems in order to solve problems related to searching, optimization, planning, control, automation, etc. One of the areas of artificial intelligence is evolutionary computation, which is inspired by the principle of natural evolution of species. Within the evolutionary computation several methods based on the intelligence of plants have been recently proposed. How the plants survive and adapt in harsh environments has aroused interest of researchers in AI. It is remarkable that the life cycle of a plant is extremely intriguing. The way the plants reproduce, propagate, disperse their seeds and select the most resistant is undoubtedly an evidence of intelligence of plants when optimize their existence. In this sense, some computer algorithms have been proposed based on the intelligent lifecycle of plants. These algorithms are in many cases, simple to implement, and efficient in solving complex problems. In this work, the performance of three algorithms, the flower pollination algorithm, strawberry plant algorithm and invasive weed optimization, all of them based on the intelligent behavior of plants, are analyzed when applied to optimization of test functions, and they are also compared with classical genetic algorithms.