Artificial Human Optimization

Abstract

The term "Artificial Human Optimization" was first created by the corresponding author of this paper in December 2016, when he published a paper titled "Entrepreneur : Artificial Human Optimization" in Transactions on Machine Learning and Artificial Intelligence (TMLAI) Volume 4, No 6 in December 2016. (December 2016). The Artificial Human Optimization Field is described as the collection of all optimization methods suggested based on Artificial Humans, according to a paper released in 2016. In the real world, we (Humans) are responsible for resolving issues. Artificial Humans imitate real Humans in the search space and solve optimization issues in the same way. The basic entities in the solution space of Particle Swarm Optimization (PSO) are Artificial Birds, whereas the basic entities in the search space of Artificial Human Optimization are Artificial Humans. Each Artificial Human is associated with a certain location in the solution space. The following are ten strategies for Artificial Human Optimization :“Human Bhagavad Gita Particle Swarm Optimization (HBGPSO)”, “Human Poverty Particle Swarm Optimization (HPPSO)”, “Human Dedication Particle Swarm Optimization (HuDePSO)”, “Human Selection Particle Swarm Optimization (HuSePSO)”, “Human Safety Particle Swarm Optimization (HuSaPSO)”, “Human Kindness Particle Swarm Optimization (HKPSO)”, “Human Relaxation Particle Swarm Optimization (HRPSO)”, “Multiple Strategy Human Particle Swarm Optimization (MSHPSO)”, “Human Thinking Particle Swarm Optimization (HTPSO)”, “Human Disease Particle Swarm Optimization (HDPSO)”. The results of applying ten Artificial Human Optimization methods to various benchmark functions are presented in this paper.