What is Nature Inspired Artificial Intelligence? Many intelligence #algorithms mimic natural phenomena such as, How animals organize their lives; How they use instincts to survive; How generations evolve; How the human brain works; and How humans learn; Such nature-inspired optimization algorithms (NIOAs) are defined as a group of algorithms that are inspired by natural phenomena, including swarm intelligence, biological systems, physical and chemical systems, etc. NIOAs include bio-inspired algorithms and physics- and chemistry-based algorithms; the bio-inspired algorithms further include swarm intelligence-based and evolutionary algorithms. NIOAs are an important branch of #artificialintelligence and NIOAs have made significant progress in the last 30 years. Thus far, a large number of common NIOAs and their variants have been proposed, such as, Genetic algorithm (GA); Particle swarm optimization (PSO) algorithm; Differential evolution (DE) algorithm; Artificial bee colony (ABC) algorithm; Ant colony optimization (ACO) algorithm; Cuckoo search (CS) algorithm; Bat algorithm (BA); Firefly algorithm (FA); Immune algorithm (IA); Grey wolf optimization (GWO); Gravitational search algorithm (GSA); Harmony search (HS) algorithm; Besides, Researchers have designed many AI algorithms by imitating human intelligence with machines, going as artificial human intelligence or human AI. A model is a simplifying image of reality or a simplified abstract view of a complex reality, as with a scientific model. The idea is to apply the components of mathematical model, variables or decision parameters; constants and calibration parameters; input parameters, data; phase parameters; output parameters; noise and random parameters.