The use of artificial intelligence (AI) in various domains has drastically increased during the last decade. Nature-inspired computing is a strong computing approach that belongs to AI and covers a wide range of techniques. It has successfully tackled many complex problems and outperformed several classical techniques. This chapter provides the original ideas behind some nature-inspired computing techniques and their applications, such as the genetic algorithms, particle swarm optimization, grey wolf optimizer, ant colony optimization, plant propagation algorithm, cuckoo optimization algorithm, and artificial neural networks.
Top2. Principles Of Some Nature-Inspired Algorithms
2.1. Genetic Algorithms
The genetic algorithms (GAs) are one of the most popular, oldest, and pioneer nature-inspired optimization algorithms. John Henry Holland developed the GAs in 1975 (Holland, 1975) based on genetic operators: chromosomes, parents, children, population, selection, crossover, and mutation. Many types of each operator can be used to solve a particular problem. The detailed principles can be found in Refs. (Holland, 1975; Sivanandam & Deepa, 2008; Sumathi & Paneerselvam, 2010).
2.2. Ant colony Optimization
The ant colony optimization (ACO) was developed by Marco Dorigo in 1992 (M. Dorigo, 1992). It is based on the ants’ behavior for foraging. The ants left a pheromone on the ground and based on its intensity and evaporation process, the optimal path will be identified. It allows finding the shortest path. The detailed principles can be found in Refs. (M. Dorigo, 1992; Marco Dorigo, Maniezzo, & Colorni, 1996; M. A. Mellal & Williams, 2018).
2.3. Particle Swarm Optimization
The particle swarm optimization (PSO) is inspired by the principles of the moving style of some species, such as birds and fishes. It was developed by Kennedy and Eberhart in 1995 (Kennedy & Eberhart, 1995). Each element of the swarm, called a particle, has a position and a velocity. The particles move randomly and the best positions are computed and updated. Comprehensive details about the PSO can be found in Refs. (Clerc, 2006; Kennedy & Eberhart, 1995; M. A. Mellal & Williams, 2018).
2.4. Grey Wolf Optimizer
The grey wolf optimizer (GWO) was developed by Mirjalili et al. in 2014 (Mirjalili, Mirjalili, & Lewis, 2014) and is inspired by the grey wolves. This algorithm is reputed to be efficient and fast. In the GWO, the wolves are divided into four hierarchies, called alpha, beta, delta, and omega. These wolves follow some rules to hunt. Details can be found in Refs. (Mohamed Arezki Mellal, Frik, & Boutiche, 2021; Mirjalili et al., 2014; Panda & Das, 2019).