Nature-Inspired Algorithm

Do you ever stick with complex problems? Are you Tired of searching for solutions? Just look around there is always a better solution you can find from nature. Nature is a god's gift where there are secrets hidden.

The nature-inspired algorithm defines the methodologies of solving a complex problem that is derived from natural phenomena. Some of the popular examples of nature-inspired optimization algorithms are:

• Cuckoo Search Algorithm
• Ant colony optimization
• Bat Algorithm
• Particle Swarm Optimization
• Genetic Algorithm

Cuckoo Search Algorithm ( CSA)

Cuckoo Search Algorithm developed in 2009 by Xin-She Yang and Suash Deb. It is based on the brood parasitism of some cuckoo species. Cuckoos are a family of birds with a unique reproductive strategy more aggressive compared to other bird species. Some cuckoo bird species like Ani and Guira lay eggs in communal nests and they may remove other eggs to increase the hatching probability of their own eggs. Other species use the brood parasitism method of laying their eggs in the nests of other birds or host nests.

The CS optimization algorithm is basically based on the following three rules:

• Each cuckoo selects a nest randomly and lays one egg in it.
• The best nests with high-quality eggs will be carried over to the next generation.
• For a fixed number of nests, a host cuckoo can discover a foreign egg with a probability є [0,1]. In this case, the host cuckoo can either throw the egg away or abandon the nest and build a new one somewhere else.

Ant Colony Optimization (ACO)

Ant Colony Optimization (ACO) is developed by Marco Dorigo in 1991 based on the behavior of biological ants. The insect species of ants prefer community survival rather than individual species. They communicate with each other using sound, touch, and pheromone.

Pheromones are organic chemical compounds secreted by ants that trigger a social response in members of the same species. It acts like a hormone outside the body of the secreting individual it helps to communicate with individuals. The ants who live on the ground, use the soil surface to leave pheromone trails that may be followed by other ants in the same colony.

Ants in the colony discover the shortest path to their food by secreting pheromone. They started to loiter randomly by searching their food and they observe the shortest path from their nest to their food. After finding the food by knowing the quality and quantity it secrets pheromone on the way to return nest.

As more ants travel through the same path, it gets stronger until there are a couple of streams of ants traveling to various food sources near the colony. Once the food source is depleted, the route is no longer populated with pheromones and slowly decays.

As it is the ant colony works on a very dynamic system, the ant colony algorithm works very well in graphs with changing topologies. Examples of such systems include computer networks and artificial intelligence simulations of workers.

Bat Algorithm (BA)

The bat algorithm (BA) is based on the echolocation behavior of bats. It was proposed by Yang in 2010. The BA has been widely applied in many applications, such as engineering optimization and pattern recognition.

Normally when a bat started to fly it makes a high frequency (12 kHz to 160 kHz) sound that a human cannot hear because the human frequency is(20 Hz to 20 kHz). Echolocation works as a type of sonar. When the sound hits an object, after a fraction of time, the echo will return back to their ears. The bat receives and detects the location of the prey in this way. It helps bats to differentiate an obstacle and prey and allows them to hunt even in darkness.

There are research areas for BA applications in signal, speech, and image processing: Speech Enhancement, Adaptive Filtering, Image Compression, Enhancement, and thresholding in Segmentation.