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For many years, the bee search methods were researched exclusively by biologists. However, the interest in applying swarm behavior in the development of new optimization algorithms was growing. In 2005, professor Dervis Karaboga became interested in the research results. He published a scientific article and was the first to describe the model of swarm intelligence mostly inspired by bee dance. The model was called the artificial bee colony.

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The gray wolf algorithm is a metaheuristic stochastic swarm intelligence algorithm developed in 2014. Its idea is based on the gray wolf pack hunting model. There are four types of wolves: alpha, beta, delta and omega. Alpha has the most "weight" in decision making and managing the pack. Next come the beta and the delta, which obey the alpha and have power over the rest of the wolves. The omega wolf always obeys the rest of the dominant wolves.

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The cuckoo is a fascinating bird, not only because of its singing, but also because of its aggressive breeding strategy, which consists in laying eggs into nests of other birds.
Cuckoo search is one of the latest nature-inspired heuristic algorithms developed by Yang and Deb in 2009. It is based on the parasitism of some cuckoo species. This algorithm has been further improved by so-called Levy flights rather than simple isotropic random walk methods.

The Cuckoo Optimization Algorithm (COA) is used for continuous non-linear optimization. COA is inspired by the lifestyle of this bird. The optimization algorithm is based on the features of the species' egg-laying and reproduction. Like other evolutionary approaches, COA starts with an initial population. The basis of the algorithm is an attempt to survive. While competing for survival, some of the birds die. Surviving cuckoos move to better places and begin to breed and lay eggs. Finally, the surviving cuckoos converge in such a way that a cuckoo society with similar fitness values is obtained.
The main advantage of this method is its simplicity: cuckoo search requires only four understandable parameters, so tuning becomes a no-brainer.

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Nature has always been an inspiration for many metaheuristic algorithms. It managed to find solutions to problems without prompting, based on individual experience. Natural selection and survival of the fittest were the main motivation for the creation of early metaheuristic algorithms. In nature, animals communicate with each other in many ways. Fireflies use their ability to blink to communicate. There are about 2000 species of fireflies with their own special flash patterns.

There are two variants of population optimization algorithms inspired by the behavior of fireflies: the Firefly Algorithm and the Glowworm Swarm Optimization (GSO) algorithm. The main difference between firefly and glowworms is that the latter are wingless. In this article, we will consider the first type of the optimization algorithm.

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