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a)      Cuckoo Search: Cuckoo search is inspired by the bird cuckoo called “Brood
parasites” birds who never build their own nest and lays their eggs in the nest
of another host bird. If the host bird finds that the eggs in its nest are not
their own eggs then it will either throw those eggs away or abandon its nest
and build a new nest. In a nest, each egg represents a solution and cuckoo egg
represents a new and good solution. The obtained solution is a new solution
based on the existing one and the modification of some characteristics. 8

b)      Ant Colony: Ants leave a pheromone trail from the source of food to
the nest and vice versa. This pheromone has shorter evaporation time; hence the
shortest path is followed by more ants and has stronger pheromone

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c)      Firefly Optimization: Firefly optimization technique is based on natural
behavior of fireflies. Fireflies use flashes for communication, to attract
prey, and as protective warning mechanism. The brighter the flash, the more the
fireflies with lesser flashes are attracted towards them. 9

d)     Particle Swarm
Optimization (PSO): PSO is based on natural behavior of
bird flocks and fish school. It optimizes a problem by having a population of
candidate solutions (particles) and moving these particles around in the search
space according to simple mathematical formulae over the particle’s position
and velocity. Each particle’s movement is influenced by its local best known
position and is guided towards the best known positions in the search-space,
which are updated as better positions are found by other particles. This is
expected to move the swarm towards the best solutions.

e)      Grey Wolf: The GWO algorithm is based on the wild behavior of the
grey wolves during hunting. The GWO divides the animal population into four
categories: alpha, beta, delta, and omega. The optimization process is same as
hunting, guided by the highest rank leaders: alpha, beta and delta which
represent the best three solutions in the search space. The omega wolves rank lowest
in the hierarchy and represent the rest of the solutions that must adjust their
positions to follow the other dominant wolves.

Bee Colony optimization:
In Bee colony optimization the search space is explored by every artificial bee
which apply a predefined number of moves, thereby constructing and/or improving
the solution, yielding a new solution. Then all the bees move back to the hive
and share information about the quality of their solutions i.e. the value of
the objective functions is computed. Then every bee decides randomly whether to
continue its own exploration and become a recruiter or a follower.  This process is repeated if solutions are

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