Bat optimization algorithm is inspired by biology and is a heuristic method for solving difficult optimization problems. It represents an attempt to simulate the behavior of bats to hunt prey and the algorithm was presented by the research Yang in Bat Algorithm is based on the bat community, which is flying through the search solutions space in a specific or special order to find the best areas. Each bat element represents one solution in dimensions of the search space. The solutions are evaluated based on the appropriate value and using the given fitness function . For example, the dimensions are a real value for the optimization solution space. Each solution represents a bat, and evaluation is done using the given fitness function. There are also two real values of the dimension of vectors associated with every bat in society . The first vector is the value of the real value and represents the location of the bat in the solution to the search space. The second vector is the real value vector and represents the velocity in all directions of dimensions . The location and velocity of the vectors randomly configured at the beginning of the algorithm.
On each occurrence the appropriate value of each element in the bat group is calculated by the given fitness function. The new velocity vector is calculated on the basis of the relative distance from the best and current solution in society. Later, the position of all the bats is updated according to the speed vector. At the end of each iteration, the best solution is created and used as a new reference point, and the search space continues to be explored until the stopping condition is met. Usually, this condition is the maximum number of iterations or improvements in the best solution . One of the creations of animal life studied by many zoologists is the echo-locating of bats . There are a few other animal groups that also have echo-positioning capabilities such as birds , whales, outdoor cannabis grow dolphins, and small insects, but this is very rare. This behavior of bats began by Lazzaro Spallanzani in . Then the term “echolocation” was introduced by Donald Griffin in 1944 with the ability of bats to produce sound with echoes beyond the frequency of human hearing and using it for general guidance in the dark and finding prey at night with echo location, bats emit ultrasound pulses with either a modified frequency or fixed frequency and sometimes a mixture of the two Tonal signals produced in the larynx are emitted in short bursts through the mouth or gills .
Suga in described that the reflected sounds were in a state of Doppler pressure or transformation which made the received resonance at a higher frequency than previously produced sound. Bats can determine the object and distance by measuring the modified echo reflection time When bats begin to search for prey in the research phase, they emit pulses at a low rate around the 10 Hz frequency . During the approach phase, where bats discover and approach prey, the pulses must be shorter to prevent interference . The shorter pulses cause a decrease in time between the pulse and echo. Also at this moment, the pulse emission rate gradually increases to 200 beats per second as bats continue to update the prey site . Suga states that the pulse emission rate increases because bats need to produce more signals to accurately follow prey as the angular position of prey changes more quickly due to the closer distance between the bat and the object. In the last stage , the frequency of the emitted pulses increases by more than 200 Hz and the pulse emission rate becomes faster in only a small fraction of a millisecond long before the prey is caught .
A colony of bats has two exclusive approaches to avoid colliding with one another while echo locating . This behavior mostly applies to vampire bat types such as regurgitation from blood meals from successful bats to feed them to their useless member in the colony as a response to the meticulously balanced energy budget of each member of the colony . Wilkiuson discovered that surviving altruistic behavior grows in survivors so that his fitness is relatively high to non-recipient, and mutual altruism also occurs during community nursing . Several researchers built algorithms by hybridizing common and difficult problems with the NP-Hard. Researchers have therefore worked in recent years to hybridize algorithms with normal and difficult problems and have achieved the best results by applying hybrid problems. The real purpose of the hybridization process is to obtain general and varied solutions that can deal with problems in the real world.