So as to mimic the hunting behavior of the bats, a technique of the bat algorithm is implemented with the following assumptions:
1 Bats utilize the technique of echolocation to detect the distance and they can also identify the difference between the target and the walls.
2 Bat can fly accidentally along with the velocity and position for a static frequency that may vary in wavelength and loudness for searching the target. They can modify the wavelength automatically with respect to their pulse depending on the target.
3 Bat’s loudness can vary in more number of ways ranging from large positive to minimum value.
Based on three assumptions, the algorithm produces a group of solutions randomly for the problem and afterward looks through the ideal solution by cycle and make stronger the nearby analysis during the time spent of searching. By providing the optimal solution randomly, bat algorithm discovers the global optimal solution to their problem. Some of the applications of bat algorithm are image processing, clustering, classification, data mining, continuous optimization, problem inverse and estimation of parameter, combination scheduling and optimization, and fuzzy logic.
1.5.1.4.14 Binary Bat Algorithm
Binary bat algorithm (BBA) is an approach utilized for solving discrete problems which was introduced by Nakamura. BBA is implemented in the problem of classification and selection of feature. It is a binary version of bat algorithm with the modification of velocity and position. In other version like continuous of bat algorithm, bat travels through the search place of target with the help of velocity and position parameters. In position, it shifts between 0’s and 1’s which act as the binary space to reach the target.
1.5.1.4.15 Cuttlefish Algorithm
The cuttlefish algorithm (CFA) is inspired by the color changing behavior of cuttlefish to identify the optimal solution of the problem. The set of patterns and hues found in cuttlefish are created by reflection of light from various types cells layer like chromatophores, iridophores, and leucophores which are stacked together, and it is a combination of specific cells on the double that permits cuttlefish to have such a huge selection of pattern and hues.
Cuttlefish is a sort of cephalopods which is distinguished for its capacities to change its shading either to apparently vanish into its condition or to deliver magnificent presentations. The pattern and hues found in cephalopods are created by various types and cell layers are stacked together including chromatophores, iridophores, and leucophores.
Cuttlefish algorithm thinks about two major measures, namely, reflection and perceptibility. Reflection process is referred to reproduce the light reflection system utilized by these three layers where the perceptibility is referred to putting on the perceptibility of coordinating example utilized by the cuttlefish. These two procedures are utilized for technique like searching to locate the optimal solution of the problem.
1.5.1.4.16 Grey Wolf Optimizer
Grey Wolf Optimizer (GWO) was introduced by Mirjalili, which is one of the mimicking of the management quality with leadership and hare coursing mechanism of grey wolves. Alpha, Beta, Delta, and Omega are the four types of grey wolves, which are used for mimicking the management quality with leadership. The technique of Grey Wolf like penetrating, surrounding, and attacking the target is used for mimicking the hare coursing for the implementation of optimization technique.
Grey Wolf has a place in a biological family named Canidae, which live in a pack of wolf. They have a severe social predominant chain of importance like Delta, Omega, Beta, and Alpha. Alpha that is referred to pioneer is a male or female which places a major role in making decision. The sets of the predominant wolf ought to be trailed by the pack. The Beta is referred as minor wolves that have ability to help the alpha in making decision. The beta is a guide to alpha for making decision and discipliner for the pack of wolf. Omega is referred as the lower positioning of grey wolf which needs to present all other predominant wolves. In the event that a wolf is neither an alpha or beta nor omega, it is called delta. Delta is referred as wolves that lead omega and report to alpha and beta.
The hare coursing strategies and the social progression of wolves are numerically displayed so as to create GWO and perform technique of optimization. The algorithm of GWO is established with the typical test mechanism that shows it has predominant investigation and utilization qualities than other techniques like swarm intelligent. When a wolf is not said to be alpha, beta, or omega, then it is called as minor or delta in certain cases. The categories of GWO are scouts, hunters, elders, caretakers, and sentinels, which have a place with this class. Scout wolves are referred as answerable for inspecting the limits of the section and threatening the pack if there should arise an occurrence of any threat. Hunter wolves are referred to as which help the alphas and betas when chasing prey and giving nourishment to the pack. Elder wolves are referred to as the proficient wolves that used to be alpha or beta. The caretaker wolves are referred to as answerable for thinking about the frail, sick, and injured scoundrels. Sentinel wolves are referred to as secure and ensure the protection of the pack.
Group coursing is also the mimicking behavior in count to the social behavior of grey wolves. Notwithstanding the social pecking order of wolves, bunch chasing is another fascinating social conduct of dim wolves. The algorithm of GWO is a moderately innovative populace-based technique of optimization that has the benefit of minimum parameter control, ability of robust optimization, and simple execution.
1.5.1.4.17 Elephant Herding Optimization
Elephant Herding Optimization (EHO) algorithm is one of the metaheuristic approach swarm-based search algorithms that is utilized to explain various problems of optimization and also utilized benchmark, localization based on energy, services selection in QOS web service compositions, appliance scheduling in smart grid identification, and PID controller tuning– based problems. The algorithm is inspired by the performance of group of elephant in the wild, in which elephants live in a group with a female elephant called leader matriarch, while the male are disconnected from the group when they are adulthood. The EHO algorithm is based on the models of collecting behaviors of elephants in two procedures. They are clan update and separation. Clan update is referred as updating the elephants and matriarch present location in every clan and separation is referred as enhancing the populace range in the subsequent phase of search.
Table 1.1 List of applications of various algorithms.
| S. no. | Algorithm | Areas of application |
|---|---|---|
| 1. | Memetic algorithm | Multi-dimensional knapsack problem, pattern recognition, feature/gene selection, training of artificial neural networks, clustering of gene expression profiles, traveling salesman problem, Robotic motion planning |
| 2. | Genetic algorithm | Allocation of document for a distributed system, PC robotized plan, server farm/server center, code breaking, criminological science, robot behavior, PC design, Bayesian inference, AI, game hypothesis |
| 3. | Ant colony optimization algorithm | Problems of generalized assignment and the set covering, classification problems, Ant Net for organized directing and multiple knapsack Problem |
