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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
4.	Particle swarm optimization algorithm	Combination with a back engendering calculation, to prepare a neural system framework structure, multi-target optimization, classification, image clustering and image clustering, image processing, automated applications, dynamic, pattern recognition, image segmentation, robotic applications, time frequency analysis, decision-making, simulation, and identification
5.	Harmony search algorithm	Power systems, power systems, transportation, medical science and robotics, industry and signal and image processing
6.	Artificial bee colony algorithm	Problem of medical pattern classification, network reconfiguration, minimum spanning tree, train neural networks, radial distribution system of network reconfiguration, and train neural networks
7.	Firefly algorithm	Semantic web composition, classification and clustering problems, neural network, fault detection, digital image compression, feature selection, digital image processing, scheduling problems, and traveling salesman problem
8.	Bat algorithm	Image processing, clustering, classification, data mining, continuous optimization, problem inverse and estimation of parameter, combination scheduling and optimization, and fuzzy logic

      The working of EHO is based on that every elephant in clan is updated by utilizing group data through clan by the procedure of updating, and afterward, the poorest elephant is supplanted by randomly produced elephant individual through the procedure of updating. EHO can discover much improved solutions on more problems of benchmark. Problems of benchmark are a lot of different types of problem of optimization that comprises of different kinds of aptitudes that utilized in testing and the estimation is verified and described. Then, the execution of estimation enhances the algorithm under various ecological conditions.

Table 1.1 lists the various applications of NIC algorithms.

References

      1. Siddique, N. and Adeli, H., Nature-Inspired Computing: An Overview and Some Future Directions. Cognit. Comput., 7, 706–714, 2015.

2. Wang, L., Kang, Q., Wu, Q.-d., Nature-inspired Computation — Effective Realization of Artificial Intelligence. Syst. Eng. - Theory Pract., 27, 126–134, 2007, 10.1016/S1874-8651(08)60034-4.

      3. Fan, X., Sayers, W., Zhang, S. et al., Review and Classification of Bio-inspired Algorithms and Their Applications. J. Bionic Eng., 17, 611–631, 2020, https://doi.org/10.1007/s42235-020-0049-9.

      4. Nguyen, B.H., Xue, B., Zhang, M., A survey on swarm intelligence approaches to feature selection in data mining. Swarm Evol. Comput., 54, 100663, 2020.

      5. Neri, F. and Cotta, C., Memetic algorithms and memetic computing optimization: A literature review. Swarm Evol. Comput., 2, 1–14, 2012, 10.1016/j. swevo.2011.11.003.

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