Nature-Inspired Algorithms and Applications. Группа авторов

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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

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      1 *Corresponding author: [email protected]

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