@inproceedings{6251ad4f56064508aaafb4d78a878b67,
title = "Federated Heuristic Optimization Based on Fuzzy Clustering and Red Fox Optimization Algorithm",
abstract = "Heuristic algorithms are dependent on many coefficients like the number of iterations or individuals. However, quite often these algorithms move individuals toward the best in the population. Based on this observation, we propose the idea of federated heuristics. The proposed idea is to initially distribute individuals into certain intervals. Then, it performs a specified number of iterations of the algorithm to identify the potentially best intervals. Sorted intervals (in relation to the best-adapted individual) make it possible to separate the appropriate size of the population in each of them. Moreover, these clusters are merged by a fuzzy algorithm due to a decrease in their numbers. The more significant the interval, the greater the number of individuals and iterations allocated in these areas. As a consequence, several instances of the selected heuristic algorithm are triggered, which can divide the best individual. The proposed technique was described using the red fox algorithm and tested at a classic set of functions with different parameters of the used heuristic.",
keywords = "fuzzy, heuristic, image processing, neural network",
author = "Dawid Polap and Katarzyna Prokop and Gautam Srivastava",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2023 ; Conference date: 13-08-2023 Through 17-08-2023",
year = "2023",
doi = "10.1109/FUZZ52849.2023.10309747",
language = "English",
series = "IEEE International Conference on Fuzzy Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2023",
address = "United States",
}