Red fox optimization algorithm

By Dawid Połap, Marcin Wozniak. Expert systems with applications, 2021

412 citations recorded by the source corpus.

Abstract Fox is very popular in various regions of the Globe, where representatives of this kind can be found in Europe, Asia, North America, and even in some arctic regions. The way this predator lives and hunts is very peculiar. It is active all year round, traversing the lands in hunting both for different domestic and wild animals. In his strategy fox is using various tricks to distract prey while creeping what makes him a very efficient predator. The territorial habits and family relations between young and adult made the fox easily adaptable to various conditions and therefore helped him to survive in a changing environment. In this article we propose a mathematical model of red fox habits, searching for food, hunting, and developing population while escaping from hunters. Described model is based on local and global optimization method with a reproduction mechanism. The novel model developed for optimization purposes we name the Red Fox Optimization Algorithm (RFO). The proposed method was subjected to benchmark tests using 22 test functions and 7 classic engineering optimization problems. Experimental results are compared to other meta-heuristic algorithms to show potential advantages.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/4b66dc5ce166ac35839c2385ec4cfb54e61bc7f1

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Citations in the corpus

Prairie Dog Optimization Algorithm (2022, methodology): “The most popular algo-rithm in this category is particle swarm optimization (PSO); others include the red fox optimization algorithm [35], salp swarm algorithm [36], ant colony optimization [13], and artiﬁcial bee colony [14].”
Spider wasp optimizer: a novel meta-heuristic optimization algorithm (2023, methodology): “2021), red fox optimization algorithm (RFO) (Połap and Woźniak 2021), ant colony optimization (Dorigo et al.”