An improved FOX optimization algorithm using adaptive exploration and exploitation for global optimization.

ABSTRACT: Optimization algorithms are essential for solving many real-world problems. However, challenges such as getting trapped in local minima and effectively balancing exploration and exploitation often limit their performance. This paper introduces an improved variation of the FOX optimization algorithm (FOX), termed Improved FOX (IFOX), incorporating a new adaptive method using a dynamically scaled step-size parameter to balance exploration and exploitation based on the current solution's fitness value. The proposed IFOX also reduces the number of hyperparameters by removing four parameters (C1, C2, a, Mint) and refines the primary equations of FOX. To evaluate its performance, IFOX was tested on 20 classical benchmark functions, 61 benchmark test functions from the congress on evolutionary co

SUBMITTER: Jumaah MA 

PROVIDER: S-EPMC12445531 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature