Enhanced Gaining-Sharing Knowledge-based algorithm

https://doi.org/10.1016/j.rico.2025.100542

Highlights

• •Enhanced Gaining-Sharing Knowledge-based algorithm (eGSK) to solve unconstrained optimization problems over continuous space is proposed.
• •The basic inspiration for the modification consists of (adjust selection criteria, modify parameters setup, and escape from local minimum solution), respectively.
• •Comparisons and statistical tests were made with the original GSK and other algorithms to verify and analyze (eGSK) algorithm performance.
• •Numerical experiments were conducted on 29 test problem sets in 10, 30, 50, and 100 dimensions from the CEC 2017 benchmark.
• •Experimental results show that (eGSK) outperforms others in terms of robustness, convergence, and quality of the solutions obtained.