Enhanced Gaining-Sharing Knowledge-based algorithm

• An improved Gaining-Sharing Knowledge-based algorithm (AIGSK) 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 Solution), respectively • Comparisons and statistical tests were made with the original GSK and other algorithms to verify and analyze (AIGSK) 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 (AIGSK) outperforms others in terms of robustness, convergence, and quality of the solutions obtained. This article suggests an Enhanced Gaining-Sharing Knowledge-based algorithm (eGSK) to resolve unrestricted optimization minimization problems over a continuous space. This algorithm is based on the idea that people learn and share knowledge throughout their lives. The modification is fundamentally inspired by the principles of Adjust Selection Criteria, Modify Parameters Setup, and Escape from Local Solution, respectively. We conducted comparisons and statistical tests with the Gaining-Sharing Knowledge-based algorithm (GSK) and other algorithms to verify and analyze the performance of the eGSK algorithm. We also performed numerical experiments on 29 test problem sets in 10, 30, 50, and 100 dimensions from the Congress on Evolutionary Computation (CEC) 2017 benchmark. The results were compared with three GSK variant algorithms, seven state-of-the-art algorithms, and GSK alongside components of the eGSK algorithm. According to test results, the eGSK algorithm performs exceptionally well at solving optimization problems with 30, 50, and 100 dimensions and is competitive in 10 dimensions. This means the proposed eGSK algorithm outperforms its competitors and achieves more competitive results, especially with high dimensions.