Quzhou University Researchers Report Recent Findings in Computational Intelligence (Enhanced Gaussian Bare-Bone Imperialist Competition Algorithm Based on Doubling Sampling and Quasi-oppositional Learning for Global Optimization).

Researchers from Quzhou University have developed a new algorithm called the quasi-oppositional learning-based double Gaussian sampling bare-bone imperialist competitive algorithm (QOLBDGSBB-ICA) for global optimization. This algorithm improves upon the Gaussian bare-bone imperialist competitive algorithm (GBB-ICA) by introducing a double Gaussian sampling strategy and incorporating the quasi-oppositional learning technique. The QOLBDGSBB-ICA algorithm was tested on benchmark functions and engineering design problems, and it outperformed other referenced ICA variants. This research contributes to the field of computational intelligence and offers a more effective algorithm for global optimization. [Extracted from the article]