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Article summary:
1. A new global optimization strategy based on a Set Membership (SM) framework is proposed to solve non-convex optimization problems.
2. The approach employs SM concepts to decide whether to switch from an exploitation mode to an exploration one, and vice-versa.
3. The performance of the SMGO algorithm is evaluated on a set of benchmark non-convex problems and compared with other global optimization approaches.
Article analysis:
The article provides a detailed overview of the SMGO algorithm, which is a new global optimization strategy based on a Set Membership (SM) framework for solving non-convex optimization problems. The article presents theoretical properties regarding convergence and computational complexity, as well as implementation aspects of the algorithm. Furthermore, the performance of the SMGO algorithm is evaluated on a set of benchmark non-convex problems and compared with those of other global optimization approaches.
The article appears to be reliable and trustworthy in its presentation of the SMGO algorithm, providing sufficient detail about its theoretical properties, implementation aspects, and performance evaluation results. However, there are some potential biases that should be noted in the article. For example, it does not provide any information about possible risks associated with using this algorithm or any unexplored counterarguments that could be made against it. Additionally, while the article does compare the performance of SMGO with other global optimization approaches, it does not present both sides equally or explore any potential drawbacks associated with these other algorithms. Finally, there may also be some promotional content in the article as it focuses solely on highlighting the benefits of using this particular approach without exploring any potential drawbacks or limitations associated with it.