Discussing the article: "Community of Scientists Optimization (CoSO): Practice"

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Check out the new article: Community of Scientists Optimization (CoSO): Practice.

We resume the topic of optimization by the scientific community. CoSO should not be viewed as a ready-made solution, but as a promising research platform. With proper development, CoSO can find its niche in tasks where adaptability and resilience to change are important, and computation time is not critical.

The CoSO algorithm demonstrates average results on the test functions, achieving 43% of the maximum possible performance. Of course, I expected more impressive results. The test stand offers an expanded set of functions, including standard and well-known ones, where anyone can further experiment with both parameter selection and function selection to better exploit the algorithm capabilities.

The main drawback of the current implementation is its high computational complexity. The multi-layer architecture with logs, dynamic resource allocation and adaptive population management creates a significant load. The algorithm is noticeably slower than other population methods, which limits its applicability to problems requiring fast solutions.

Author: Andrey Dik