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

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

Secrets of effective optimization of trading strategies in metaheuristic approaches. Community of Scientists Optimization is a new population-based algorithm inspired by the mechanisms of the scientific community. Unlike traditional nature-inspired metaphors, CoSO models unique aspects of human scientific activity: publishing results in journals, competing for grants, and forming research teams.

Continuing our exploration of optimization methods, in this article we will consider another approach to solving optimization problems - the CoSO (Community of Scientists Optimization) algorithm, based on simulating the mechanisms of the scientific community. Unlike classical bio-inspired algorithms, CoSO replicates the unique characteristics of scientific activity: publishing results in journals, competition for grants, forming research groups, and balancing in-depth study of known areas with the search for fundamentally new solutions. The CoSO algorithm was developed and published in 2012 by two scientists, A. Milani and V. Santucci.

An interesting feature of this approach is the natural self-organization of the search process. Like a real scientific community, the algorithm concentrates resources in promising areas, stores and disseminates the best solutions through the journal mechanism, and maintains the necessary diversity by funding "outsiders". Dynamically changing population size allows the algorithm to adapt to the specifics of a particular problem without the need for fine-tuning of parameters, which is another innovation because we usually use a constant population. In this article, we will take a detailed look at the mathematical foundations of the algorithm, its key components, and the mechanisms of interaction between them. 

Author: Andrey Dik