Discussing the article: "Corona Virus Optimization (CVO) Algorithm"

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Check out the new article: Corona Virus Optimization (CVO) Algorithm.

We describe and implement CVO: infection as candidate generation, coordinate-wise normal perturbation, and a dynamic population. The algorithm is integrated into C_AO and has been tested on a standard benchmark. The analysis identifies a scaling-related cause of stagnation and provides a practical solution — a shift to a relative step based on the range width. The code is ready for use.

The number of nature-inspired optimization algorithms is growing every year, and nearly every new study promises attractive plots showing rapid convergence. For a trader looking to optimize the parameters of their trading system, it is becoming increasingly difficult to distinguish a truly powerful tool from a mediocre one presented in a favorable light on easy, low-dimensional problems. Corona Virus Optimization (CVO) is based on a model of coronavirus spread: infected individuals transmit symptoms to susceptible individuals, the infected population grows or is renewed after another success, and the force of infection controls the size of the search step. It's a fresh idea, and the original paper reports strong results — but how will the algorithm perform on a fair benchmark shared by all algorithms, without cherry-picking convenient test functions?

In this article, I walk through the implementation of CVO: from the original formulas to working code in the standard C_AO test harness. I explain how the dynamic population works, the meaning of R0 and the force of infection I/N, and the necessary departures from the original paper. The most important of these is coordinate-wise perturbation, which prevents the algorithm from exploiting the diagonal symmetry of the test functions. Next comes a run on the standard test suite (Hilly, Forest, and Megacity in 10, 50, and 1,000 dimensions) and, more importantly than the results themselves, an analysis of why a strictly faithful implementation performed only as well as a random walk and what exactly lifts it above that level.

Author: Andrey Dik

[salehrayan246]

Well done.

Have you seen the Tianji Horse Racing Optimisation?

https://link.springer.com/article/10.1007/s10462-025-11269-9

It looks interesting. Can it be replicated for MQL5?