My realization of multimodal evolutionary algorithm called sequential nishing in python3.
Study of multimodal algorithms for finding several local maxima of a function.
Scope of definition is [0; 1].
An interesting algorithm called sequential nishing proposed by Beasley et al. In this method after searching for a global optimal solution using any search algorithm, including an evolutionary one, we can change the landscape of the solution in such a way that the attraction to the region is already the found peak is penalized to ensure that the next time the search algorithm is run, another peak is found. Continuing the process described above, we will find several consecutive peaks. The key to a consistent niche is changing the landscape of the solution. As a rule, the punishment for striving to the area of the already found peak(s) means a decrease in the values of the objective function in these peaks.
Disadvantage of the sequential niche is the sequential approach, that is, one run per peak, which is inefficient for real-time applications.
Max points and their values are printed in console.
In animation below you can see how this algorithm works.
