[Python] How to Compare Optimization Algorithms (GA, PSO, HC) in a Dynamic Environment Using...

I'm currently working on a project where I need to compare the performance of different optimization algorithms — specifically Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Hill Climbing (HC) — in a dynamic environment using Python.

I have already found this GitHub repository as a base: https://github.com/prakhargurawa/Optimization-Algo-Comparision

However, I need to adapt it to a dynamic environment, where the objective function changes over time (e.g., the optimum location shifts every few generations).

There's anyway to do it on a simple way?

I'm kind of desperate for help! Thanks in advance to anyone that answer this

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