When I was researching a function without given all local minima, like the underlined function:
I used optimtool.unconstrain to search local minima, got an efficient experience about searching the nearest minimum point. Add a mechanism to jump out of the local area would increase the runtime of the whole script, so porgo is a new progam to accelerate to search global minima.
refer to test.py and the global minima of 4-dimensional
glos is the main runtime to serve as a global search class, users can run train_gen module with given cycles at any times until the function searching process converged.
init:
- objective_function: Callable, a high-dimensional function with convex, non-convex, and many local minima.
- bounds: List[List[float]] | List[Tuple[float]], changes this value makes a significant influence of mini and fit_mini.
- mutation: float=0.5, increase this value makes the search radius larger.
- recombination: float=0.9, increase this value allows larger number of mutation.
rand_pop:
- population_size: int=50, randomly init the population (or called initial points) with shape at (population, dimension).
- verbose: bool=False, whether to output initial population when manually replace the random generated rule.
train_gen:
- cycles: int=1000, try to run several times (until converged) when give a smaller cycle number if search bounds is in large space.
result:
- verbose: bool=False, whether to output console information after search populations were updated (check self.mini and self.fit_mini, the top3 updated results are (self.mini, self.fit_mini) < (self.medi, self.fit_medi) < (self.maxi, self.fit_maxi)).
Storn, R and Price, K, Differential Evolution - a Simple and Efficient Heuristic for Global Optimization over Continuous Spaces, Journal of Global Optimization, 1997, 11, 341 - 359.