Exchange Market Algorithm
Example:
from bejoor.economics_based import ExchangeMarketAlgorithm
def sphere_function(sol):
return sum(x**2 for x in sol)
solution_vector = [{"type": "float", "lower_bound": -5, "upper_bound": 10}] * 7
ema = ExchangeMarketAlgorithm(objective_function=sphere_function, solution_vector_size=7,
solution_vector=solution_vector, optimization_side="min",
trading_probability=0.8, population_size=250, epochs=50)
ema.run()
print(f'Best Global Objective Value: {ema.global_best_objective_value}')
print(f'Best Global Solution: {ema.global_best_solution}')
Parameters:
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objective_function: Objective function needs to be optimized.
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solution_vector_size: Vector size of the candidate solutions.
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solution_vector: A vector which determines the types of each variable in solution vectors.
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optimization_side: Determines maximize or minimize the objective function.
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target_objective_value: Target Objective value.
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target_objective_lower_bound: Target Objective lower bound.
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target_objective_upper_bound: Target Objective upper bound.
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population_size: Number of individuals in the population.
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epochs: Number of generations to run the algorithm.
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trading_probability: Probability of trading information between individuals (solutions).