# !/usr/bin/env python
# Created by "Thieu" at 16:19, 16/03/2020 ----------%
#       Email: <EMAIL>            %
#       Github: https://github.com/thieu1995        %
# --------------------------------------------------%
#
# Examples:
# >>>
# >>> from mealpy.swarm_based import PSO
# >>> import numpy as np
# >>>
# >>> def fitness_function(solution):
# >>>     return np.sum(solution ** 2)
# >>>
# >>> problem = {
# >>>    "fit_func": fitness_function,
# >>>    "lb": [-100, ] * 30,
# >>>    "ub": [100, ] * 30,
# >>>    "minmax": "min",
# >>>    "save_population": False,
# >>>    "log_to": "file",
# >>>    "log_file": "mealpy.log"
# >>> }
# >>>
# >>> ## Run the algorithm
# >>> model = PSO.C_PSO(problem, epoch=5, pop_size=50, name="C-PSO", fit_name="Square")
# >>> best_position, best_fitness = model.solve()
# >>> print(f"Best solution: {best_position}, Best fitness: {best_fitness}")


__version__ = "2.4.0"

from . import bio_based
from . import evolutionary_based
from . import human_based
from . import math_based
from . import music_based
from . import physics_based
from . import probabilistic_based
from . import swarm_based
from . import system_based
from . import utils

# !/usr/bin/env python
# Created by "Thieu" at 16:19, 16/03/2020 ----------%
#       Email: <EMAIL>            %
#       Github: https://github.com/thieu1995        %
# --------------------------------------------------%
#
# Examples:
# >>>
# >>> from mealpy.swarm_based import PSO
# >>> import numpy as np
# >>>
# >>> def fitness_function(solution):
# >>>     return np.sum(solution ** 2)
# >>>
# >>> problem = {
# >>>    "fit_func": fitness_function,
# >>>    "lb": [-100, ] * 30,
# >>>    "ub": [100, ] * 30,
# >>>    "minmax": "min",
# >>>    "save_population": False,
# >>>    "log_to": "file",
# >>>    "log_file": "mealpy.log"
# >>> }
# >>>
# >>> ## Run the algorithm
# >>> model = PSO.C_PSO(problem, epoch=5, pop_size=50, name="C-PSO", fit_name="Square")
# >>> best_position, best_fitness = model.solve()
# >>> print(f"Best solution: {best_position}, Best fitness: {best_fitness}")


__version__ = "2.4.0"

from . import bio_based
from . import evolutionary_based
from . import human_based
from . import math_based
from . import music_based
from . import physics_based
from . import probabilistic_based
from . import swarm_based
from . import system_based
from . import utils

# !/usr/bin/env python # Created by "Thieu" at 16:19, 16/03/2020 ----------% #       Email: <EMAIL>            % #       Github: https://github.com/thieu1995        % # --------------------------------------------------% # # Examples: # >>> # >>> from mealpy.swarm_based import PSO # >>> import numpy as np # >>> # >>> def fitness_function(solution): # >>>     return np.sum(solution ** 2) # >>> # >>> problem = { # >>>    "fit_func": fitness_function, # >>>    "lb": [-100, ] * 30, # >>>    "ub": [100, ] * 30, # >>>    "minmax": "min", # >>>    "save_population": False, # >>>    "log_to": "file", # >>>    "log_file": "mealpy.log" # >>> } # >>> # >>> ## Run the algorithm # >>> model = PSO.C_PSO(problem, epoch=5, pop_size=50, name="C-PSO", fit_name="Square") # >>> best_position, best_fitness = model.solve() # >>> print(f"Best solution: {best_position}, Best fitness: {best_fitness}")