build_new_optimizer.py

#!/usr/bin/env python
# Created by "Thieu" at 18:01, 28/08/2022 ----------%                                                                               
#       Email: nguyenthieu2102@gmail.com            %                                                    
#       Github: https://github.com/thieu1995        %                         
# --------------------------------------------------%

import numpy as np
from mealpy.optimizer import Optimizer


class MyAlgorithm(Optimizer):
    """
    This is an example how to build new optimizer

    Notes
    ~~~~~
    + Read more at: https://mealpy.readthedocs.io/en/latest/pages/build_new_optimizer.html
    """

    def __init__(self, epoch=10000, pop_size=100, m_clusters=2, p1=0.75, **kwargs):
        """
        Args:

            epoch (int): maximum number of iterations, default = 10000
            pop_size (int): number of population size, default = 100
            m_clusters (int): number of clusters
            p1 (float): the probability of updating the worst solution
        """
        super().__init__(**kwargs)
        self.epoch = self.validator.check_int("epoch", epoch, [1, 100000])
        self.pop_size = self.validator.check_int("pop_size", pop_size, [10, 10000])
        self.m_clusters = self.validator.check_int("m_clusters", m_clusters, [2, 5])
        self.p1 = self.validator.check_float("p1", p1, (0, 1.0))

        self.nfe_per_epoch = self.pop_size
        self.sort_flag = True
        # Determine to sort the problem or not in each epoch
        ## if True, the problem always sorted with fitness value increase
        ## if False, the problem is not sorted

    def initialize_variables(self):
        """
        This is method is called before initialization() method.
        Returns:

        """
        ## Support variables
        self.n_agents = int(self.pop_size / self.m_clusters)
        self.space = self.problem.ub - self.problem.lb

    def initialization(self):
        """
        Override this method if needed. But the first 2 lines of code is required.
        """
        ### Required code
        if self.pop is None:
            self.pop = self.create_population(self.pop_size)

        ### Your additional code can be implemented here
        self.mean_pos = np.mean([agent[self.ID_POS] for agent in self.pop])

    def evolve(self, epoch):
        """
        You can do everything in this function (i.e., Loop through the population multiple times)

        Args:
            epoch (int): The current iteration
        """
        epxilon = 1 - 1 * (epoch + 1) / self.epoch      # The epxilon in each epoch is changing based on this equation

        ## 1. Replace the almost worst agent by random agent
        if np.random.uniform() < self.p1:
            idx = np.random.randint(self.n_agents, self.pop_size)
            solution_new = self.create_solution(self.problem.lb, self.problem.ub)
            self.pop[idx] = solution_new

        ## 2. Replace all bad solutions by current_best + noise
        for idx in range(self.n_agents, self.pop_size):
            pos_new = self.pop[0][self.ID_POS] + epxilon * self.space * np.random.normal(0, 1)
            pos_new = self.amend_position(pos_new, self.problem.lb, self.problem.ub)
            fit_new = self.get_target_wrapper(pos_new)
            if self.compare_agent([pos_new, fit_new], self.pop[idx]):
                self.pop[idx] = [pos_new, fit_new]

        ## 3. Move all good solutions toward current best solution
        for idx in range(0, self.n_agents):
            if idx == 0:
                pos_new = self.pop[idx][self.ID_POS] + epxilon * self.space * np.random.uniform(0, 1)
            else:
                pos_new = self.pop[idx][self.ID_POS] + epxilon * self.space * (self.pop[0][self.ID_POS] - self.pop[idx][self.ID_POS])
            pos_new = self.amend_position(pos_new, self.problem.lb, self.problem.ub)
            fit_new = self.get_target_wrapper(pos_new)
            if self.compare_agent([pos_new, fit_new], self.pop[idx]):
                self.pop[idx] = [pos_new, fit_new]

        ## Do additional works here if needed.


## Time to test our new optimizer
def fitness(solution):
    return np.sum(solution**2)

problem_dict1 = {
    "fit_func": fitness,
    "lb": [-100, ]*100,
    "ub": [100, ]*100,
    "minmax": "min",
}

epoch = 50
pop_size = 50
model = MyAlgorithm(epoch, pop_size)
best_position, best_fitness = model.solve(problem_dict1)
print(f"Solution: {best_position}, Fitness: {best_fitness}")