Fix bug self.epoch in SCSO

Author: thieu1995

mealpy/swarm_based/SCSO.py

@@ -16,10 +16,6 @@ class OriginalSCSO(Optimizer):
         1. https://link.springer.com/article/10.1007/s00366-022-01604-x
         2. https://www.mathworks.com/matlabcentral/fileexchange/110185-sand-cat-swarm-optimization
 
-    Notes:
-        1. The matlab code will not work since the R value always in the range (-1, 1).
-        2. The authors make a mistake in matlab code. It should be 0 <= R <= 1 in the If condition
-
     Examples
     ~~~~~~~~
     >>> import numpy as np
@@ -57,11 +53,11 @@ def __init__(self, epoch=10000, pop_size=100, **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.set_parameters(["epoch", "pop_size"])
-        self.P = np.arange(1, 361)
         self.sort_flag = False
 
     def initialize_variables(self):
         self.S = 2      # maximum Sensitivity range
+        self.P = np.arange(1, 361)
 
     def get_index_roulette_wheel_selection__(self, p):
         p = p / np.sum(p)
@@ -75,16 +71,15 @@ def evolve(self, epoch):
         Args:
             epoch (int): The current iteration
         """
-        t = self.epoch + 1
-        guides_r = self.S - (self.S * t / self.epoch)
+        guides_r = self.S - (self.S * (epoch + 1) / self.epoch)
         pop_new = []
         for idx in range(0, self.pop_size):
             r = np.random.rand() * guides_r
             R = (2*guides_r)*np.random.rand() - guides_r        # controls to transition phases
             pos_new = self.pop[idx][self.ID_POS].copy()
             for jdx in range(0, self.problem.n_dims):
                 teta = self.get_index_roulette_wheel_selection__(self.P)
-                if 0 <= R <= 1:
+                if -1 <= R <= 1:
                     rand_pos = np.abs(np.random.rand() * self.g_best[self.ID_POS][jdx] - self.pop[idx][self.ID_POS][jdx])
                     pos_new[jdx] = self.g_best[self.ID_POS][jdx] - r * rand_pos * np.cos(teta)
                 else:

run.py

@@ -19,7 +19,7 @@
 from mealpy.swarm_based.AGTO import MGTO
 from mealpy import EOA, SBO, SMA, SOA, MA, BRO, BSO, CHIO, FBIO, HBO, QSA, SARO, TLO
 from mealpy import PSS, ASO, EO, FLA, BFO, GJO, GTO, HHO, MPA, SeaHO, SRSR, AVOA, SA, BSO
-from mealpy import GWO
+from mealpy import GWO, SCSO
 
 # from mealpy.utils.problem import Problem
 from mealpy import Problem
@@ -114,6 +114,7 @@ def fit_func(self, solution):
     model = GWO.GWO_WOA(epoch, pop_size)
     model = GWO.RW_GWO(epoch, pop_size)
     # model = BSO.ImprovedBSO(epoch, pop_size)
+    model = SCSO.OriginalSCSO(epoch, pop_size)
     best_position, best_fitness = model.solve(P1)#, mode="thread", n_workers=4, termination=term_dict1)
 
     print(best_position)