文章复现： guo_integrated_2012

SMO/main.py

@@ -0,0 +1,79 @@
+# -*- coding: utf-8 -*-
+"""
+Python code of Spider-Monkey Optimization (SMO)
+Coded by: Mukesh Saraswat (emailid: saraswatmukesh@gmail.com), Himanshu Mittal (emailid: himanshu.mittal224@gmail.com) and Raju Pal (emailid: raju3131.pal@gmail.com)
+The code template used is similar to code given at link: https://github.com/himanshuRepo/CKGSA-in-Python
+ and C++ version of the SMO at link: http://smo.scrs.in/
+
+Reference: Jagdish Chand Bansal, Harish Sharma, Shimpi Singh Jadon, and Maurice Clerc. "Spider monkey optimization algorithm for numerical optimization." Memetic computing 6, no. 1, 31-47, 2014.
+@link: http://smo.scrs.in/
+
+-- Main.py: Calling the Spider-Monkey Optimization (SMO) Algorithm
+                for minimizing of an objective Function
+
+Code compatible:
+ -- Python: 2.* or 3.*
+"""
+import smo
+import benchmarks
+import csv
+import numpy
+import time
+import math
+
+
+def selector(func_details, popSize, Iter, succ_rate, mean_feval):
+    function_name = func_details[0]
+    lb = func_details[1]
+    ub = func_details[2]
+    dim = func_details[3]
+    acc_err = func_details[4]
+    obj_val = func_details[5]
+
+
+    x, succ_rate, mean_feval = smo.main(getattr(benchmarks, function_name), lb, ub, dim, popSize, Iter, acc_err,
+                                        obj_val, succ_rate, mean_feval)
+    return x, succ_rate, mean_feval
+
+
+# Select number of repetitions for each experiment.
+# To obtain meaningful statistical results, usually 30 independent runs are executed for each algorithm.
+NumOfRuns = 2
+
+# Select general parameters for all optimizers (population size, number of iterations)
+PopulationSize = 10
+Iterations = 500
+
+mean_error = 0
+total_feval = 0
+mean1 = 0
+var = 0
+sd = 0
+mean_feval = 0
+succ_rate = 0
+GlobalMins = numpy.zeros(NumOfRuns)
+
+for k in range(0, NumOfRuns):
+
+    func_details = benchmarks.getFunctionDetails()
+    print("Run: {}".format(k + 1))
+    x, succ_rate, mean_feval = selector(func_details, PopulationSize, Iterations, succ_rate, mean_feval)
+    mean_error = mean_error + x.error;
+    mean1 = mean1 + x.convergence[-1]
+    total_feval = total_feval + x.feval
+    GlobalMins[k] = x.convergence[-1]
+
+
+mean1 = mean1 / NumOfRuns;
+mean_error = mean_error / NumOfRuns
+if (succ_rate > 0):
+    mean_feval = mean_feval / succ_rate
+total_feval = total_feval / NumOfRuns
+for k in range(NumOfRuns):
+    var = var + math.pow((GlobalMins[k] - mean1), 2)
+var = var / NumOfRuns
+sd = math.sqrt(var)
+
+print(
+    "Values after executing SMO: \n Mean Error:{} \n Mean Function eval:{} \n Total Function eval:{} \n Variance:{} \n STD:{}".format(
+        mean_error, mean_feval, total_feval, var, sd))