OptimizationForEveryone/Run_Optimization.py at main · Optimization-for-Everyone/OptimizationForEveryone · GitHub

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from algorithms import HHO,BAT,CS,DE,FFA,GWO,JAYA,MFO,MVO,PSO,SCA,SSA,WOA,GA,SA,HS
from algorithms.SMA import BaseSMA
from algorithms.HHO import HHO
from solution import solution
from enumOptimizations import Optimizations
from write_operations import WriteOperations
from enumFunctions import Functions
import functions
import sys
import numpy


def Single(opt, params, sma):
    switcher = {
                Optimizations.BAT: lambda: Run_BAT(params[0],params[1],params[2],params[3],params[4],params[5]),
                Optimizations.CS: lambda: Run_CS(params[0],params[1],params[2],params[3],params[4],params[5]),
                Optimizations.DE: lambda: Run_DE(params[0],params[1],params[2],params[3],params[4],params[5]),
                Optimizations.FFA: lambda: Run_FFA(params[0],params[1],params[2],params[3],params[4],params[5]),
                Optimizations.GA: lambda: Run_GA(params[0],params[1],params[2],params[3],params[4],params[5]),
                Optimizations.GWO: lambda: Run_GWO(params[0],params[1],params[2],params[3],params[4],params[5]),
                Optimizations.HHO: lambda: Run_HHO(params[0],params[1],params[2],params[3],params[4],params[5]),
                Optimizations.JAYA: lambda: Run_JAYA(params[0],params[1],params[2],params[3],params[4],params[5]),
                Optimizations.MFO: lambda: Run_MFO(params[0],params[1],params[2],params[3],params[4],params[5]),
                Optimizations.MVO: lambda: Run_MVO(params[0],params[1],params[2],params[3],params[4],params[5]),
                Optimizations.PSO: lambda: Run_PSO(params[0],params[1],params[2],params[3],params[4],params[5]),
                Optimizations.SCA: lambda: Run_SCA(params[0],params[1],params[2],params[3],params[4],params[5]),
                Optimizations.SMA: lambda: Run_SMA(sma[0], sma[1], sma[2], sma[3], sma[4], sma[5], sma[6]),
                Optimizations.SSA: lambda: Run_SSA(params[0],params[1],params[2],params[3],params[4],params[5]),
                Optimizations.WOA: lambda: Run_WOA(params[0],params[1],params[2],params[3],params[4],params[5]),
                Optimizations.WOA: lambda: Run_WOA(params[0],params[1],params[2],params[3],params[4],params[5]),
                Optimizations.SA: lambda: Run_SA(params[0],params[1],params[2],params[3],params[4],params[5]),
        }
    func = switcher.get(opt, lambda: 'Invalid')
    sol = func()
    return sol

def Triple(opt1, opt2, opt3, param1,param2,param3, sma1,sma2,sma3,numberOfRuns,numberOfRuns2,numberOfRuns3):
    sol1 = MultipleRun(opt1,param1,sma1,numberOfRuns)
    sol2 = MultipleRun(opt2,param2,sma2,numberOfRuns2)
    sol3 = MultipleRun(opt3,param3,sma3,numberOfRuns3)
    return sol1, sol2, sol3

def Double(opt1, opt2, param1,param2, sma1, sma2,numberOfRuns,numberOfRuns2):
    sol1 = MultipleRun(opt1,param1,sma1,numberOfRuns)
    sol2 = MultipleRun(opt2,param2,sma2,numberOfRuns2)
    return sol1, sol2

def MultipleRun(opt, params, sma, number):
    if number <= 0:
       number = 1

    min = sys.float_info.max
    best_sol = solution()
    outputs = numpy.array([], dtype='float64')
    for i in range(number):
        sol = Single(opt, params, sma)
        output = sol.best

        if output <= min:
             min = output
             best_sol = sol
        outputs = numpy.append(outputs,output)

    bestfitnessMean = numpy.mean(outputs)
    bestfitnessStd = numpy.std(outputs)
    multiple_run_result = [outputs, bestfitnessMean, bestfitnessStd]


    WriteOperations(optimizationName = opt.name, fuctionName = Functions(params[0]).name,
        solution = best_sol, multipleRun = number, multiple_run_result = multiple_run_result).write()

    return best_sol

def Run_HHO(functionIndex,maxiter,dim,searchAgents_no,_lb,_ub):
        #lb=TextBox_lb.text
        lb = [_lb]
        ub = [_ub]
        obj_func=functions.selectFunction(functionIndex)
        solution = HHO(obj_func, lb, ub, dim, searchAgents_no, maxiter)
        sol = numpy.array(solution.convergence)
        return solution
def Run_SMA(functionIndex,problem_size,verbose,epoch,pop_size,_lb,_ub):
        lb = [_lb]
        ub = [_ub]
        obj_func=functions.selectFunction(functionIndex)
        md1 = BaseSMA(obj_func, lb, ub, problem_size, verbose, epoch, pop_size)
        best_pos1, best_fit1, list_loss1, sol1, sol2 = md1.train()
        # return : the global best solution, the fitness of global best solution and the loss of training process in each epoch/iteration
        #print(md1.solution[0])
        #print(md1.solution[1])
        #print(md1.loss_train)
        return sol2
def Run_GA(functionIndex,maxiter,dim,searchAgents_no,_lb,_ub):
        lb = [_lb]
        ub = [_ub]
        obj_func = functions.selectFunction(functionIndex)
        solution = GA.GA(obj_func, _lb, _ub, dim, searchAgents_no, maxiter)
        sol = numpy.array(solution.convergence)
        return solution

def Run_BAT(functionIndex,maxiter,dim,searchAgents_no,_lb,_ub):
        lb = [_lb]
        ub = [_ub]
        obj_func = functions.selectFunction(functionIndex)
        solution = BAT.BAT(obj_func, _lb, _ub, dim, searchAgents_no, maxiter)
        return solution

def Run_CS(functionIndex,maxiter,dim,searchAgents_no,_lb,_ub):
        lb = [_lb]
        ub = [_ub]
        obj_func = functions.selectFunction(functionIndex)
        solution = CS.CS(obj_func, _lb, _ub, dim, searchAgents_no, maxiter)
        return solution

def Run_DE(functionIndex,maxiter,dim,searchAgents_no,_lb,_ub):
        lb = [_lb]
        ub = [_ub]
        obj_func = functions.selectFunction(functionIndex)
        solution = DE.DE(obj_func, _lb, _ub, dim, searchAgents_no, maxiter)
        return solution

def Run_FFA(functionIndex,maxiter,dim,searchAgents_no,_lb,_ub):
        lb = [_lb]
        ub = [_ub]
        obj_func = functions.selectFunction(functionIndex)
        solution = FFA.FFA(obj_func, _lb, _ub, dim, searchAgents_no, maxiter)
        return solution

def Run_GWO(functionIndex,maxiter,dim,searchAgents_no,_lb,_ub):
        lb = [_lb]
        ub = [_ub]
        obj_func = functions.selectFunction(functionIndex)
        solution = GWO.GWO(obj_func, _lb, _ub, dim, searchAgents_no, maxiter)
        return solution

def Run_JAYA(functionIndex,maxiter,dim,searchAgents_no,_lb,_ub):
        lb = [_lb]
        ub = [_ub]
        obj_func = functions.selectFunction(functionIndex)
        solution = JAYA.JAYA(obj_func, _lb, _ub, dim, searchAgents_no, maxiter)
        return solution

def Run_MFO(functionIndex,maxiter,dim,searchAgents_no,_lb,_ub):
        lb = [_lb]
        ub = [_ub]
        obj_func = functions.selectFunction(functionIndex)
        solution = MFO.MFO(obj_func, _lb, _ub, dim, searchAgents_no, maxiter)
        return solution

def Run_MVO(functionIndex,maxiter,dim,searchAgents_no,_lb,_ub):
        lb = [_lb]
        ub = [_ub]
        obj_func = functions.selectFunction(functionIndex)
        solution = MVO.MVO(obj_func, _lb, _ub, dim, searchAgents_no, maxiter)
        return solution

def Run_PSO(functionIndex,maxiter,dim,searchAgents_no,_lb,_ub):
        lb = [_lb]
        ub = [_ub]
        obj_func = functions.selectFunction(functionIndex)
        solution = PSO.PSO(obj_func, _lb, _ub, dim, searchAgents_no, maxiter)
        return solution

def Run_SCA(functionIndex,maxiter,dim,searchAgents_no,_lb,_ub):
        lb = [_lb]
        ub = [_ub]
        obj_func = functions.selectFunction(functionIndex)
        solution = SCA.SCA(obj_func, _lb, _ub, dim, searchAgents_no, maxiter)
        return solution
def Run_SSA(functionIndex,maxiter,dim,searchAgents_no,_lb,_ub):
        lb = [_lb]
        ub = [_ub]
        obj_func = functions.selectFunction(functionIndex)
        solution = SSA.SSA(obj_func, _lb, _ub, dim, searchAgents_no, maxiter)
        return solution

def Run_WOA(functionIndex,maxiter,dim,searchAgents_no,_lb,_ub):
        lb = [_lb]
        ub = [_ub]
        obj_func = functions.selectFunction(functionIndex)
        solution = WOA.WOA(obj_func, _lb, _ub, dim, searchAgents_no, maxiter)
        return solution

def Run_SA(functionIndex,maxiter,dim,searchAgents_no,_lb,_ub):
        obj_func = functions.selectFunction(functionIndex)
        solution = SA.simulated_annealing( min_values = _lb, max_values = _ub, dim = dim, temperature_iterations = maxiter, target_function = obj_func)
        return solution

def Run_HS(functionIndex,maxiter,dim,searchAgents_no,_lb,_ub):
        lb = [_lb]
        ub = [_ub]
        obj_func = functions.selectFunction(functionIndex)
        solution = HS.HS(obj_func, _lb, _ub, dim, searchAgents_no, maxiter, num_processes = 2)
        return solution