def __init__(self, pll_type=None, *args, **kwargs):

        """
		ALHSO Optimizer Class Initialization
		
		**Keyword arguments:**
		
		- pll_type -> STR: Parallel Implementation (None, 'POA'-Parallel Objective Analysis), *Default* = None
		
		Documentation last updated:  Feb. 16, 2010 - Peter W. Jansen
		"""

        #
        if pll_type == None:
            self.poa = False
        elif pll_type.upper() == "POA":
            self.poa = True
        else:
            raise ValueError("pll_type must be either None or 'POA'")
            # end

            #
        name = "ALHSO"
        category = "Global Optimizer"
        def_opts = {
            "hms": [int, 5],  # Memory Size [1,50]
            "hmcr": [float, 0.95],  # Probability rate of choosing from memory [0.7,0.99]
            "par": [float, 0.65],  # Pitch adjustment rate [0.1,0.99]
            "dbw": [int, 2000],  # Variable Bandwidth Quantization
            "maxoutiter": [int, 2e3],  # Maximum Number of Outer Loop Iterations (Major Iterations)
            "maxinniter": [int, 2e2],  # Maximum Number of Inner Loop Iterations (Minor Iterations)
            "stopcriteria": [int, 1],  # Stopping Criteria Flag
            "stopiters": [
                int,
                10,
            ],  # Consecutively Number of Outer Iterations for which the Stopping Criteria must be Satisfied
            "etol": [float, 1e-6],  # Absolute Tolerance for Equality constraints
            "itol": [float, 1e-6],  # Absolute Tolerance for Inequality constraints
            "atol": [float, 1e-6],  # Absolute Tolerance for Objective Function
            "rtol": [float, 1e-6],  # Relative Tolerance for Objective Function
            "prtoutiter": [int, 0],  # Number of Iterations Before Print Outer Loop Information
            "prtinniter": [int, 0],  # Number of Iterations Before Print Inner Loop Information
            "xinit": [int, 0],  # Initial Position Flag (0 - no position, 1 - position given)
            "rinit": [float, 1.0],  # Initial Penalty Factor
            "fileout": [int, 1],  # Flag to Turn On Output to filename
            "filename": [
                str,
                "ALHSO.out",
            ],  # We could probably remove fileout flag if filename or fileinstance is given
            "seed": [float, 0],  # Random Number Seed (0 - Auto-Seed based on time clock)
            "scaling": [int, 1],  # Design Variables Scaling Flag (0 - no scaling, 1 - scaling between [-1,1])
        }
        informs = {}
        Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		FILTERSD Optimizer Class Initialization
		
		**Keyword arguments:**
		
		- pll_type -> STR: Parallel Implementation (None, 'POA'-Parallel Objective Analysis), *Default* = None
		
		Documentation last updated:  Feb. 16, 2010 - Peter W. Jansen
		'''
		
		#
		if (pll_type == None):
			self.poa = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
		else:
			raise ValueError("pll_type must be either None or 'POA'")
		#end
		
		#
		name = 'FILTERSD'
		category = 'Local Optimizer'
		def_opts = {
		'rho':[float,100.0],			# initial trust region radius
		'htol':[float,1e-6],			# tolerance allowed in sum h of constraint feasibilities
		'rgtol':[float,1e-5],			# tolerance allowed in reduced gradient l2 norm
		'maxit':[int,1000],				# maximum number of major iterations allowed
		'maxgr':[int,1e5],				# upper limit on the number of gradient calls
		'ubd':[float,1e5],				# upper bound on the allowed constraint violation
		'dchk':[int,0],					# derivative check flag (0 - no check, 1 - check)
		'dtol':[float,1e-8],			# derivative check tolerance
		'iprint':[int,1],				# verbosity of printing (0 - none, 1 - Iter, 2 - Debug)
		'iout':[int,6],     			# Output Unit Number
		'ifile':[str,'FILTERSD.out'],	# Output File Name
		}
		informs = {
		-1 : 'ws not large enough',
		-2 : 'lws not large enough',
		-3 : 'inconsistency during derivative check',
		0 : 'successful run',
		1 : 'unbounded NLP (f <= fmin at an htol-feasible point)',
		2 : 'bounds on x are inconsistent',
		3 : 'local minimum of feasibility problem and h > htol, (nonlinear constraints are locally inconsistent)',
		4 : 'initial point x has h > ubd (reset ubd or x and re-enter)',
		5 : 'maxit major iterations have been carried out',
		6 : 'termination with rho <= htol',
		7 : 'not enough workspace in ws or lws (see message)',
		8 : 'insufficient space for filter (increase mxf and re-enter)',
		9 : 'unexpected fail in LCP solver',
		10 : 'unexpected fail in LCP solver',
		}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		PSQP Optimizer Class Initialization
		
		**Keyword arguments:**
		
		- pll_type -> STR: Parallel Implementation (None, 'POA'-Parallel Objective Analysis), *Default* = None
		
		Documentation last updated:  Feb. 16, 2010 - Peter W. Jansen
		'''
		
		#
		if (pll_type == None):
			self.poa = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
		else:
			raise ValueError("pll_type must be either None or 'POA'")
		#end
		
		#
		name = 'PSQP'
		category = 'Local Optimizer'
		def_opts = {
		'XMAX':[float,1e16],  		# Maximum Stepsize
		'TOLX':[float,1e-16],  		# Variable Change Tolerance
		'TOLC':[float,1e-6],  		# Constraint Violation Tolerance
		'TOLG':[float,1e-6],  		# Lagrangian Gradient Tolerance
		'RPF':[float,1e-4],  		# Penalty Coefficient
		'MIT':[int,1000],  			# Maximum Number of Iterations
		'MFV':[int,2000],  			# Maximum Number of Function Evaluations
		'MET':[int,2],  			# Variable Metric Update (1 - BFGS, 2 - Hoshino)
		'MEC':[int,2],  			# Negative Curvature Correction (1 - None, 2 - Powell's Correction)	
		'IPRINT':[int,2],			# Output Level (0 - None, 1 - Final, 2 - Iter)
		'IOUT':[int,6],     		# Output Unit Number
		'IFILE':[str,'PSQP.out'],	# Output File Name
		}
		informs = {
		1 : 'Change in design variable was less than or equal to tolerance',
		2 : 'Change in objective function was less than or equal to tolerance',
		3 : 'Objective function less than or equal to tolerance',
		4 : 'Maximum constraint value is less than or equal to tolerance',
		11 : 'Maximum number of iterations exceeded',
		12 : 'Maximum number of function evaluations exceeded',
		13 : 'Maximum number of gradient evaluations exceeded',
		-6 : 'Termination criterion not satisfied, but obtained point is acceptable',
		#<0 : 'Method failed',
		}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		SOLVOPT Optimizer Class Initialization
		
		**Keyword arguments:**
		
		- pll_type -> STR: Parallel Implementation (None, 'POA'-Parallel Objective Analysis), *Default* = None
		
		Documentation last updated:  Feb. 16, 2010 - Peter W. Jansen
		'''
		
		#
		if (pll_type == None):
			self.poa = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
		else:
			raise ValueError("pll_type must be either None or 'POA'")
		#end
		
		# 
		name = 'SOLVOPT'
		category = 'Local Optimizer'
		def_opts = {
		'xtol':[float,1e-4],			# Variables Tolerance
		'ftol':[float,1e-6],			# Objective Tolerance
		'maxit':[int,15000],			# Maximum Number of Iterations
		'iprint':[int,1],     			# Output Level (-1 -> None, 0 -> Final, N - each Nth iter)
		'gtol':[float,1e-8],  			# Constraints Tolerance
		'spcdil':[float,2.5], 			# Space Dilation 
		'iout':[int,6],     			# Output Unit Number
		'ifile':[str,'SOLVOPT.out'],	# Output File Name
		}
		informs = {
		1 : 'Normal termination.', 
		-2 : 'Improper space dimension.',
		-3 : 'Objective equals infinity.',
		-4 : 'Gradient equals zero or infinity.',
		-5 : 'Objective equals infinity.',
		-6 : 'Gradient equals zero or infinity.',
		-7 : 'Objective function is unbounded.',
		-8 : 'Gradient zero at the point, but stopping criteria are not fulfilled.',
		-9 : 'Iterations limit exceeded.',
		-11 : 'Premature stop is possible. Try to re-run the routine from the obtained point.',
		-12 : 'Result may not provide the optimum. The function apparently has many extremum points.',
		-13 : 'Result may be inaccurate in the coordinates. The function is flat at the optimum.',
		-14 : 'Result may be inaccurate in a function value. The function is extremely steep at the optimum.',
		}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		FSQP Optimizer Class Initialization
		
		**Keyword arguments:**
		
		- pll_type -> STR: Parallel Implementation (None, 'POA'-Parallel Objective Analysis), *Default* = None
		
		Documentation last updated:  Feb. 16, 2010 - Peter W. Jansen
		'''
		
		#
		if (pll_type == None):
			self.poa = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
		else:
			raise ValueError("pll_type must be either None or 'POA'")
		#end
		
		#
		name = 'FSQP'
		category = 'Local Optimizer'
		def_opts = {
		'mode':[int,100],			# FSQP Mode (See Manual)
		'iprint':[int,2],			# Output Level (0 - None, 1 - Final, 2 - Major, 3 - Major Details)
		'miter':[int,500],			# Maximum Number of Iterations
		'bigbnd':[float,1e10],		# Plus Infinity Value
		'epstol':[float,1e-8],		# Convergence Tolerance
		'epseqn':[float,0],			# Equality Constraints Tolerance
		'iout':[int,6],     		# Output Unit Number
		'ifile':[str,'FSQP.out'],	# Output File Name
		}
		informs = {
		0 : 'Normal termination of execution',
		1 : 'User-provided initial guess is infeasible for linear constraints, unable to generate a point satisfying all these constraints',
		2 : 'User-provided initial guess is infeasible for nonlinear inequality constraints and linear constraints, unable to generate a point satisfying all these constraints',
		3 : 'The maximum number of iterations has been reached before a solution is obtained',
		4 : 'The line search fails to find a new iterate',
		5 : 'Failure of the QP solver in attempting to construct d0, a more robust QP solver may succeed',
		6 : 'Failure of the QP solver in attempting to construct d1, a more robust QP solver may succeed',
		7 : 'Input data are not consistent, check print out error messages',
		8 : 'Two consecutive iterates are numerically equivalent before a stopping criterion is satisfied',
		9 : 'One of the penalty parameters exceeded bigbnd, the algorithm is having trouble satisfying a nonlinear equality constraint',
		}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		SLSQP Optimizer Class Initialization
		
		**Keyword arguments:**
		
		- pll_type -> STR: Parallel Implementation (None, 'POA'-Parallel Objective Analysis), *Default* = None
		
		Documentation last updated:  Feb. 16, 2010 - Peter W. Jansen
		'''
		
		#
		if (pll_type == None):
			self.poa = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
		else:
			raise ValueError("pll_type must be either None or 'POA'")
		#end
		
		#
		name = 'SLSQP'
		category = 'Local Optimizer'
		def_opts = {
		# SLSQP Options
		'ACC':[float,1e-6],			# Convergence Accurancy
		'MAXIT':[int,50], 			# Maximum Iterations
		'IPRINT':[int,1],			# Output Level (<0 - None, 0 - Screen, 1 - File)
		'IOUT':[int,6],     		# Output Unit Number
		'IFILE':[str,'SLSQP.out'],	# Output File Name
		}
		informs = {
		-1 : "Gradient evaluation required (g & a)",
		0 : "Optimization terminated successfully.",
		1 : "Function evaluation required (f & c)",
		2 : "More equality constraints than independent variables",
		3 : "More than 3*n iterations in LSQ subproblem",
		4 : "Inequality constraints incompatible",
		5 : "Singular matrix E in LSQ subproblem",
		6 : "Singular matrix C in LSQ subproblem",
		7 : "Rank-deficient equality constraint subproblem HFTI",
		8 : "Positive directional derivative for linesearch",
		9 : "Iteration limit exceeded",
		}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		ALGENCAN Optimizer Class Initialization
		
		**Keyword arguments:**
		
		- pll_type -> STR: Parallel Implementation (None, 'POA'-Parallel Objective Analysis), *Default* = None
		
		Documentation last updated:  Feb. 16, 2010 - Peter W. Jansen
		'''
		
		#
		if (pll_type == None):
			self.poa = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
		else:
			raise ValueError("pll_type must be either None or 'POA'")
		#end
		
		#
		name = 'ALGENCAN'
		category = 'Local Optimizer'
		def_opts = {
		# ALGENCAN Options
		'epsfeas':[float,1.0e-8],		# Feasibility Convergence Accurancy
		'epsopt':[float,1.0e-8],		# Optimality Convergence Accurancy
		'efacc':[float,1.0e-4],			# Feasibility Level for Newton-KKT Acceleration
		'eoacc':[float,1.0e-4],			# Optimality Level for Newton-KKT Acceleration
		'checkder':[bool,False],		# Check Derivatives Flag
		'iprint':[int,10],				# Print Flag (0 - None, )
		'ifile':[str,'ALGENCAN.out'],	# Output File Name
		'ncomp':[int,6],				# Print Precision
		}
		informs = {
		0 : "Solution was found.",
		1 : "Stationary or infeasible point was found.",
		2 : "penalty parameter is too large infeasibile or badly scaled problem",
		3 : "Maximum of iterations reached.",
		}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		MMFD Optimizer Class Initialization
		
		**Keyword arguments:**
		
		- pll_type -> STR: Parallel Implementation (None, 'POA'-Parallel Objective Analysis), *Default* = None
		
		Documentation last updated:  Feb. 16, 2010 - Peter W. Jansen
		'''
		
		#
		if (pll_type == None):
			self.poa = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
		else:
			raise ValueError("pll_type must be either None or 'POA'")
		#end
		
		#
		name = 'MMFD'
		category = 'Local Optimizer'
		def_opts = {
		'IOPT':[int,0],           	# Feasible Directions Approach (0 - MMFD, 1 - MFD)
		'IONED':[int,0],          	# One-Dimensional Search Method (0,1,2,3)
		'CT':[float,-3e-2],       	# Constraint Tolerance
		'CTMIN':[float,4e-3],     	# Active Constraint Tolerance
		'DABOBJ':[float,1e-3],    	# Objective Absolute Tolerance (DABOBJ*abs(f(x)))
		'DELOBJ':[float,1e-3],    	# Objective Relative Tolerance
		'THETAZ':[float,1e-1],    	# Push-Off Factor
		'PMLT':[float,1e1],       	# Penalty multiplier for equality constraints
		'ITMAX':[int,4e2],        	# Maximum Number of Iterations
		'ITRMOP':[int,3],         	# consecutive Iterations Iterations for Convergence
		'IPRINT':[int,2],         	# Print Control (0 - None, 1 - Final, 2 - Iters)
		'IFILE':[str,'MMFD.out'],	# Output File Name
		}
		informs = {
		}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		MMA Optimizer Class Initialization
		
		**Keyword arguments:**
		
		- pll_type -> STR: Parallel Implementation (None, 'POA'-Parallel Objective Analysis), *Default* = None
		
		Documentation last updated:  Feb. 16, 2010 - Peter W. Jansen
		'''
		
		#
		if (pll_type == None):
			self.poa = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
		else:
			raise ValueError("pll_type must be either None or 'POA'")
		#end
		
		
		#
		name = 'MMA'
		category = 'Local Optimizer'
		def_opts = {
		# MMA Options
		'MAXIT':[int,1000],     	# Maximum Iterations
		'GEPS':[float,1e-6],    	# Dual Objective Gradient Tolerance
		'DABOBJ':[float,1e-6],  	# 
		'DELOBJ':[float,1e-6],  	# 
		'ITRM':[int,2],         	# 
		'IPRINT':[int,1],       	# Output Level (<0 - None, 0 - Screen, 1 - File)
		'IOUT':[int,6],         	# Output Unit Number
		'IFILE':[str,'MMA.out'],	# Output File Name
		}
		informs = {
		0 : 'The optimality conditions are satisfied.', 
		1 : 'The algorithm has been stopped after MAXIT iterations.',
		}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		NSGA2 Optimizer Class Initialization
		
		**Keyword arguments:**
		
		- pll_type -> STR: Parallel Implementation (None, 'POA'-Parallel Objective Analysis), *Default* = None
		
		Documentation last updated:  Feb. 16, 2010 - Peter W. Jansen
		'''
		
		#
		if (pll_type == None):
			self.poa = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
		else:
			raise ValueError("pll_type must be either None or 'POA'")
		#end
		
		#
		name = 'NSGA-II'
		category = 'Global Optimizer'
		def_opts = {
		'PopSize':[int,100],			# 
		'maxGen':[int,150],				# 
		'pCross_real':[float,0.6],		# 
		'pMut_real':[float,0.2],		# 
		'eta_c':[float,10],				# 
		'eta_m':[float,20],				# 
		'pCross_bin':[float,0],			# 
		'pMut_bin':[float,0],			# 
		'PrintOut':[int,1],				# Flag to Turn On Output to filename (0 - , 1 - , 2 - )
		'seed':[float,0],				# Random Number Seed (0 - Auto-Seed based on time clock)
		'xinit':[int,0],				# Use Initial Solution Flag (0 - random population, 1 - use given solution)
		}
		informs = {}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		COBYLA Optimizer Class Initialization
		
		**Keyword arguments:**
		
		- pll_type -> STR: Parallel Implementation (None, 'POA'-Parallel Objective Analysis), *Default* = None
		
		Documentation last updated:  Feb. 16, 2010 - Peter W. Jansen
		'''
		
		#
		if (pll_type == None):
			self.poa = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
		else:
			raise ValueError("pll_type must be either None or 'POA'")
		#end
		
		#
		name = 'COBYLA'
		category = 'Local Optimizer'
		def_opts = {
		'RHOBEG':[float,0.5],		# Initial Variables Change
		'RHOEND':[float,1.0e-6],	# Convergence Accurancy
		'IPRINT':[int,2],			# Print Flag (0 - None, 1 - Final, 2,3 - Iteration)
		'MAXFUN':[int,3500],     	# Maximum Iterations
		'IOUT':[int,6],     		# Output Unit Number
		'IFILE':[str,'COBYLA.out'],	# Output File Name
		}
		informs = {
		0: 'Normal return',
		1: 'Max. number of function evaluations reach',
		2: 'Rounding errors are becoming damaging',
		}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

	def __init__(self, *args, **kwargs):
		
		'''
		HSO Optimizer Class Initialization
		
		Documentation last updated:  October. 22, 2008 - Ruben E. Perez
		'''
		
		# 
		name = 'HSO'
		category = 'Global Optimizer'
		def_opts = {
		'hms':[int,10],				# Memory Size [4,10]
		'dbw':[float,0.01],			# 
		'hmcr':[float,0.96],		# 
		'par':[float,0.6],			# 
		'maxiter':[int,1e4],		# Maximum Number Iterations
		'printout':[int,0],			# Flag to Turn On Information Output
		'xinit':[int,0],			# Initial Position Flag (0 - no position, 1 - position given)
		'seed':[float,0],			# Random Number Seed (0 - Auto-Seed based on time clock)
		}
		informs = {}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		CONMIN Optimizer Class Initialization
		
		**Keyword arguments:**
		
		- pll_type -> STR: Parallel Implementation (None, 'POA'-Parallel Objective Analysis), *Default* = None
		
		Documentation last updated:  Feb. 16, 2010 - Peter W. Jansen
		'''
		
		#
		if (pll_type == None):
			self.poa = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
		else:
			raise ValueError("pll_type must be either None or 'POA'")
		#end
		
		#
		name = 'CONMIN'
		category = 'Local Optimizer'
		def_opts = {
		'ITMAX':[int,1e4],			# Maximum Number of Iterations
		'DELFUN':[float,1e-6],		# Objective Relative Tolerance
		'DABFUN':[float,1e-6],		# Objective Absolute Tolerance
		'ITRM':[int,2],				# 
		'NFEASCT':[int,20],			# 
		'IPRINT':[int,2],			# Print Control (0 - None, 1 - Final, 2,3,4,5 - Debug)
		'IOUT':[int,6],     		# Output Unit Number
		'IFILE':[str,'CONMIN.out'],	# Output File Name
		}
		informs = {
		}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		SDPEN Optimizer Class Initialization
		
		**Keyword arguments:**
		
		- pll_type -> STR: Parallel Implementation (None, 'POA'-Parallel Objective Analysis), *Default* = None
		
		Documentation last updated:  August. 09, 2012 - Ruben E. Perez
		'''
		
		#
		if (pll_type == None):
			self.poa = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
		else:
			raise ValueError("pll_type must be either None or 'POA'")
		#end
		
		#
		name = 'SDPEN'
		category = 'Local Optimizer'
		def_opts = {
		# SDPEN Options
		'alfa_stop':[float,1e-6],	# Convergence Tolerance
		'nf_max':[int,5000],		# Maximum Number of Function Evaluations
		'iprint':[int,0],			# Output Level (<0 - None, 0 - Final, 1 - Iters, 2 - Full)
		'iout':[int,6],     		# Output Unit Number
		'ifile':[str,'SDPEN.out'],	# Output File Name
		}
		informs = {
		1 : 'finished successfully',
		2 : 'maximum number of evaluations reached',
		}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

    def __init__(self, pll_type=None, *args, **kwargs):

        """
		CONMIN Optimizer Class Initialization
		
		**Keyword arguments:**
		
		- pll_type -> STR: Parallel Implementation (None, 'POA'-Parallel Objective Analysis), *Default* = None
		
		Documentation last updated:  Feb. 16, 2010 - Peter W. Jansen
		"""

        #
        if pll_type == None:
            self.poa = False
        elif pll_type.upper() == "POA":
            self.poa = True
        else:
            raise ValueError("pll_type must be either None or 'POA'")
            # end

            #
        name = "CONMIN"
        category = "Local Optimizer"
        def_opts = {
            "ITMAX": [int, 1e4],  # Maximum Number of Iterations
            "DELFUN": [float, 1e-6],  # Objective Relative Tolerance
            "DABFUN": [float, 1e-6],  # Objective Absolute Tolerance
            "ITRM": [int, 2],  #
            "NFEASCT": [int, 20],  #
            "IPRINT": [int, 2],  # Print Control (0 - None, 1 - Final, 2,3,4,5 - Debug)
            "IOUT": [int, 6],  # Output Unit Number
            "IFILE": [str, "CONMIN.out"],  # Output File Name
        }
        informs = {}
        Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		KSOPT Optimizer Class Initialization
		
		**Keyword arguments:**
		
		- pll_type -> STR: Parallel Implementation (None, 'POA'-Parallel Objective Analysis), *Default* = None
		
		Documentation last updated:  Feb. 16, 2010 - Peter W. Jansen
		'''
		
		#
		if (pll_type == None):
			self.poa = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
		else:
			raise ValueError("pll_type must be either None or 'POA'")
		#end
		
		#
		name = 'KSOPT'
		category = 'Local Optimizer'
		def_opts = {
		'ITMAX':[int,4e2],			# Maximum Number of Iterations
		'RDFUN':[float,1e-4],		# Objective Convergence Relative Tolerance
		'RHOMIN':[float,5.0],		# Initial KS multiplier
		'RHOMAX':[float,100.0],		# Final KS multiplier
		'IPRINT':[int,2],   		# Print Control (0 - None, 1 - Final, 2 - Iters)
		'IOUT':[int,6],    			# Output Unit Number
		'IFILE':[str,'KSOPT.out'],	# Output File Name
		}
		informs = {
		}
		Optimizer.__init__(self, name, category, def_opts, informs, *args, **kwargs)

	def __init__(self, pll_type=None, *args, **kwargs):
		
		'''
		MIDACO Optimizer Class Initialization
		
		**Keyword arguments:**
		
		- pll_type -> STR: Parallel Implementation (None, 'POA'-Parallel Objective Analysis), *Default* = None
		
		Documentation last updated:  Feb. 16, 2010 - Peter W. Jansen
		'''
		
		#
		if (pll_type == None):
			self.poa = False
			self.spm = False
		elif (pll_type.upper() == 'POA'):
			self.poa = True
			self.spm = False
		elif (pll_type.upper() == 'SPM'):
			self.poa = False
			self.spm = True
		else:
			raise ValueError("pll_type must be either None, 'POA' or 'SPM'")
		#end
		
		#
		name = 'MIDACO'
		category = 'Global Optimizer'
		def_opts = {
		# MIDACO Options
		'ACC':[float,0],       			    # Accuracy for constraint violation (0 - default)
		'ISEED':[int,0],            		# Seed for random number generator  (e.g. ISEED = 0,1,2,3,...)
		'FSTOP':[int,0],					# Objective Function Stopping Value (0 - disabled)
		'AUTOSTOP':[int,0],					# Automatic stopping criteria (0 - disable, 1 to 500 - from local to global)
		'ORACLE':[float,0],					# Oracle parameter for constrained problems (0 - Use internal default)
		'FOCUS':[int,0],					# Focus of MIDACO search process around best solution (0 - Use internal default)
		'ANTS':[int,0],						# Number of iterates (ants) per generation (0 - Use internal default)
		'KERNEL':[int,0],					# Size of the solution archive (0 - Use internal default)
		'CHARACTER':[int,0],				# Internal custom parameters (0 - Use internal default, 1 - IP problems, 2 - NLP problems, 3 - MINLP problems)
		'MAXEVAL':[int,10000],				# Maximum function evaluations
		'MAXTIME':[int,86400],				# Maximum time limit, in seconds
		'IPRINT':[int,1],					# Output Level (<0 - None, 0 - Screen, 1 - File(s))
		'PRINTEVAL':[int,1000],				# Print-Frequency for current best solution
		'IOUT1':[int,36],					# History output unit number
		'IOUT2':[int,37],					# Best solution output unit number
		'IFILE1':[str,'MIDACO_HIST.out'],	# History output file name
		'IFILE2':[str,'MIDACO_BEST.out'],	# Best output file name
		'LKEY':[str,'MIDACO_LIMITED_VERSION___[CREATIVE_COMMONS_BY-NC-ND_LICENSE]'],
		}
		informs = {
		1 : 'Feasible solution,   MIDACO was stopped by the user submitting ISTOP=1',
		2 : 'Infeasible solution, MIDACO was stopped by the user submitting ISTOP=1',
		3 : 'Feasible solution,   MIDACO stopped automatically using AUTOSTOP option',
		4 : 'Infeasible solution,   MIDACO stopped automatically using AUTOSTOP option',
		5 : 'Feasible solution,   MIDACO stopped automatically by FSTOP',
		51 : 'WARNING: Some X(i)  is greater/lower than +/- 1.0D+12 (try to avoid huge values!)',
		52 : 'WARNING: Some XL(i) is greater/lower than +/- 1.0D+12 (try to avoid huge values!)',
		53 : 'WARNING: Some XU(i) is greater/lower than +/- 1.0D+12 (try to avoid huge values!)',
		61 : 'WARNING: Some X(i)  should be discrete (e.g. 1.000) , but is continuous (e.g. 1.234)',
		62 : 'WARNING: Some XL(i) should be discrete (e.g. 1.000) , but is continuous (e.g. 1.234)',
		63 : 'WARNING: Some XU(i) should be discrete (e.g. 1.000) , but is continuous (e.g. 1.234)',
		71 : 'WARNING: Some XL(i) = XU(I) (fixed variable)',
		81 : 'WARNING: F(X) has value NaN for starting point X (sure your problem is correct?)',
		82 : 'WARNING: Some G(X) has value NaN for starting point X (sure your problem is correct?)',
		91 : 'WARNING: FSTOP is greater/lower than +/- 1.0D+8',
		92 : 'WARNING: ORACLE is greater/lower than +/- 1.0D+8',
		101 : 'ERROR: L    <= 0 or L > 1.0D+6',
		102 : 'ERROR: N    <= 0 or N > 1.0D+6',
		103 : 'ERROR: NINT <  0',
		104 : 'ERROR: NINT >  N',
		105 : 'ERROR: M    <  0 or M > 1.0D+6',
		106 : 'ERROR: ME   <  0',
		107 : 'ERROR: ME   >  M',
		201 : 'ERROR: some X(i)  has type NaN',
		202 : 'ERROR: some XL(i) has type NaN',
		203 : 'ERROR: some XU(i) has type NaN',
		204 : 'ERROR: some X(i) < XL(i)',
		205 : 'ERROR: some X(i) > XU(i)',
		206 : 'ERROR: some XL(i) > XU(i)',
		301 : 'ERROR: ACC < 0   or   ACC > 1.0D+6',
		302 : 'ERROR: ISEED < 0   or   ISEED > 1.0D+12',
		303 : 'ERROR: FSTOP greater/lower than +/- 1.0D+12',
		304 : 'ERROR: AUTOSTOP < 0   or   AUTOSTOP > 1.0D+6',
		305 : 'ERROR: ORACLE greater/lower than +/- 1.0D+12',
		306 : 'ERROR: |FOCUS| < 1   or   FOCUS > 1.0D+12',
		307 : 'ERROR: ANTS < 0   or   ANTS > 1.0D+8',
		308 : 'ERROR: KERNEL < 0   or   KERNEL > 100',
		309 : 'ERROR: ANTS < KERNEL',
		310 : 'ERROR: ANTS > 0 but KERNEL = 0',
		311 : 'ERROR: KERNEL > 0 but ANTS = 0',
		312 : 'ERROR: CHARACTER < 0   or   CHARACTER > 1000',
		313 : 'ERROR: some MIDACO parameters has type NaN',
		401 : 'ERROR: ISTOP < 0 or ISTOP > 1',
		501 : 'ERROR: Double precision work space size LRW is too small (see below LRW), RW must be at least of size LRW = 200*N+2*M+1000',
		601 : 'ERROR: Integer work space size LIW is too small (see below LIW), IW must be at least of size LIW = 2*N+L+100',
		701 : 'ERROR: Input check failed! MIDACO must be called initially with IFAIL = 0',
		801 : 'ERROR: L > LMAX (user must specifiy LMAX below in the MIDACO source code)',
		802 : 'ERROR: L*M+1 > LXM (user must specifiy LXM below in the MIDACO source code)',
		900 : 'ERROR: Invalid or corrupted LICENSE_KEY',
#.........这里部分代码省略.........

    def __init__(self, pll_type=None, *args, **kwargs):
        """IPOPT Optimizer Class Initialization.

        Documentation last updated:  Feb. 16, 2010 - Peter W. Jansen
        """

        name = "IPOPT"
        category = "Local Optimizer"
        def_opts = {
            # IPOPT Printing Options
            # Print Control (0 - None, 1 - Final,2,3,4,5 - Debug)
            "IPRINT": [int, 2],
            "IOUT": [int, 6],  # Output Unit Number
            "output_file": [str, "IPOPT.out"],  # Output File Name
            # Output options
            "print_level": [int, 5],  # Output verbosity level
            # Print all options set by the user
            "print_user_options": [str, "no"],
            # Switch to print all algorithmic options
            "print_options_documentation": [str, "no"],
            "output_file": [str, ""],  # File name of desired output file
            "file_print_level": [int, 5],  # Verbosity level for output file
            "option_file_name": [str, ""],  # File name of options file
            # Termination options
            "tol": [float, 1e-8],  # relative convergence tolerance
            "max_iter": [int, 3000],  # Maximum number of iterations
            "max_cpu_time": [float, 1e6],  # Maximum number of CPU seconds.
            # Desired threshold for the dual infeasibility
            "dual_inf_tol": [float, 1],
            # Desired threshold for the constraint violation
            "constr_viol_tol": [float, 1e-4],
            # Desired threshold for the complementarity conditions
            "compl_inf_tol": [float, 1e-4],
            # "Acceptable" convergence tolerance (relative)
            "acceptable_tol": [float, 1e-6],
            # Number of "acceptable" iterates before triggering termination
            "acceptable_iter": [int, 15],
            # "Acceptance" threshold for the constraint violation
            "acceptable_constr_viol_tol": [float, 1e-2],
            # "Acceptance" threshold for the dual infeasibility.
            "acceptable_dual_inf_tol": [float, 1e10],
            # "Acceptance" threshold for the complementarity conditions
            "acceptable_compl_inf_tol": [float, 1e-2],
            # "Acceptance" stopping criterion based on objective function change
            "acceptable_obj_change_tol": [float, 1e20],
            # Threshold for maximal value of primal iterates
            "diverging_iterates_tol": [float, 1e20],
            # NLP scaling options
            # Scaling factor for the objective function
            "obj_scaling_factor": [float, 1],
            # Select the technique use for scaling the NLP ('none',
            # 'user-scaling', 'gradient-based', 'equilibration-based')
            "nlp_scaling_method": [str, "gradient-based"],
            # Maximum gradient after NLP scaling
            "nlp_scaling_max_gradient": [float, 100],
            # Minimum value of gradient-based scaling values
            "nlp_scaling_min_value": [float, 1e-8],
            # NLP options
            # Factor for initial relaxation of the bounds
            "bound_relax_factor": [float, 1e-8],
            # Indicates whether final points should be projected into original
            # bounds
            "honor_original_bounds": [str, "yes"],
            # Indicates whether it is desired to check for Nan/Inf in derivative
            # matrices
            "check_derivatives_for_naninf": [str, "no"],
            # any bound less or equal this value will be considered -inf (i.e.
            # not lower bounded)
            "nlp_lower_bound_inf": [float, -1e19],
            # any bound greater or this value will be considered +inf (i.e. not
            # upper bounded)
            "nlp_upper_bound_inf": [float, 1e19],
            # Determines how fixed variables should be handled
            # ('make_parameter', 'make_constraint', 'relax_bounds')
            "fixed_variable_treatment": [str, "make_parameter"],
            # Indicates whether all equality constraints are linear
            "jac_c_constant": [str, "no"],
            # Indicates whether all inequality constraints are linear
            "jac_d_constant": [str, "no"],
            # Indicates whether the problem is a quadratic problem
            "hessian_constant": [str, "no"],
            # Initialization options
            # Desired minimum relative distance from the initial point to bound
            "bound_frac": [float, 0.01],
            # Desired minimum absolute distance from the initial point to bound
            "bound_push": [float, 0.01],
            # Desired minimum relative distance from the initial slack to bound
            "slack_bound_frac": [float, 0.01],
            # Desired minimum absolute distance from the initial slack to bound
            "slack_bound_push": [float, 0.01],
            # Initial value for the bound multipliers
            "bound_mult_init_val": [float, 1],
            # Maximum allowed least-square guess of constraint multipliers
            "constr_mult_init_max": [float, 1000],
            # Initialization method for bound multipliers ('constant',
            # 'mu_based')
            "bound_mult_init_method": [str, "constant"],
            # Barrier parameter options
            # Indicates if we want to do Mehrotra's algorithm
            "mehrotra_algorithm": [str, "no"],
#.........这里部分代码省略.........

    def __init__(self, pll_type=None, *args, **kwargs):

        """
		SNOPT Optimizer Class Initialization
		
		**Keyword arguments:**
		
		- pll_type -> STR: Parallel Implementation (None, 'POA'-Parallel Objective Analysis), *Default* = None
		
		Documentation last updated:  Feb. 16, 2010 - Peter W. Jansen
		"""

        #
        if pll_type == None:
            self.poa = False
        elif pll_type.upper() == "POA":
            self.poa = True
        else:
            raise ValueError("pll_type must be either None or 'POA'")
            # end

            #
        name = "SNOPT"
        category = "Local Optimizer"
        def_opts = {
            # SNOPT Printing Options
            "Major print level": [int, 1],  # Majors Print (1 - line major iteration log)
            "Minor print level": [int, 1],  # Minors Print (1 - line minor iteration log)
            "Print file": [str, "SNOPT_print.out"],  # Print File Name (specified by subroutine snInit)
            "iPrint": [int, 18],  # Print File Output Unit (override internally in snopt?)
            "Summary file": [str, "SNOPT_summary.out"],  # Summary File Name (specified by subroutine snInit)
            "iSumm": [int, 19],  # Summary File Output Unit (override internally in snopt?)
            "Print frequency": [int, 100],  # Minors Log Frequency on Print File
            "Summary frequency": [int, 100],  # Minors Log Frequency on Summary File
            "Solution": [str, "Yes"],  # Print Solution on the Print File
            "Suppress options listing": [type(None), None],  # (options are normally listed)
            "System information": [str, "No"],  # Print System Information on the Print File
            # SNOPT Problem Specification Options
            "Problem Type": [
                str,
                "Minimize",
            ],  # ('Maximize': alternative over Minimize, 'Feasible point': alternative over Minimize or Maximize)
            "Objective row": [int, 1],  # (has precedence over ObjRow (snOptA))
            "Infinite bound": [float, 1.0e20],  # Infinite Bound Value
            # SNOPT Convergence Tolerances Options
            "Major feasibility tolerance": [float, 1.0e-6],  # Target Nonlinear Constraint Violation
            "Major optimality tolerance": [float, 1.0e-6],  # Target Complementarity Gap
            "Minor feasibility tolerance": [float, 1.0e-6],  # For Satisfying the QP Bounds