Prediction of Pile Bearing Capacity Using Opposition-Based Differential Flower Pollination-Optimized Least Squares Support Vector Regression (ODFP-LSSVR)

Algorithm 1

The ODFP optimization algorithm.

	Randomly initialize a population Pop =
	// PS denotes the number of population member
	Define LB and UB
	// LB and UB are lower and upper boundaries, respectively
	Define the objective cost function CF
	Calculate the objective cost function of the population: PopF
	Specify the probability of population jumping Jr
	Define the maximum number of function calls: MNFC
	For i = 1 : PS
	, d = 1, 2, …, D
	//  D is the number of searched parameters
	//  Xi,op denotes the opposition of Xi
	Compute CF(Xop)
	If (CF(Xop) < CF(X))
	Pop [X] = Pop [Xop]
	PopF[X] = PopF[Xop]
	End If
	End For
	Identify the best solution Xbest
	Identify the best cost function CF(Xbest)
	Count = 0 // counting number of function calls
	while Count < MNFC
	For i = 1 : PS
	Generate r∼U(0,1)
	If r < p // Perform global search
	Generate a trial solution via
	Else // Perform local search
	Perform mutation operation
	Perform crossover operation
	End If
	Update Xbest
	End For
	Generate θ∼U(0,1)
	If θ < Jr
	For i = 1 : PS
	, d = 1, 2, …, D
	Compute CF(Xop)
	If (CF(Xop) < CF(X))
	Pop [X] = Pop [Xop]
	PopF[X] = PopF[Xop]
	End If
	End For
	End If
	Update Count
	Update Xbest
	Update CF(Xbest)
	End For
	Return Xbest