Differential Evolution with Novel Mutation and Adaptive Crossover Strategies for Solving Large Scale Global Optimization Problems

Algorithm 2

Description of ANDE algorithm.

() Begin

() 

() Create a random initial population ,

    set the Learning Period (LP) = 10% GEN, set the Max_failure_counter = 20.

    For each , set the failure_counter_list = 0, set the CR_Flag_List = 0,

    For each CR values in the list, set the CR_Ratio_List = 0 ,

() Evaluate

() For   to GEN Do

()  For   to NP Do

()   Select randomly

()   

()   Compute the (crossover rate) according to Procedure 1.

()      For   to   Do

()       If (  or  ) Then

()        If () Then (Use New Triangular Mutation Scheme)

() Determine the tournament , and based on ,

    (three randomly selected vectors) and compute according to eq. (2).

()

()        Else

()         

            End If

()       Else

()        

()       End If

()      End For

()      If  ()  Then

()        , ()

          If  ()  Then

          , ()

          End

          CR_Flag_List = 1

           The relative change improvement ratio (RCIR) is updated

()        CR_Ratio_List = CR_Ratio_List + ).

          Else

()        

          CR_Flag_List = 0

()      End If

()   End For

()  

() End For

() End