Simulation Study of Swarm Intelligence Based on Life Evolution Behavior

Algorithm 1

Quasi-Newton local search.

//norm denotes computing Euclidean distance.

(1) Begin

(2) Choose    based on -means clustering

(3) For  i = 1 : m

(4)  Fminunc(, )//Quasi-Newton local search

(5)  Get

(6)  If fitness() < min()

(7)   Localbest = .

(8)  EndIf

(9) EndFor

(10)    are generated

(11) For  

(12)   For  

(13)   Compute = norm()

(14)   Endfor

(15) Endfor

(16) Choose    from    in terms of

(17)

(18) End (stopping criteria)