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📄 pso_result.txt

📁 matlab编写的经典pso算法
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   78.00     33.00     27.07     45.00     44.9691  -31025.54 
u=100
   77.7838   37.9075   26.3666   45.1263   26.9861  -31736
   77.7838   41.9294   26.3666   45.1263   26.9861  -31736
   77.7838   36.3973   26.3666   45.1263   26.9861
   77.7838   40.3089   26.3666   45.1263   26.9861
   77.7838   40.0007   26.3666   45.1263   26.9861
   77.7838   37.9236   26.3666   45.1263   26.9861

u=1000
   77.9720   33.5243   26.9551   45.0132   43.3049  -31101
   77.9720   33.2809   26.9551   45.0132   43.3049
   77.9720   33.5430   26.9551   45.0132   43.3049
   77.9720   33.5074   26.9551   45.0132   43.3049

u=100000
   77.9997   32.9998   27.0621   45.0002   44.9697  -31027
   77.9997   32.9998   27.0621   45.0002   44.9697  -31027
   77.9997   32.9998   27.0621   45.0002   44.9697  -31027
   77.9997   32.9998   27.0621   45.0002   44.9697
   77.9997   32.9998   27.0621   45.0002   44.9697
   77.9997   32.9998   27.0621   45.0002   44.9697
  77.99973237396117  32.99977456277399  27.06212859865528  45.00016017296107  44.96970924971482 -31026.84102319030
  77.99973237429269  32.99977456022766  27.06212859561232  45.00016017246236  44.96970927144667 -31026.84102319030
  77.99973237468629  32.99977456412795  27.06212859611432  45.00016017139627  44.96970927285977 -31026.84102319030

u=100000
maxIteration=100

  78.38206286467808  33.29732025618596  27.40255051286531  44.84229807830314  43.86735291089446 -30968.68075786036
  78.01305174633117  34.40689642029032  29.81379688263448  44.81513478813780  37.36456630503020 -3.068441549126633e+004

SwarmSize=100;
MaxIteration=300;

 for i=1:10
pso_syber
end



  77.99987081246373  33.01249531539552  27.08074592659001  45.00028628058003  44.91371485883698 -3.102532122199590e+004
  77.99979294828657  33.00473908830379  27.06655781929394  45.00004661079613  44.95971510412193 -3.102657757926162e+004
  77.99973148018981  32.99977396454122  27.06213135168962  45.00016161250886  44.96970109983966 -3.102684102265574e+004
  77.99985103903561  33.00424697683487  27.09291642923421  44.99970677530900  44.87869025605941 -3.102427241765897e+004
  78.00044216467569  33.00335661985333  27.06727848212830  44.99872834158935  44.96021589732082 -3.102648084494985e+004
  77.99977092300024  33.00075072205855  27.06495055734997  45.00032276593854  44.96451824703340 -3.102672994363510e+004
  78.00045424813798  33.00880571018575  27.07534038028549  44.99888151933733  44.93150860058400 -3.102574538436880e+004
  77.99894642025745  33.11458634261406  27.11670276678421  45.00005811057060  44.79285024124623 -3.102132644262395e+004
  77.99942149636810  33.00165209957768  27.06279941258334  44.99990164271945  44.96667998201994 -3.102674395413747e+004
  77.99975240729414  32.99974992658846  27.06201525380631  45.00013564123570  44.96983496964458 -3.102684073260136e+004

SwarmSize=100;
MaxIteration=1000;

 for i=1:10
pso_syber
end
  77.99987081246373  33.01249531539552  27.08074592659001  45.00028628058003  44.91371485883698

fxmin

fxmin =

   -3.102532122199590e+004


Current_Iteration =

   301

xmin=

xmin =

  77.99979294828657  33.00473908830379  27.06655781929394  45.00004661079613  44.95971510412193

fxmin

fxmin =

   -3.102657757926162e+004


Current_Iteration =

   301

xmin=

xmin =

  77.99973148018981  32.99977396454122  27.06213135168962  45.00016161250886  44.96970109983966

fxmin

fxmin =

   -3.102684102265574e+004


Current_Iteration =

   301

xmin=

xmin =

  77.99985103903561  33.00424697683487  27.09291642923421  44.99970677530900  44.87869025605941

fxmin

fxmin =

   -3.102427241765897e+004


Current_Iteration =

   301

xmin=

xmin =

  78.00044216467569  33.00335661985333  27.06727848212830  44.99872834158935  44.96021589732082

fxmin

fxmin =

   -3.102648084494985e+004


Current_Iteration =

   301

xmin=

xmin =

  77.99977092300024  33.00075072205855  27.06495055734997  45.00032276593854  44.96451824703340

fxmin

fxmin =

   -3.102672994363510e+004


Current_Iteration =

   301

xmin=

xmin =

  78.00045424813798  33.00880571018575  27.07534038028549  44.99888151933733  44.93150860058400

fxmin

fxmin =

   -3.102574538436880e+004


Current_Iteration =

   301

xmin=

xmin =

  77.99894642025745  33.11458634261406  27.11670276678421  45.00005811057060  44.79285024124623

fxmin

fxmin =

   -3.102132644262395e+004


Current_Iteration =

   301

xmin=

xmin =

  77.99942149636810  33.00165209957768  27.06279941258334  44.99990164271945  44.96667998201994

fxmin

fxmin =

   -3.102674395413747e+004


Current_Iteration =

   301

xmin=

xmin =

  77.99975240729414  32.99974992658846  27.06201525380631  45.00013564123570  44.96983496964458

fxmin

fxmin =

   -3.102684073260136e+004


Current_Iteration =

   301

>>  for i=1:10
pso_syber
end

  77.99973236968083  32.99977456003147  27.06212860229375  45.00016017367457  44.96970927331390  3.102684102319030e+004
  77.99973236970345  32.99977456431788  27.06212859516466  45.00016017312405  44.96970928632495  3.102684102319030e+004
  77.99973237260454  32.99977456282692  27.06212860254068  45.00016017218895  44.96970925805685 -3.102684102319030e+004
  77.99973237319462  32.99977456318929  27.06212860218437  45.00016017327523  44.96970927311183 -3.102684102319030e+004
  77.99973237420026  32.99977456305633  27.06212859331541  45.00016017234318  44.96970926571469 -3.102684102319030e+004
  77.99973237091876  32.99977456100985  27.06212858922718  45.00016017502603  44.96970928722715 -3.102684102319030e+004
  77.99973237099866  32.99977456076288  27.06212859960900  45.00016017289178  44.96970926560267 -3.102684102319030e+004
  77.99973237262324  32.99977456080322  27.06212859941609  45.00016017396330  44.96970927036301 -3.102684102319030e+004
  77.99973237216503  32.99977455986902  27.06212859446789  45.00016017152325  44.96970929445914 -3.102684102319030e+004
  77.99973237043956  32.99977456172062  27.06212859139539  45.00016017175679  44.96970929324894 -3.102684102319030e+004
精确到7-8位

77.999732 32.999775 27.062129 45.000160 44.969709 -31026.841023
77.999732 32.999775 27.062129 45.000160 44.969709 -31026.841023
77.999732 32.999775 27.062129 45.000160 44.969709 -31026.841023
77.999732 32.999775 27.062129 45.000160 44.969709 -31026.841023
77.999732 32.999775 27.062129 45.000160 44.969709 -31026.841023
77.999732 32.999775 27.062129 45.000160 44.969709 -31026.841023
77.999732 32.999775 27.062129 45.000160 44.969709 -31026.841023
77.999732 32.999775 27.062129 45.000160 44.969709 -31026.841023
77.999732 32.999775 27.062129 45.000160 44.969709 -31026.841023
77.999732 32.999775 27.062129 45.000160 44.969709 -31026.841023

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