result3.asv

用matlab实现模拟退火算法,是学习模拟退火算法的有用工具

% the first running result
Best_tour_length = 1925;


besttour =[    1     9    15     4     3     8    14    10    16     5     6    11    13     7  2    12    -1];

Temperature_of_best_tour_length =17.3385;

Solution_count = 629;

Search_stop_temperature = 4.0184;

Elapsed_time = 0.2030;

Solutions_generated =10272;

temp1 =[   30.0000   30.0000   29.1000   27.3802   27.3802   25.7620   24.9892   24.2395 ...
   22.1227   22.1227   21.4590   21.4590   20.8153   19.5851   18.4276   17.8748 ...
   17.3385   16.3138   15.8244   15.3497   14.8892   14.4425   14.0092   13.5890 ...
   13.1813   12.4023   12.4023   12.0302   11.3192   11.3192   10.9796   10.6503 ...
   10.3308    9.7202    9.7202    9.4286    9.1457    8.8714    8.6052    8.3471 ...
    8.0967    7.8538    7.6181    7.3896    7.1679    6.9529    6.7443    6.5420 ...
    6.3457    6.1553    5.9707    5.7916    5.6178    5.4493    5.2858    5.1272 ...
    4.9734  4.8242    4.6795    4.5391    4.4029    4.2708    4.1427    4.0184  ];%
lenn1=[2602        2551        2526        2540        2489        2227        2148 ...
        2108        2222        2195        2121        2081        2075        2031 ...
        2016        1976        1925        1925        1925        1929        1929 ...
       1929        1929        1929        1929        1969        1929        1929 ...
        2011        1929        1929        1929        1929        1933        1929 ...
        1929        1929        1929        1929        1929        1929        1929 ...
        1929        1929        1929        1929        1929        1929        1929 ...
       1929        1929        1929        1929        1929        1929        1929 ...
        1929 1929 1929 1929 1929 1929 1929 1929];
    
    solnn1 =[  1          14          32          46          59          78          92 ...
         118         199         226         263         289         367         446 ...
         523         549         629         806        1074        1155        1517 ...
        1697        1877        2057        2237        2417        2443        2623 ...
        2803        2850        3030        3210        3481        3598        3687 ...
        3867        4047        4318        4498        4678        4858        5220 ...
        5400        5580        5760        5940        6120        6300        6662 ...
        6933        7204        7475        7837        8017        8197        8377 ...
        8557        8828        9099        9279        9459        9639        9819 ...
        9999];
    
    % the second data
    
    Best_tour_length = 1775;


besttour =[ 1    11    10     6     8     3     2    12     7     4    14    13    16     9    15     5    -1];




Temperature_of_best_tour_length = 7.6181;


Solution_count = 943;


Search_stop_temperature =5.7916;


Elapsed_time =0.3440;


Solutions_generated =2176;


temp2 =[   30.0000   30.0000   28.2270   27.3802   26.5588   24.9892   24.2395   22.8069 ...
   22.1227   22.1227   19.5851   19.5851   18.4276   17.8748   17.3385   16.8184 ...
   16.3138   15.3497   14.8892   14.4425   13.5890   13.1813   12.7859   12.0302 ...
   10.9796   10.9796   10.0208   10.0208    9.7202    9.4286    8.8714    8.8714 ...
    8.3471    7.8538    7.6181    7.1679    6.7443    6.5420    6.5420    6.1553 ...
    6.1553    5.7916    5.7916]


lenn2 =[     2750        2742        2742        2676        2668        2624        2609 ...
        2664        2689        2602        2636        2612        2614        2489 ...
        2444        2425        2401        2336        2312        2304        2168 ...
        2166        2148        2135        2139        2130        2156        2132 ...
        2057        2028        2036        2005        1796        1785        1775 ...
        1775        1806        1806        1788        1785        1775        1785 ...
        1775];


solnn2 =[

           1           2           5           9          35          52          66 ...
          85         112         119         165         166         175         195 ...
         200         212         213         250         251         291         304 ...
         307         308         310         392         431         470         471 ...
         512         583         665         702         786         872         943 ...
        1287        1303        1397        1398        1490        1561        1832 ...
        1903];
    
  % the third running result
  Best_tour_length = 1663;

       


besttour =[     1     6    10    14     5     3     2     7    16     4    12     8    15    13    11  9    -1];
     


Temperature_of_best_tour_length = 30;

Solution_count =72;


Search_stop_temperature =24.2395;


Elapsed_time = 0.0310;


Solutions_generated = 926;




temp3 =[ 30.0000   30.0000   27.3802   26.5588   26.5588   24.9892   24.9892   24.2395];

  


lenn3 =[1692        1663        1793        1716        1663        1716        1663 1663];
        


solnn3 =[ 1    72   234   236   237   399   400   653];

 plot(temp1,lenn1, '-*',temp2,lenn2, '-o',temp3,lenn3,'-.')
 title('Simulated Annealing w/ 2-Opt local search')
		xlabel('Temperature (not scaled)')
		ylabel('Tour Lengths/Costs')
		grid
figure, 
 plot(temp1,solnn1,'-^