Research Article

Wind Farm Layout Optimization Based on Dynamic Opposite Learning-Enhanced Sparrow Search Algorithm

Table 7

Comparison of running rates of different algorithms.

Running time (s)PSOJayacfwPSOcfPSONTLBODEETLBOSSADOLSSA

wt124.0423.714139.0246.4345.3220.4245.351.6110.61
wt224.9221.57162.5346.1744.4918.8445.761.5011.99
wt322.6021.40102.9243.4644.6618.5945.782.8810.58
wt422.8122.9873.9945.5444.2920.8648.701.4710.48
wt523.8821.8957.8644.4746.3019.0643.174.3511.29
wt646.5741.65172.5793.9587.1651.8485.427.6617.96
wt722.3523.41162.5545.8045.0318.8244.701.6211.05
wt820.9325.85143.0943.6644.6518.7043.133.7710.61
wt925.0422.81154.2945.2847.6418.6744.591.5210.59
wt1022.7922.27106.6246.2844.7318.7645.801.7211.11
wt1123.2123.5390.4246.4845.7519.4045.321.8311.08
wt1225.8125.0566.0754.0351.8924.2153.542.6712.17
wt1325.9925.0169.8155.0551.5224.2051.492.6111.86
wt1421.8822.35142.0444.7545.1517.3446.381.5610.39
wt1522.1321.75149.1644.4744.3817.7643.671.5711.01
wt1621.9422.1854.1346.9543.8617.9844.161.6010.36
HF152.4152.53287.19101.99100.5340.02100.253.6124.87
HF249.6649.13208.75103.70101.1740.96102.294.2323.81
HF360.1556.73236.39120.81116.6553.04115.995.8327.69
HF450.4749.78167.00104.3098.6938.57100.454.2723.09
HF551.3249.89216.66103.07100.6440.71101.743.5225.67
HF652.4253.72175.70110.14103.6541.97104.403.5023.46
Case 117359.339088.3617632.0227698.2418927.3623917.2221054.653584.462894.15
Case 222280.7212236.2716910.4933490.5225678.7524227.2920365.824355.442799.29
Case 310907.018077.9812479.3520951.0315711.3215522.3318307.372657.812562.42
Case 423422.6618635.6348127.9948025.3632596.3340159.2549128.378913.257639.36
Case 519104.8418741.1249839.6649589.8336847.6437018.0444042.776374.576201.06
Case 626273.1219359.7649359.9150526.0737786.6339279.5046214.078175.306507.15
Average time4287.893100.527053.158275.646033.796454.447161.251218.781033.40