Mathematical Problems in Engineering / 2020 / Article / Tab 6 / Research Article
Optimizing High-Dimensional Functions with an Efficient Particle Swarm Optimization Algorithm Table 6 The performance of PSOTS, the traditional PSO algorithm (PSO), the AsynLnPSO algorithm (APSO), the LinWPSO algorithm (LPSO), GPSO, CCAS, and VPSO on 9 functions in the benchmark dataset when the dimensionality is 1000.
Function Ackley Cigar Griewank Rastrigin Rosenbrock Noncon-rast Sphere Tablet Ellipse PSOTS Best 0.551 58336.090 0.013 1049.727 1583.446 1036.363 5.691 7.021 4561.731 Mean 0.654 66590.620 0.017 1206.143 1695.683 1224.481 6.641 8.866 6034.033 STD 0.066 8530.537 0.003 108.670 80.271 122.191 0.872 1.767 1089.670 PSO Best 0.883 101581.400 0.022 1856.572 2091.178 2176.873 10.588 12.760 8299.737 Mean 0.959 119206.001 0.030 3752.308 2202.001 7460.916 12.395 13.751 9268.079 STD 0.052 10297.240 0.004 2558.991 100.112 2777.144 1.500 1.345 1063.982 APSO Best 0.903 114610.100 0.019 2051.027 2102.363 2168.402 11.284 12.864 8528.893 Mean 0.976 126324.800 0.029 3395.736 2268.229 8025.089 12.318 16.518 10217.47 STD 0.044 5550.201 0.005 2225.827 119.697 2069.662 0.623 1.783 1131.887 LPSO Best 0.840 96167.060 0.022 1844.305 1959.617 8590.621 9.892 12.613 6859.916 Mean 0.864 105884.300 0.028 4760.095 2053.572 8861.428 10.804 15.071 8193.032 STD 0.029 7576.268 0.003 3024.811 75.338 135.469 0.814 1.484 916.128 GPSO Best 0.671 63817.053 0.018 1630.047 2305.846 1779.136 6.781 9.207 6002.318 Mean 0.827 84967.952 0.031 4267.252 2552.418 2407.985 8.051 13.506 8425.598 STD 0.052 10801.034 0.003 1479.187 85.12814 132.854 0.833 2.204 1820.534 CCAS Best 0.676 58706.483 0.013 1363.015 1847.001 1681.628 6.632 9.931 6127.127 Mean 0.820 82986.121 0.027 4717.921 2528.346 2605.835 7.767 15.745 7943.676 STD 0.092 9234.537 0.007 843.3347 113.5974 1637.341 0.825 1.039 755.427 VPSO Best 0.657 57804.753 0.014 1330.581 1915.181 1661.348 6.568 8.420 5118.947 Mean 0.741 85502.262 0.029 3187.537 2146.646 2969.365 7.753 12.515 7077.191 STD 0.027 8076.055 0.008 676.163 93.69879 1248.472 0.848 1.956 672.217