Research Article

Lévy-Flight Moth-Flame Algorithm for Function Optimization and Engineering Design Problems

Table 5

Results of multimodal benchmark functions.

Benchmark functionResultAlgorithm
ABCBAGGSADAPSOGSAMFOLMFO

()Best575.16081363.3691563.422730.92416922.18321769.4410
Worst709.0191810.4651867.6161890.95451476.2722125.690
Mean653.93161630.7681752.8381367.38991231.5781951.4260
Std36.46425100.720381.02372280.04565117.190379.018270

()Best12.5425819.2052710.529078.66661219.2330919.921318.88E − 16
Worst14.804519.956411.8904214.7543619.9667720.018978.88E − 16
Mean13.8211319.7361411.2102411.8890919.6971919.954338.88E − 16
Std0.5541620.2601540.3836541.4537040.311070.0193960

()Best142.25484290.404137.628561.43179654.93881229.640
Worst316.0365283.435221.9276600.1511631.5472048.4540
Mean225.31034997.492171.5404224.07441251.6951543.040
Std51.75854184.813516.55978104.4042204.6207197.60010

()Best73933983.86E + 0828.177741662.38470433987.03E + 088.88E − 16
Worst1.67E + 086.85E + 0877685.77231720332.05E + 091.92E + 098.88E − 16
Mean587310935.59E + 0811043.7625160127.28E + 081.3E + 098.88E − 16
Std371775918191407920355.0346412045.15E + 083.01E + 080

()Best194649681.25E + 09646700.52620391266665171.68E + 0919.4713
Worst3.4E + 082.09E + 0933190031.83E + 082.47E + 094.04E + 0919.79025
Mean1.53E + 081.72E + 091684844271830539.73E + 082.62E + 0919.62771
Std822965651.89E + 08724732.9347599747.07E + 085.46E + 080.072549

()Best47.2870690.66079103.328315.3869458.10706129.05951.1E − 125
Worst62.0743168.7346139.0044197.7987107.5327216.95781.1E − 103
Mean53.55967119.4603118.7804116.361980.96392171.30743.9E − 105
Std3.43808219.186618.5258943.541112.9369523.186542E − 104

()Best5373.3544315.808461.66981142.9244411.2445741.4175E − 168
Worst6101.20410167.763585.3454914.8449429.56811241.252.6E − 117
Mean5810.78952831327.2343451.426873.2228566.5258.6E − 119
Std207.77041065.437640.87661139.3561451.2941527.9924.7E − 118