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Test Data (1304_200)
magnific0 edited this page Feb 25, 2014
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1 revision
Trials: 200 - Population size: 200 - Generations: 500
Testing problem: Schwefel, Dimension: 10
With Population Size: 200
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 9.09494701773e-11
Mean: 260.631908637
Std: 115.943791588
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: 0.0
Mean: 1.18438334614
Std: 11.7844655013
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: 0.000532741532879
Mean: 0.00406424299774
Std: 0.00219265845394
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: 0.0
Mean: 5.91171556152e-14
Std: 2.24213964168e-13
Algorithm name: DE - 1220 - gen:500 self_adaptation:1 variants:[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] memory:0 ftol:1e-30 xtol:1e-30
Best: 2.78305378743e-10
Mean: 1.0586963981e-08
Std: 1.91273413954e-08
Algorithm name: Simulated Annealing (Corana's) - iter:100000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 0.0372645908601
Mean: 340.564504011
Std: 153.675177527
Algorithm name: Improved Harmony Search - iter:100000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: 1.35631207741e-05
Mean: 3.55271318085e-05
Std: 9.0401393391e-06
Algorithm name: A Simple Genetic Algorithm - gen:500 CR:0.95 M:0.02 elitism:1 mutation:GAUSSIAN (0.1) selection:ROULETTE crossover:EXPONENTIAL
Best: 0.706053608593
Mean: 222.745039481
Std: 147.409426062
Algorithm name: CMAES - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-30 xtol:1e-30 memory:0
Best: 1287.67864998
Mean: 1864.54688095
Std: 163.372505843
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: 0.000201731157176
Mean: 3.7089764187
Std: 17.5696521827
Testing problem: Rastrigin, Dimension: 10
With Population Size: 200
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 5.56923396289e-11
Mean: 2.73809223196
Std: 0.969941129513
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: 0.0
Mean: 0.0248739764273
Std: 0.155337933001
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: 2.14708944162
Mean: 4.41432114393
Std: 0.976017598301
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: 0.0
Mean: 1.7763568394e-15
Std: 6.50253370002e-15
Algorithm name: DE - 1220 - gen:500 self_adaptation:1 variants:[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] memory:0 ftol:1e-30 xtol:1e-30
Best: 0.0
Mean: 0.0
Std: 0.0
Algorithm name: Simulated Annealing (Corana's) - iter:100000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 0.071960276868
Mean: 5.44027732023
Std: 2.34290895752
Algorithm name: Improved Harmony Search - iter:100000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: 1.22950662274e-06
Mean: 5.91773239023e-06
Std: 1.47515207112e-06
Algorithm name: A Simple Genetic Algorithm - gen:500 CR:0.95 M:0.02 elitism:1 mutation:GAUSSIAN (0.1) selection:ROULETTE crossover:EXPONENTIAL
Best: 0.0566846441254
Mean: 0.325021907027
Std: 0.196026376901
Algorithm name: CMAES - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-30 xtol:1e-30 memory:0
Best: 0.0
Mean: 0.547227481401
Std: 0.749528485518
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: 2.91284626996e-06
Mean: 0.000234929736914
Std: 0.000373941282077
Testing problem: Rosenbrock, Dimension: 10
With Population Size: 200
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 0.000213826175189
Mean: 1.69260072143
Std: 1.57339765127
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: 4.62724373706
Mean: 6.06603085877
Std: 0.385909607242
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: 0.428552744359
Mean: 0.906020815366
Std: 0.207839304191
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: 0.0290259256466
Mean: 1.55171816039
Std: 0.95495961894
Algorithm name: DE - 1220 - gen:500 self_adaptation:1 variants:[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] memory:0 ftol:1e-30 xtol:1e-30
Best: 3.64507878861
Mean: 5.16595506596
Std: 0.355826831427
Algorithm name: Simulated Annealing (Corana's) - iter:100000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 0.00963768678196
Mean: 0.764593149924
Std: 1.50918132854
Algorithm name: Improved Harmony Search - iter:100000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: 6.56753905851
Mean: 7.37980967973
Std: 0.244824276187
Algorithm name: A Simple Genetic Algorithm - gen:500 CR:0.95 M:0.02 elitism:1 mutation:GAUSSIAN (0.1) selection:ROULETTE crossover:EXPONENTIAL
Best: 6.91508006068
Mean: 34.1775700906
Std: 30.5421359611
Algorithm name: CMAES - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-30 xtol:1e-30 memory:0
Best: 1.98455956598e-26
Mean: 2.91229165782e-23
Std: 1.09376145956e-22
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: 0.0566545231597
Mean: 0.318296125386
Std: 0.149060505142
Testing problem: Ackley, Dimension: 10
With Population Size: 200
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 1.74799521524e-08
Mean: 9.2099266773e-08
Std: 4.38161411478e-08
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: 4.4408920985e-16
Mean: 3.8546943415e-15
Std: 6.96186857221e-16
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: 0.000230896828132
Mean: 0.000448791072642
Std: 8.94773256919e-05
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: 1.96810123754e-10
Mean: 5.60238664349e-10
Std: 1.63155779678e-10
Algorithm name: DE - 1220 - gen:500 self_adaptation:1 variants:[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] memory:0 ftol:1e-30 xtol:1e-30
Best: 3.99680288865e-15
Mean: 1.1525003174e-12
Std: 2.67001231832e-12
Algorithm name: Simulated Annealing (Corana's) - iter:100000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 0.0243472523538
Mean: 0.0839679759625
Std: 0.0310362376931
Algorithm name: Improved Harmony Search - iter:100000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: 0.000492696807527
Mean: 0.000956076344098
Std: 0.000125342941196
Algorithm name: A Simple Genetic Algorithm - gen:500 CR:0.95 M:0.02 elitism:1 mutation:GAUSSIAN (0.1) selection:ROULETTE crossover:EXPONENTIAL
Best: 0.0643978541588
Mean: 0.282011222921
Std: 0.117102053669
Algorithm name: CMAES - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-30 xtol:1e-30 memory:0
Best: 3.99680288865e-15
Mean: 1.07647224468e-14
Std: 9.14502937029e-15
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: 4.32057794133e-05
Mean: 0.000184356230802
Std: 7.41705562621e-05
Testing problem: Griewank, Dimension: 10
With Population Size: 200
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 5.5633275764e-13
Mean: 0.0157227828632
Std: 0.00933612601497
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: 0.0
Mean: 0.00166328514667
Std: 0.00379218988797
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: 0.0847918769646
Mean: 0.182197248938
Std: 0.0336608121709
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: 2.96400080035e-08
Mean: 0.000103268327058
Std: 0.000183893975325
Algorithm name: DE - 1220 - gen:500 self_adaptation:1 variants:[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] memory:0 ftol:1e-30 xtol:1e-30
Best: 0.0
Mean: 9.81988101945e-12
Std: 8.66626373236e-11
Algorithm name: Simulated Annealing (Corana's) - iter:100000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 0.0978125145981
Mean: 0.330246941432
Std: 0.120898148612
Algorithm name: Improved Harmony Search - iter:100000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: 1.37008557469e-05
Mean: 0.0019855453631
Std: 0.00355795932405
Algorithm name: A Simple Genetic Algorithm - gen:500 CR:0.95 M:0.02 elitism:1 mutation:GAUSSIAN (0.1) selection:ROULETTE crossover:EXPONENTIAL
Best: 0.208885494919
Mean: 0.859154929976
Std: 0.198485142481
Algorithm name: CMAES - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-30 xtol:1e-30 memory:0
Best: 0.0
Mean: 1.66533453694e-18
Std: 1.74749950496e-17
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: 1.33040190975e-06
Mean: 0.000845909287089
Std: 0.00205890013972
Testing problem: Levy5, Dimension: 10
With Population Size: 200
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: -4387.20160305
Mean: -4178.79938131
Std: 150.94774702
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: -4411.52297573
Mean: -4389.35351183
Std: 30.8433654427
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: -3724.86628084
Mean: -2971.5958832
Std: 204.815810994
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: -4411.33854401
Mean: -4400.53420191
Std: 9.85178158634
Algorithm name: DE - 1220 - gen:500 self_adaptation:1 variants:[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] memory:0 ftol:1e-30 xtol:1e-30
Best: -4407.01586189
Mean: -4368.30544771
Std: 23.7254369786
Algorithm name: Simulated Annealing (Corana's) - iter:100000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: -4253.59893768
Mean: -3598.23413052
Std: 445.383626662
Algorithm name: Improved Harmony Search - iter:100000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: -4411.4856384
Mean: -4411.12174008
Std: 3.14735869906
Algorithm name: A Simple Genetic Algorithm - gen:500 CR:0.95 M:0.02 elitism:1 mutation:GAUSSIAN (0.1) selection:ROULETTE crossover:EXPONENTIAL
Best: -4330.2214256
Mean: -3836.97967203
Std: 383.870705214
Algorithm name: CMAES - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-30 xtol:1e-30 memory:0
Best: -4411.52297573
Mean: -4206.07020919
Std: 238.229391895
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: -4406.46604815
Mean: -4291.77040001
Std: 46.1582267305
Testing problem: Cassini 1, Dimension: 6
With Population Size: 200
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 5.04018201754
Mean: 6.79372906208
Std: 2.22542730927
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: 4.93070824972
Mean: 9.53159623524
Std: 3.04336166126
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: 5.20080937001
Mean: 5.33209031981
Std: 0.401683809415
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: 5.30888577429
Mean: 5.56747764215
Std: 0.579631151048
Algorithm name: DE - 1220 - gen:500 self_adaptation:1 variants:[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] memory:0 ftol:1e-30 xtol:1e-30
Best: 5.54016006552
Mean: 6.36938825305
Std: 0.530607818007
Algorithm name: Simulated Annealing (Corana's) - iter:100000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 5.09640034788
Mean: 14.4438901322
Std: 5.70277891704
Algorithm name: Improved Harmony Search - iter:100000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: 5.32249495721
Mean: 5.41008844656
Std: 0.341458865549
Algorithm name: A Simple Genetic Algorithm - gen:500 CR:0.95 M:0.02 elitism:1 mutation:GAUSSIAN (0.1) selection:ROULETTE crossover:EXPONENTIAL
Best: 5.42297048468
Mean: 13.2175830297
Std: 4.63230170252
Algorithm name: CMAES - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-30 xtol:1e-30 memory:0
Best: 7.33477787268
Mean: 15.5196398272
Std: 2.20661850595
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: 5.70051564357
Mean: 7.95660144168
Std: 1.74478604827
Testing problem: GTOC_1, Dimension: 8
With Population Size: 200
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: -1364984.44933
Mean: -872774.195224
Std: 156700.444001
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: -1513182.71537
Mean: -1019599.17812
Std: 164862.801168
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: -778873.844385
Mean: -485480.554516
Std: 110521.445287
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: -937736.897924
Mean: -668613.035871
Std: 107913.229358
Algorithm name: DE - 1220 - gen:500 self_adaptation:1 variants:[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] memory:0 ftol:1e-30 xtol:1e-30
Best: -1100301.28142
Mean: -701340.44943
Std: 99273.891416
Algorithm name: Simulated Annealing (Corana's) - iter:100000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: -1061933.47287
Mean: -164672.732069
Std: 226898.242732
Algorithm name: Improved Harmony Search - iter:100000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: -1240853.77337
Mean: -843177.770245
Std: 152241.668924
Algorithm name: A Simple Genetic Algorithm - gen:500 CR:0.95 M:0.02 elitism:1 mutation:GAUSSIAN (0.1) selection:ROULETTE crossover:EXPONENTIAL
Best: -1132191.23926
Mean: -276656.009097
Std: 267113.493905
Algorithm name: CMAES - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-30 xtol:1e-30 memory:0
Best: -1352425.71763
Mean: -474388.699503
Std: 346410.853614
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: -997769.975275
Mean: -667391.144783
Std: 121671.62254
Testing problem: Cassini 2, Dimension: 22
With Population Size: 200
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 11.3676255684
Mean: 17.294847587
Std: 2.3867494764
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: 14.7044937149
Mean: 21.5705987875
Std: 1.64109032484
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: 18.4687143877
Mean: 25.3534035537
Std: 2.20414883178
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: 13.8270261692
Mean: 20.8843973196
Std: 2.28013808627
Algorithm name: DE - 1220 - gen:500 self_adaptation:1 variants:[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] memory:0 ftol:1e-30 xtol:1e-30
Best: 14.6437452038
Mean: 19.4598861009
Std: 2.02614327292
Algorithm name: Simulated Annealing (Corana's) - iter:100000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 8.81796533657
Mean: 20.8111203558
Std: 5.01913672117
Algorithm name: Improved Harmony Search - iter:100000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: 12.5244912563
Mean: 23.8786498013
Std: 3.36491074799
Algorithm name: A Simple Genetic Algorithm - gen:500 CR:0.95 M:0.02 elitism:1 mutation:GAUSSIAN (0.1) selection:ROULETTE crossover:EXPONENTIAL
Best: 15.1097524412
Mean: 24.237713453
Std: 3.12454108224
Algorithm name: CMAES - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-30 xtol:1e-30 memory:0
Best: 14.5153678492
Mean: 19.7705726549
Std: 1.4364668943
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: 12.3497888051
Mean: 20.7899379688
Std: 2.65512840743
Testing problem: Messenger full, Dimension: 26
With Population Size: 200
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 9.21673706616
Mean: 15.4832080945
Std: 1.75174823924
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: 10.046345902
Mean: 15.3960081099
Std: 1.15022907391
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: 17.5132339334
Mean: 24.5203473463
Std: 2.41054823148
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: 12.9035005334
Mean: 21.0767579742
Std: 2.41936163633
Algorithm name: DE - 1220 - gen:500 self_adaptation:1 variants:[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] memory:0 ftol:1e-30 xtol:1e-30
Best: 13.5005389096
Mean: 19.369744293
Std: 2.06563110594
Algorithm name: Simulated Annealing (Corana's) - iter:100000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 7.48104509949
Mean: 18.1403801852
Std: 5.44902331814
Algorithm name: Improved Harmony Search - iter:100000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: 16.9701462087
Mean: 20.4024838399
Std: 1.5630929897
Algorithm name: A Simple Genetic Algorithm - gen:500 CR:0.95 M:0.02 elitism:1 mutation:GAUSSIAN (0.1) selection:ROULETTE crossover:EXPONENTIAL
Best: 8.59740835525
Mean: 19.8852583339
Std: 3.2165163987
Algorithm name: CMAES - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-30 xtol:1e-30 memory:0
Best: 12.5571444921
Mean: 14.4784372542
Std: 1.25033843041
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: 13.1431332721
Mean: 22.2151104976
Std: 3.04117115649