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Test Data (111113_20)
magnific0 edited this page Feb 25, 2014
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1 revision
Testing problem: <class 'PyGMO.problem._problem.schwefel'>, Dimension: 10
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 0.000127282363792
Mean: 616.494887937
Std: 244.261659932
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 strategy:2
Best: 0.00013772071361
Mean: 2.96551166462
Std: 18.4905344442
Algorithm name: Simulated Annealing (Corana's) - iter:10000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 0.0234692712502
Mean: 297.054916441
Std: 157.200855726
Algorithm name: Improved Harmony Search - iter:10000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: 0.000153450675498
Mean: 0.0103727975804
Std: 0.140784634033
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: 119.231825433
Mean: 460.494515106
Std: 180.083019853
Algorithm name: CMAES - - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-06 xtol:1e-06 memory:0
Best: 592.21355552
Mean: 1715.32597413
Std: 440.404261085
Algorithm name: Artificial Bee Colony optimization - gen:500 limit:20
Best: 15.5970540235
Mean: 367.986927805
Std: 133.204448181
Testing problem: <class 'PyGMO.problem._problem.rastrigin'>, Dimension: 10
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 0.00162118999148
Mean: 6.5780108993
Std: 3.08816840068
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 strategy:2
Best: 0.315157739313
Mean: 5.82518932865
Std: 2.26326735658
Algorithm name: Simulated Annealing (Corana's) - iter:10000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 0.0328692638161
Mean: 4.51878436273
Std: 2.17412902874
Algorithm name: Improved Harmony Search - iter:10000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: 4.0015069942e-06
Mean: 0.274838143063
Std: 0.448433901035
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: 0.0927752930042
Mean: 0.782171296743
Std: 0.459549633203
Algorithm name: CMAES - - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-06 xtol:1e-06 memory:0
Best: 3.97983690673
Mean: 28.8235775007
Std: 18.3300525763
Algorithm name: Artificial Bee Colony optimization - gen:500 limit:20
Best: 0.105736893518
Mean: 3.08410155717
Std: 1.539647106
Testing problem: <class 'PyGMO.problem._problem.rosenbrock'>, Dimension: 10
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 0.00881908051088
Mean: 5.35980499387
Std: 8.29479205435
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 strategy:2
Best: 0.00233318996891
Mean: 1.67429798118
Std: 0.811028608295
Algorithm name: Simulated Annealing (Corana's) - iter:10000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 0.0393858444712
Mean: 7.97218135917
Std: 21.9686295211
Algorithm name: Improved Harmony Search - iter:10000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: 0.956472060957
Mean: 36.5647581809
Std: 29.9834967967
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: 2.3176473038
Mean: 78.6449379687
Std: 136.229795235
Algorithm name: CMAES - - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-06 xtol:1e-06 memory:0
Best: 1.41869170473e-07
Mean: 0.398547631601
Std: 1.17176587245
Algorithm name: Artificial Bee Colony optimization - gen:500 limit:20
Best: 0.502531001134
Mean: 5.71225878071
Std: 4.37778157048
Testing problem: <class 'PyGMO.problem._problem.ackley'>, Dimension: 10
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 2.32413630563e-08
Mean: 3.81402542811e-07
Std: 3.20494564451e-07
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 strategy:2
Best: 4.59418721985e-05
Mean: 0.000204415621159
Std: 0.00011144298738
Algorithm name: Simulated Annealing (Corana's) - iter:10000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 0.0304032060458
Mean: 0.0904880526515
Std: 0.0312906237811
Algorithm name: Improved Harmony Search - iter:10000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: 0.000519901529526
Mean: 0.00150213212251
Std: 0.000281762944794
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: 0.136327907423
Mean: 0.555926357611
Std: 0.251733137635
Algorithm name: CMAES - - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-06 xtol:1e-06 memory:0
Best: 8.22381640564e-07
Mean: 12.0849119124
Std: 7.57404637393
Algorithm name: Artificial Bee Colony optimization - gen:500 limit:20
Best: 5.30673283095e-11
Mean: 0.0781086335943
Std: 0.17954617318
Testing problem: <class 'PyGMO.problem._problem.griewank'>, Dimension: 10
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 0.0073960428895
Mean: 0.058624388728
Std: 0.0298311227375
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 strategy:2
Best: 0.0617435807014
Mean: 0.231192160911
Std: 0.069104553669
Algorithm name: Simulated Annealing (Corana's) - iter:10000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 0.0678499286157
Mean: 0.338599242126
Std: 0.124937374683
Algorithm name: Improved Harmony Search - iter:10000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: 6.37169851614e-05
Mean: 0.0240581151169
Std: 0.0182685343858
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: 0.226853211148
Mean: 0.756159401577
Std: 0.228699676964
Algorithm name: CMAES - - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-06 xtol:1e-06 memory:0
Best: 3.54994147633e-07
Mean: 0.145155362298
Std: 0.269767299407
Algorithm name: Artificial Bee Colony optimization - gen:500 limit:20
Best: 0.000383901933643
Mean: 0.0953773876024
Std: 0.0730986075721
Testing problem: <class 'PyGMO.problem._problem.cassini_1'>, Dimension: 6
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 5.12727016433
Mean: 12.4492587595
Std: 3.20911906948
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 strategy:2
Best: 5.30342353741
Mean: 9.05345352859
Std: 3.31420832697
Algorithm name: Simulated Annealing (Corana's) - iter:10000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 5.42637297463
Mean: 22.4314032415
Std: 18.6058495472
Algorithm name: Improved Harmony Search - iter:10000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: 5.1301147108
Mean: 12.2243254481
Std: 3.52544564444
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: 7.47821473209
Mean: 31.2279575884
Std: 20.2901814586
Algorithm name: CMAES - - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-06 xtol:1e-06 memory:0
Best: 5.30343166312
Mean: 24.5250563408
Std: 18.9947307127
Algorithm name: Artificial Bee Colony optimization - gen:500 limit:20
Best: 5.86364558724
Mean: 11.5058660912
Std: 2.95659767329
Testing problem: <class 'PyGMO.problem._problem.cassini_2'>, Dimension: 22
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 13.7429592617
Mean: 23.1529500775
Std: 3.58283810753
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 strategy:2
Best: 22.6587792033
Mean: 29.8462647696
Std: 2.62100838765
Algorithm name: Simulated Annealing (Corana's) - iter:10000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 12.920925309
Mean: 26.5056382664
Std: 6.92018725721
Algorithm name: Improved Harmony Search - iter:10000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: 13.5869094673
Mean: 22.112864336
Std: 4.46327833456
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: 20.2919460802
Mean: 33.8411039653
Std: 6.98168322137
Algorithm name: CMAES - - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-06 xtol:1e-06 memory:0
Best: 22.9876374119
Mean: 42.5195980363
Std: 11.5476095342
Algorithm name: Artificial Bee Colony optimization - gen:500 limit:20
Best: 15.6710234164
Mean: 27.385262391
Std: 4.14237372586