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Test Data (1304_20)
magnific0 edited this page Feb 25, 2014
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1 revision
Trials: 200 - Population size: 20 - Generations: 500
Testing problem: Schwefel, Dimension: 10
With Population Size: 20
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 3.90173227061e-10
Mean: 662.177396549
Std: 259.715809161
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: 5.18205242468
Mean: 589.875680607
Std: 261.108821851
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: 2.41019915848e-05
Mean: 3.55874668133
Std: 20.203158925
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: 0.0
Mean: 8.29068342301
Std: 30.2191877094
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: 1.81898940355e-12
Mean: 1.77657713507
Std: 14.3964704106
Algorithm name: Simulated Annealing (Corana's) - iter:10000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 0.0206870813663
Mean: 318.852919657
Std: 164.580350265
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: 2.19376288442e-05
Mean: 7.55118002871e-05
Std: 2.80266195254e-05
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: 4.09590559753
Mean: 490.243092454
Std: 183.34626468
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: 1293.84684093
Mean: 1955.03198714
Std: 255.543335231
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: 5.74337496144
Mean: 390.231328647
Std: 142.633481888
Testing problem: Rastrigin, Dimension: 10
With Population Size: 20
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 0.995081205816
Mean: 6.49082621999
Std: 2.99428601142
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: 9.46131336832e-07
Mean: 5.56354676126
Std: 2.67852256895
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: 0.43524353786
Mean: 5.81734334515
Std: 2.07549908656
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: 0.0
Mean: 0.0547227229511
Std: 0.332939526798
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:10000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 0.0703110329116
Mean: 5.04430919802
Std: 2.17685236273
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: 3.9512086687e-06
Mean: 0.321937638448
Std: 0.453070042068
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: 0.109628084758
Mean: 0.899979795119
Std: 0.572313222435
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: 1.98991811419
Mean: 7.19355032946
Std: 3.47767226821
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: 0.106095458641
Mean: 3.17671052683
Std: 1.54276390386
Testing problem: Rosenbrock, Dimension: 10
With Population Size: 20
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 0.00711442738739
Mean: 4.43693309312
Std: 1.96952902913
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: 0.170175248087
Mean: 63.6560630988
Std: 95.1076881512
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: 0.152816542164
Mean: 1.70368430755
Std: 0.799726728534
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: 4.36494250499e-05
Mean: 2.8423740121
Std: 2.12087406832
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: 4.57848297919
Mean: 5.95872420193
Std: 0.33044742107
Algorithm name: Simulated Annealing (Corana's) - iter:10000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 0.0275643811236
Mean: 3.87218587517
Std: 10.7116394736
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: 0.0839408615079
Mean: 22.2419121153
Std: 25.2658283774
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: 1.00112178995
Mean: 53.7711374685
Std: 103.996463386
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: 2.14964596673e-28
Mean: 0.0200873416585
Std: 0.281185584894
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: 0.980762680478
Mean: 6.64971944786
Std: 5.14446859296
Testing problem: Ackley, Dimension: 10
With Population Size: 20
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 4.91551941373e-08
Mean: 0.00577620651948
Std: 0.0814768418335
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: 0.00237869328833
Mean: 1.60327495021
Std: 1.28360368573
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: 4.81832788748e-05
Mean: 0.000221597301935
Std: 0.000115886967436
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: 1.7870904756e-10
Mean: 0.00577574372718
Std: 0.0814768746391
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: 1.63089097782e-12
Std: 3.87838924327e-12
Algorithm name: Simulated Annealing (Corana's) - iter:10000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 0.0208230698492
Mean: 0.0841415406396
Std: 0.0257333714121
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: 0.000638784801592
Mean: 0.00139768239132
Std: 0.00024965691124
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: 0.209227212323
Mean: 0.60539076096
Std: 0.195119343838
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: 2.10551576174e-13
Std: 2.45135794532e-12
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: 0.000212630053344
Mean: 0.112270770063
Std: 0.233942401953
Testing problem: Griewank, Dimension: 10
With Population Size: 20
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 4.10935026851e-06
Mean: 0.056090488126
Std: 0.0313500926074
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: 0.00566699491523
Mean: 0.246432130336
Std: 0.443465168549
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: 0.091601421184
Mean: 0.238100804994
Std: 0.0638434666277
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: 2.67708077928e-12
Mean: 0.00547786390036
Std: 0.00684947991691
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: 5.07567518065e-05
Std: 0.000714986741889
Algorithm name: Simulated Annealing (Corana's) - iter:10000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 0.107969536782
Mean: 0.340068006446
Std: 0.130261854541
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: 9.23700700344e-05
Mean: 0.023614215755
Std: 0.0166816367708
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: 0.218096592106
Mean: 0.811166396901
Std: 0.235274969774
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: 0.00332636174621
Std: 0.00552262544874
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: 0.00047226827658
Mean: 0.0994500933744
Std: 0.0670960393046
Testing problem: Levy5, Dimension: 10
With Population Size: 20
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: -4325.65761273
Mean: -3454.57090741
Std: 519.91955685
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: -4404.64044644
Mean: -3605.28887518
Std: 512.995443482
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: -3596.36297575
Mean: -2561.29840637
Std: 342.036605793
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: -4411.52297553
Mean: -4347.64334069
Std: 62.7249510748
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: -4409.29033597
Mean: -4252.89235606
Std: 121.088019977
Algorithm name: Simulated Annealing (Corana's) - iter:10000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: -4231.27838905
Mean: -3554.11437368
Std: 400.907274719
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: -4411.33012791
Mean: -4113.79982299
Std: 256.900952051
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: -4172.5072479
Mean: -3372.29159666
Std: 409.58031358
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: -4113.00700325
Mean: -2395.35690529
Std: 599.72593408
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: -3964.53595437
Mean: -2941.12755347
Std: 383.705675506
Testing problem: Cassini 1, Dimension: 6
With Population Size: 20
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 5.31800699846
Mean: 12.3607438574
Std: 3.3298782005
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: 5.26154678032
Mean: 13.7168846537
Std: 4.47929495529
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: 4.93176322905
Mean: 8.75581243217
Std: 3.47819507196
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: 5.00665773787
Mean: 9.49119054539
Std: 3.2576464154
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: 5.42808178767
Mean: 10.1789304469
Std: 3.71090924295
Algorithm name: Simulated Annealing (Corana's) - iter:10000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 5.36587739673
Mean: 21.3173446707
Std: 15.1784518042
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: 5.36988725436
Mean: 10.221719704
Std: 4.18470889468
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: 5.91223847264
Mean: 26.5169721487
Std: 14.7600069077
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: 4.93070842698
Mean: 15.8149301855
Std: 3.04498930615
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: 6.21511811086
Mean: 12.8318786471
Std: 3.46539288062
Testing problem: GTOC_1, Dimension: 8
With Population Size: 20
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: -1044976.4212
Mean: -433955.328326
Std: 283469.897501
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: -1138278.10661
Mean: -424547.855333
Std: 290055.178241
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: -859168.936088
Mean: -276588.462936
Std: 156346.473138
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: -779548.121999
Mean: -384622.590259
Std: 171213.103915
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: -843871.175003
Mean: -415105.47682
Std: 163231.565798
Algorithm name: Simulated Annealing (Corana's) - iter:10000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: -948928.113591
Mean: -95035.8180183
Std: 166692.421957
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: -1359163.48555
Mean: -694665.986351
Std: 215950.519879
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: -853112.334221
Mean: -110514.491584
Std: 173999.552953
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: -1132812.55768
Mean: -105353.686309
Std: 169808.712152
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: -780852.079732
Mean: -178976.681879
Std: 143384.217286
Testing problem: Cassini 2, Dimension: 22
With Population Size: 20
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 13.5189856711
Mean: 23.3990371532
Std: 3.82444968547
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: 17.9715739194
Mean: 27.8342476071
Std: 3.65957950746
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: 21.7359948683
Mean: 29.7737655064
Std: 2.78511959177
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: 15.9818839184
Mean: 26.0434993416
Std: 3.04570540004
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: 14.935935327
Mean: 24.8253980476
Std: 3.14729502657
Algorithm name: Simulated Annealing (Corana's) - iter:10000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 12.654046653
Mean: 26.4512645588
Std: 7.06868820368
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: 13.0623741473
Mean: 23.4017731665
Std: 4.03501611575
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: 17.5262306737
Mean: 33.1139607968
Std: 7.71387913894
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: 16.4637402461
Mean: 23.3069564462
Std: 3.67925785898
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: 15.4321461296
Mean: 29.6617970247
Std: 4.30205061083
Testing problem: Messenger full, Dimension: 26
With Population Size: 20
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 10.7312531734
Mean: 19.904631493
Std: 3.62300965799
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: 9.93232023826
Mean: 22.3506401433
Std: 3.84651189989
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: 19.8985188459
Mean: 30.112761153
Std: 3.84084939633
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: 16.8458918028
Mean: 26.5524112933
Std: 3.41113387709
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: 16.9796636944
Mean: 24.8489333163
Std: 3.47761531864
Algorithm name: Simulated Annealing (Corana's) - iter:10000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 9.34216647409
Mean: 24.4172459188
Std: 8.17330668532
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: 14.8884597996
Mean: 22.1781578371
Std: 4.6710128447
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: 15.4145267948
Mean: 32.5869109839
Std: 8.96334638724
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: 13.2330284758
Mean: 18.2181844994
Std: 2.51766943202
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: 18.9331337508
Mean: 31.9316788083
Std: 5.56133076738