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Test Data (110301_20)

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magnific0 edited this page Feb 25, 2014 · 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:  118.43846189
    Mean:  662.110826137
    Std:   254.736369705
    Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.8 strategy:2 
    Best:  0.000127283981783
    Mean:  1.77670405419
    Std:   14.396470473
    Algorithm name: Simulated Annealing (Corana's) - niter:10000 Ts:1 Tf:0.1 step_adj:1 bin_size:20 range:1 
    Best:  0.282258526512
    Mean:  395.461212986
    Std:   176.270077404
    Algorithm name: Improved Harmony Search - gen:10000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1 
    Best:  0.000146739569573
    Mean:  0.0107936428013
    Std:   0.14077743221
    Algorithm name: A Simple Genetic Algorithm - gen:500 CR:0.8 M:0.1 elitism:1 mutation type:0 selection type:1 crossover type:1 
    Best:  119.547587285
    Mean:  862.091726784
    Std:   270.229502489
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:  0.994959203394
    Mean:  7.21787135905
    Std:   3.23260347706
    Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.8 strategy:2 
    Best:  2.8274346846e-06
    Mean:  0.0802086848242
    Std:   0.261131511477
    Algorithm name: Simulated Annealing (Corana's) - niter:10000 Ts:1 Tf:0.1 step_adj:1 bin_size:20 range:1 
    Best:  1.1280313728
    Mean:  5.91047584239
    Std:   2.30243859618
    Algorithm name: Improved Harmony Search - gen:10000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1 
    Best:  7.28361411007e-06
    Mean:  0.539020862507
    Std:   0.584945802539
    Algorithm name: A Simple Genetic Algorithm - gen:500 CR:0.8 M:0.1 elitism:1 mutation type:0 selection type:1 crossover type:1 
    Best:  0.103668847646
    Mean:  1.39874754192
    Std:   0.908378613362
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:  0.00199490878109
    Mean:  6.82111277549
    Std:   13.2922097325
    Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.8 strategy:2 
    Best:  0.556690920881
    Mean:  2.6151337877
    Std:   0.655109264618
    Algorithm name: Simulated Annealing (Corana's) - niter:10000 Ts:1 Tf:0.1 step_adj:1 bin_size:20 range:1 
    Best:  0.186399211829
    Mean:  4.20731397007
    Std:   11.5249873942
    Algorithm name: Improved Harmony Search - gen:10000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1 
    Best:  0.873461413883
    Mean:  26.0785920392
    Std:   27.5691275669
    Algorithm name: A Simple Genetic Algorithm - gen:500 CR:0.8 M:0.1 elitism:1 mutation type:0 selection type:1 crossover type:1 
    Best:  2.01715626635
    Mean:  30.1210158665
    Std:   55.9287950204
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:  3.63226431155e-08
    Mean:  4.45633001647e-07
    Std:   3.73726101008e-07
    Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.8 strategy:2 
    Best:  5.00727404118e-06
    Mean:  2.20103830842e-05
    Std:   1.05896938135e-05
    Algorithm name: Simulated Annealing (Corana's) - niter:10000 Ts:1 Tf:0.1 step_adj:1 bin_size:20 range:1 
    Best:  0.249137032466
    Mean:  0.889135855622
    Std:   0.388751246022
    Algorithm name: Improved Harmony Search - gen:10000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1 
    Best:  0.000775559529999
    Mean:  0.00152544350965
    Std:   0.000264841334272
    Algorithm name: A Simple Genetic Algorithm - gen:500 CR:0.8 M:0.1 elitism:1 mutation type:0 selection type:1 crossover type:1 
    Best:  0.0613524187551
    Mean:  0.18975553703
    Std:   0.0723902595707
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:  7.04319833654e-05
    Mean:  0.0572263648459
    Std:   0.0305637928851
    Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.8 strategy:2 
    Best:  0.025739157367
    Mean:  0.111654043789
    Std:   0.0416034716097
    Algorithm name: Simulated Annealing (Corana's) - niter:10000 Ts:1 Tf:0.1 step_adj:1 bin_size:20 range:1 
    Best:  0.66813092931
    Mean:  1.42048019902
    Std:   0.2271232911
    Algorithm name: Improved Harmony Search - gen:10000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1 
    Best:  0.000102545689771
    Mean:  0.0292906813671
    Std:   0.0197158603267
    Algorithm name: A Simple Genetic Algorithm - gen:500 CR:0.8 M:0.1 elitism:1 mutation type:0 selection type:1 crossover type:1 
    Best:  0.160676945778
    Mean:  0.556650282801
 Std:   0.244012342983

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