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2021-12-24 15:10:14,440 - pyswarms.single.global_best - INFO - Optimize for 20 iters with {'c1': 0.4, 'c2': 0.8, 'w': 0.3}
2021-12-24 15:10:43,712 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.013333320617675781, best pos: [-0.04550809 0.32967938 0.38640711 0.30698243 -0.78099491 0.58092664
0.01487479 0.30540031 0.62235259 -0.07715878 -0.21593149 -0.07222031
-0.23369948 0.23326358 0.31269185 -0.71218044 0.47641634 -0.67050789
-0.51173085 0.47442693 -0.49497589 -0.56564321 0.0063656 -0.30840548
0.02882204 0.2457204 0.43367811 -0.24884434 -0.41763095 -0.13957323
-0.37176375 0.29578205 0.6729181 -0.73842422 -0.16968892 -0.32929589
-0.93466398 -0.77066601 0.10704728 0.11593037 0.70126716 -0.51289081
0.4862886 -0.53846688 -0.31008575 0.78484932 0.60336497 -0.64309738
0.00469037 0.15168334 0.51994791 0.66854125 0.7185689 -0.17259144
-0.08229466 0.65804748 0.07537048 -0.38277041 0.67841477 -0.46641096
-0.22578131 0.39821009 0.5220811 0.66683023 0.78338438 -0.58045664
0.71120823 0.37737763 0.87171139 -0.6019598 -0.44752316 -0.05359088
-0.49676028 -0.14879591 -0.35263974 0.5306445 0.35052938 0.15994291
0.70338971 -0.76581162 0.11593576 0.41823839 -0.35560326]
2021-12-24 15:11:56,671 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.4, 'c2': 0.8, 'w': 0.3}
2021-12-24 15:12:11,429 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.14666664600372314, best pos: [ 0.31195331 -0.11413643 0.78701044 -0.39364172 0.56989702 -0.06157644
-0.20608728 0.03815522 -0.72808933 -0.34961396 -0.45999692 -0.08289407
0.26271256 -0.24447461 0.1932648 0.39217129 0.56749249 -0.68158859
0.13353599 0.11312849 -0.01281245 -0.09188209 0.41324592 -0.1854531
0.39965421 -0.03898302 0.4111428 0.24359659 0.78196339 0.21477817
0.42662923 -0.11328706 -0.44988434 -0.83661578 0.2465271 -0.23791602
0.69526616 -0.1494819 0.50251875 -0.03108062 0.01058332 -0.08921089
0.14723419 -0.39071822 0.68692766 0.19252435 0.39406525 0.26321247
-0.21465808 0.51270192 -0.71263219 0.28644382 -0.02158699 0.38290503
0.61950399 0.39114633 -0.33111314 0.71688269 0.42110683 0.30051403
-0.30016197 -0.1616585 -0.15238313 -0.2322124 0.20597433 0.78245106
0.56900742 0.13500861 0.21094984 -0.25302522 0.19058396 -0.58446064
0.06853392 -0.04661844 0.042566 0.21650065 0.29342631 0.28273501
0.1368777 0.18687902 0.30616988 0.42400952 -0.01823348]
2021-12-24 15:12:37,028 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.3, 'c2': 0.8, 'w': 0.4}
2021-12-24 15:12:46,767 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.1066666841506958, best pos: [ 3.93454739e-01 -1.96156804e-02 -2.33726746e-01 1.34173596e-01
-1.74478147e-01 -5.94546652e-01 -1.29385578e-01 -1.00120440e-01
1.51593425e-01 -1.85596280e-02 -4.70328948e-01 -7.10184612e-01
-2.21227255e-01 6.43900266e-01 -3.91793676e-01 -2.10393744e-01
-9.46563073e-02 -2.03293090e-01 -4.16984167e-01 3.63339432e-01
5.09338792e-02 5.89423680e-01 -4.72491929e-01 5.53148190e-01
-5.51946622e-01 -1.62567439e-01 -6.12695861e-02 -1.06892279e-01
4.23162094e-01 3.96618997e-01 5.69641765e-02 3.88253802e-01
-2.20345407e-01 -1.83015632e-01 5.70590624e-01 3.30653824e-01
-4.45686122e-01 -5.88835634e-01 -4.86619146e-01 5.61643816e-01
1.43573212e-01 5.58262189e-01 -4.54148884e-01 -4.36664571e-01
2.11677660e-01 -4.44161146e-01 8.93623922e-01 1.01986923e-01
4.25128097e-01 1.99101701e-01 2.47660808e-01 -1.42620810e-01
4.94369600e-01 -2.59470342e-01 1.23503450e-01 -5.00264842e-02
7.64890245e-01 -2.35993826e-02 -4.51768705e-01 2.41454447e-01
-3.28837196e-01 3.04610852e-01 -2.30933330e-01 -9.67594910e-02
1.66907479e-01 7.67463281e-01 -4.57090503e-01 -2.45079238e-04
7.64761250e-01 8.76220824e-01 -2.59287568e-01 4.76202727e-01
-2.60619769e-01 -1.17107360e-01 -8.07382032e-01 4.10983816e-02
1.94904031e-01 1.26781567e-01 1.46790674e-01 5.54121489e-01
-1.73373751e-01 3.80431280e-02 -2.27506426e-01]
2021-12-24 15:12:55,940 - pyswarms.single.global_best - INFO - Optimize for 15 iters with {'c1': 0.3, 'c2': 0.8, 'w': 0.4}
2021-12-24 15:13:10,761 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.026666641235351562, best pos: [ 0.37648876 0.52550025 0.27868861 0.49125151 -0.22334734 -0.21524013
0.81827066 0.40944827 0.53159103 -0.22921871 -0.10054244 -0.07531332
0.06575708 0.53502484 0.38305814 0.22312674 -0.36743654 -0.00764047
0.14228572 0.31422637 0.91387233 0.31442486 0.69285565 -0.1159172
0.03274359 -0.41176154 0.50317966 0.48103588 0.15484857 -0.16734504
-0.60260445 0.71035991 -0.60892911 0.78707346 -0.80168175 0.22406515
-0.03028243 0.19318296 0.03233849 -0.27001624 -0.40412654 0.3745683
0.718033 0.30335705 -0.32914314 0.06085858 -0.20809357 0.43770373
0.44412568 0.1938437 -0.05982613 -0.34481906 0.42462468 -0.51726579
-0.369245 0.45608668 -0.61954098 0.3654282 -0.545277 -0.36671382
-0.36328286 0.29821843 0.2141392 -0.85631254 -0.80235601 0.10829876
-0.51970679 0.46240147 -0.44930346 0.2701291 0.17594199 0.7242234
0.18786565 0.81032199 0.64329943 0.78851881 -0.35031106 0.13099553
0.03494212 -0.12649696 0.6223655 0.39067569 -0.15042912]
2021-12-24 15:20:22,060 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.3, 'c2': 0.8, 'w': 0.4}
2021-12-24 15:20:46,558 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.09333330392837524, best pos: [ 0.37496702 -0.39117362 0.24054782 0.68608371 -0.60934994 -0.65155336
-0.33426704 0.74688696 0.07409111 -0.40405233 0.24260478 0.68431493
-0.23936307 0.59362324 0.06458691 0.11687053 0.10274735 0.20797045
-0.82220403 0.87899601 -0.21009084 0.93180125 0.40079567 -0.24410009
0.51468845 0.06259973 0.39295168 0.70598321 -0.63449534 -0.19880143
-0.57768259 -0.15142872 0.60442335 0.36530328 0.53831311 -0.58560106
-0.31895384 -0.94404421 0.59093741 0.01475247 0.43399138 -0.49937562
-0.048844 -0.74144785 0.84120189 -0.34877253 -0.52730779 -0.25673029
0.28221575 -0.0108088 -0.74991211 -0.24770618 0.0252 0.12744961
0.40590807 0.07403343 -0.62950042 -0.55231381 0.99236361 -0.13347311
-0.72435414 -0.53188988 -0.50521768 0.55475059 0.39843702 0.38120302
0.28970702 0.51983821 0.89679399 0.80099021 0.06961821 -0.14184434
0.17369427 -0.07476284 -0.29819094 0.37360526 0.15281916 0.0151315
-0.82649483 -0.16221703 0.92600267 0.12073796 -0.65182587]
2021-12-24 15:21:15,945 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.3, 'c2': 0.8, 'w': 0.4}
2021-12-24 15:21:35,564 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.013333320617675781, best pos: [ 0.48592975 0.51714906 -0.14686375 -0.28694026 0.93106962 0.32872656
0.37823404 0.46877334 -0.75844581 -0.15739049 -0.40545587 0.45579509
-0.10238073 0.46653963 -0.03915547 0.19850814 0.63059817 -0.11306076
0.81563969 -0.54372796 -0.27144016 0.66634637 -0.67508944 0.24356216
-0.63979593 -0.08564186 0.37984684 0.44920715 0.45346678 -0.00814287
0.19576625 0.45393067 0.47258745 -0.87974458 0.43906273 0.67386261
0.52514259 0.512235 -0.54465195 0.26927912 0.72838838 -0.17182079
0.20487974 0.70713815 -0.34963713 0.80841311 -0.79511774 0.33385184
0.29574759 0.51619409 -0.16022775 0.55859429 0.30243279 0.46265288
0.32803738 0.16766953 0.52852657 -0.7243563 0.48972548 0.18219974
0.74799504 -0.32067014 0.00840875 0.34861746 -0.41239207 -0.15368962
0.18369395 0.34360157 0.09548845 0.09496859 0.10226721 0.09322292
-0.51170326 0.34180906 0.77744806 -0.40982609 -0.826135 0.09469398
0.57302205 0.18747799 -0.55942978 -0.12527957 -0.53185195]
2021-12-27 13:26:57,612 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.3, 'c2': 0.8, 'w': 0.4}
2021-12-27 13:27:18,888 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.0533333420753479, best pos: [-0.27870843 -0.0751216 0.38204384 0.23425878 -0.15284633 -0.00719908
0.12623929 0.65738932 -0.12623901 0.63882964 0.72133467 -0.1710212
0.31416137 0.19462992 -0.05808766 -0.2060965 -0.54768538 -0.35562554
0.8686761 -0.36972517 0.06803684 0.60644802 0.57466729 -0.12985907
0.06035206 0.16716783 -0.25976779 0.39191649 0.25923877 -0.09525394
0.17025635 0.36205333 0.14215223 -0.30016848 0.31553668 0.42522842
0.47324584 0.06595148 -0.15359145 -0.0988236 -0.57571064 0.05823058
0.6702293 0.53999839 -0.40198864 0.28995898 0.58153193 0.88701174
-0.37750401 0.70126769 0.58106362 -0.16677001 -0.28516966 0.29767033
-0.21474559 0.0684149 -0.09731318 0.05674533 0.54003249 -0.13504345
0.60496694 -0.45541289 -0.04347122 0.46866128 -0.13498677 -0.05238729
0.18933643 0.87919564 -0.5027533 0.08024469 -0.13346654 -0.11060739
0.55204846 0.03767206 0.67695722 -0.58707428 -0.51267546 -0.40548255
0.11872619 0.12949638 0.91051098 -0.13950763 0.03954607]
2021-12-27 13:28:45,036 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.3, 'c2': 0.8, 'w': 0.4}
2021-12-27 13:29:03,175 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.13333332538604736, best pos: [ 0.45425134 0.80894159 0.54554152 0.2300179 -0.44850049 -0.66143681
0.13387732 -0.05605168 0.88801746 0.71342472 0.34919066 0.93411899
0.37556946 0.58929275 0.12492294 0.71186036 0.47126433 0.30163907
0.14342986 -0.31669216 0.33098211 -0.36551876 0.99140908 0.21541753
0.51737152 0.07591739 0.81305238 0.58476357 -0.30702178 0.51160159
0.41315852 0.16480395 -0.17920029 0.86768053 -0.88177414 -0.26116592
-0.33001607 -0.60744562 0.50651723 -0.0621259 -0.19229912 0.37927031
-0.49881829 -0.51737749 0.35621874 -0.45759982 -0.32147834 0.67408049
0.81966951 -0.578781 -0.00460305 -0.28039164 0.46138731 0.88474606
0.35984179 0.3601446 0.02035266 -0.59771663 -0.59097503 0.68452893
0.40905812 0.27097204 0.99767478 0.14003375 0.36965807 0.25894829
-0.69539896 -0.22512838 -0.61367382 0.48269718 -0.02431249 0.11474174
0.27109515 -0.10276166 -0.75172249 0.54411361 0.43438548 -0.12832022
0.26986008 0.394329 -0.35827744 -0.01375973 -0.04906412]
2021-12-27 13:29:03,313 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.3, 'c2': 0.8, 'w': 0.4}
2021-12-27 13:29:23,635 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.12000000476837158, best pos: [ 0.1797017 0.62163215 0.58519518 0.36062545 -0.66156506 0.73211676
0.34110676 -0.20127684 -0.68744978 -0.85732863 0.80640176 0.39015801
-0.60726414 0.39445753 -0.78721677 0.59709727 -0.12195495 -0.78758049
0.12668288 -0.4694242 0.6415798 0.94974067 0.81527848 0.24806083
-0.64671994 0.17299864 0.20043413 0.12112952 -0.31584172 -0.36642782
0.64527287 0.8770172 0.36745513 0.48488444 0.26743854 0.04033523
-0.21168364 0.69737883 0.16075458 0.66924778 0.39843093 0.34980774
-0.30233427 -0.57327542 0.66506139 -0.05622487 0.48607352 0.68656004
0.24635259 -0.20171963 0.39209078 -0.08161767 0.54891464 -0.34682149
0.12932325 0.31366886 0.13919944 -0.36408606 0.81142757 0.17994754
-0.26140714 0.37532527 0.16877888 0.30167495 0.30808507 0.50925943
-0.43985851 0.21981339 0.14745656 0.88231822 0.95262177 -0.05495779
0.55512005 -0.23970808 0.59426363 -0.2969771 -0.45063261 -0.59019389
-0.27641118 0.73869099 0.33182571 0.82870337 0.0070297 ]
2021-12-27 13:29:23,770 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.3, 'c2': 0.8, 'w': 0.4}
2021-12-27 13:29:42,262 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.04000002145767212, best pos: [ 0.32322178 -0.45471279 0.35701515 0.46513087 -0.32640748 -0.46179367
0.17468727 0.18723697 -0.05526697 -0.5418174 0.66936374 -0.30348229
-0.14733259 0.27494672 0.67228609 -0.24055102 0.00782638 -0.05999169
-0.00761224 0.24578109 -0.48701722 0.78145484 0.50367274 0.40575088
-0.00521038 0.17684574 -0.48611594 -0.1749539 0.64027986 0.29741369
-0.26343493 0.38876619 0.03298184 0.637831 -0.21797463 0.19573261
-0.43297663 0.12005292 -0.33989161 -0.00535451 0.06844525 -0.23241277
0.76392631 0.12103987 0.27980358 -0.98292727 -0.05699711 0.24208351
0.43910034 0.03999825 0.34667589 -0.31567358 -0.36680174 0.38280868
-0.30054868 0.60064231 -0.21708054 0.55142559 -0.32684952 0.06220455
-0.68961122 0.27488881 0.05974955 0.02408239 0.09526099 0.4021286
-0.39278137 -0.9475165 -0.03157881 -0.25828811 0.42228399 -0.34623721
-0.36020347 -0.21956833 0.41045356 0.24748634 0.61906647 -0.21104628
0.15207362 -0.27033456 0.12032218 0.3949482 -0.08154139]
2021-12-27 13:29:59,105 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.3, 'c2': 0.8, 'w': 0.4}
2021-12-27 13:30:14,689 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.06666666269302368, best pos: [ 6.54730060e-01 6.62488296e-01 7.85785496e-01 7.18257249e-01
-2.27911754e-01 4.43325294e-01 2.38463405e-01 4.84775955e-01
-1.22024957e-01 1.40168429e-01 5.43066101e-01 -7.35545715e-01
-1.10891111e-01 2.78094827e-02 1.80594393e-01 -3.27810815e-02
-3.24790635e-01 5.23442869e-01 3.77722742e-02 -7.66420972e-02
-1.52110253e-01 2.20028359e-01 6.41125095e-01 -2.76457063e-01
-6.06055899e-01 6.88730199e-01 3.42177755e-01 4.19759770e-01
4.41478927e-01 5.17567428e-01 5.67249257e-01 8.56630943e-01
-6.30528964e-01 2.59698394e-01 4.00015692e-01 5.95364805e-01
3.35067195e-01 2.25028832e-01 3.10091672e-01 7.01540917e-01
1.42142696e-01 2.57778437e-01 8.91999560e-02 3.61328870e-01
-7.10447142e-04 -2.16660675e-01 4.89187460e-01 6.89359964e-01
1.47011702e-01 -1.01851962e-01 6.47264813e-01 1.56036229e-01
7.93103375e-02 -5.10421651e-01 4.01229160e-01 4.63078737e-01
2.19712963e-01 5.04715977e-01 -4.30272068e-01 3.06243953e-01
3.74223933e-01 1.42599876e-01 9.56164214e-03 3.47685118e-02
-1.54033788e-01 -8.28259656e-02 -5.00035051e-01 5.61294632e-01
-5.88677822e-02 5.06508163e-01 1.90245341e-01 4.69366242e-01
-1.56133924e-01 5.20882244e-01 5.29480652e-02 5.02089266e-01
2.58142218e-01 4.36021983e-01 4.60761895e-01 2.66271349e-01
-1.36096479e-01 -1.56301250e-01 -4.37638838e-01]
2021-12-27 13:30:44,450 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.2, 'c2': 0.8, 'w': 0.35}
2021-12-27 13:30:59,699 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.04000002145767212, best pos: [ 0.85650839 0.42763698 0.40815687 0.5108547 -0.09155299 -0.53942113
-0.1801304 0.03322666 -0.1068766 -0.51100256 0.3635365 -0.26970681
0.02342693 -0.23372896 0.13734568 -0.53569226 -0.24916103 0.90135892
-0.34412557 0.61070016 -0.54131038 0.3016417 0.10070847 0.11011441
-0.94555341 -0.55593747 -0.45167487 -0.35141225 0.04850836 0.13966548
0.23703867 0.18640813 0.80979517 0.33856774 0.32434789 0.91316706
0.22605487 0.08695653 -0.0442146 -0.52495393 0.06301401 0.61910222
0.81520014 0.40374352 0.75931805 -0.25841715 0.32110203 0.31772209
0.83108339 -0.08624332 0.76453018 0.20044767 -0.66189619 0.20891969
-0.5817281 0.89080943 -0.37653242 0.54910579 0.67349224 0.1620074
-0.11074508 -0.20063857 -0.0456579 -0.86312335 -0.17402706 -0.06591546
-0.55998789 0.05012814 0.62306466 0.35380336 -0.37091945 0.64130342
0.10506286 0.32518001 -0.43702351 0.43400633 -0.27958913 0.29014158
0.44300145 -0.00100076 0.29891321 0.81299128 -0.38771287]
2021-12-27 13:31:26,712 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.3, 'c2': 0.8, 'w': 0.4}
2021-12-27 13:31:44,711 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.1066666841506958, best pos: [ 0.11190476 0.10218041 0.41078546 0.41261083 0.70232123 0.66156168
-0.04116735 -0.04846393 -0.24897615 -0.054294 0.64863517 0.46853741
0.26242345 -0.45857968 0.15058902 0.24882351 0.3113786 0.10636849
0.67724677 -0.25851795 0.47060376 0.64255579 -0.70188193 -0.20505317
-0.36152859 0.02531367 -0.82262787 -0.34962591 -0.71290276 -0.41695443
-0.28373709 -0.45578496 0.24498108 0.92289422 -0.93433637 0.80533694
-0.14032378 0.64766939 0.1749601 0.67102177 0.17992605 0.35142591
-0.17771188 -0.51149962 -0.00255723 -0.15067151 0.86653953 -0.39494393
0.1813366 -0.45831908 -0.42908813 -0.28361939 0.4375267 0.28364409
0.10815105 0.44983578 0.93033957 0.67199854 0.73570864 -0.53957882
-0.24763567 0.607095 0.3214175 0.77780702 0.26879428 0.72337305
0.00273292 0.75125273 0.5983577 -0.17143417 -0.50254236 -0.73388692
0.58439818 -0.80238263 0.78201108 -0.44669516 0.82072019 0.08796586
0.73403448 0.50851877 -0.68328123 0.92184704 -0.61733405]
2021-12-27 13:32:15,894 - matplotlib.animation - WARNING - MovieWriter imagemagick unavailable; using Pillow instead.
2021-12-27 13:32:15,895 - matplotlib.animation - INFO - Animation.save using <class 'matplotlib.animation.PillowWriter'>
2021-12-27 13:32:31,074 - pyswarms.single.global_best - INFO - Optimize for 15 iters with {'c1': 0.2, 'c2': 0.8, 'w': 0.35}
2021-12-27 13:32:51,326 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.0533333420753479, best pos: [ 6.00140282e-01 1.99537363e-01 5.39496121e-01 -3.77985801e-01
8.23424011e-02 -3.43290817e-01 -5.94244894e-01 3.64616272e-01
6.75298743e-01 1.74630111e-02 -3.34758984e-01 6.17704875e-01
-4.13805899e-01 -1.02339402e-01 6.35253065e-02 -2.49007595e-01
3.73842421e-01 -6.22259055e-02 -1.45928343e-01 1.50104749e-01
-3.33956221e-01 8.50971152e-01 -8.44242970e-01 9.88871818e-02
2.25608902e-01 -3.86878256e-01 -6.20459155e-01 3.64325677e-01
-2.54945736e-01 5.74302454e-01 -2.91271628e-01 -5.97682649e-01
-7.56324799e-02 -7.91573527e-01 1.21479221e-01 -1.84647932e-01
7.79885492e-01 5.56481769e-01 1.28175663e-01 2.95780831e-01
2.39426305e-01 2.94008587e-02 6.61753969e-03 2.67543471e-01
-2.62352197e-01 7.93600405e-01 1.93921577e-02 -4.28742880e-01
-7.30396603e-01 -2.13437449e-01 6.93639181e-01 2.24794721e-01
-2.07682065e-02 3.81176753e-01 4.10780364e-04 -4.81586471e-01
-1.76071388e-01 2.51854847e-01 9.75715601e-02 5.85090389e-01
-1.80666863e-01 2.66015412e-01 5.53287820e-01 -8.65525746e-01
-6.59755858e-01 6.36678878e-01 2.11411819e-01 -3.39907309e-01
4.45722821e-01 -1.55686583e-01 3.39143717e-01 -6.67098466e-01
-4.24589918e-01 -1.71448649e-01 3.03800395e-01 -1.09963871e-01
-7.42746405e-02 -1.49920112e-01 4.19694156e-01 6.36800513e-01
-6.19856419e-01 6.19706438e-01 8.16847086e-01]
2021-12-27 13:32:53,318 - matplotlib.animation - WARNING - MovieWriter imagemagick unavailable; using Pillow instead.
2021-12-27 13:32:53,320 - matplotlib.animation - INFO - Animation.save using <class 'matplotlib.animation.PillowWriter'>
2021-12-27 14:43:05,894 - pyswarms.single.global_best - INFO - Optimize for 15 iters with {'c1': 0.3, 'c2': 0.7, 'w': 0.5}
2021-12-27 14:43:25,788 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.19999998807907104, best pos: [-0.2468042 -0.24411724 0.4129017 0.30645019 0.90373285 0.44013944
0.23351474 -0.21873431 -0.32249028 0.39114235 -0.12713475 0.57613811
-0.79572306 0.49635063 0.61112873 -0.8007812 -0.1720909 0.51081009
0.38581977 0.21224721 -0.00392582 0.0599701 -0.07544748 -0.21032058
0.2254668 0.36399796 -0.26183867 0.37113974 0.52273203 -0.07790917
0.29558787 0.36412862 0.73673779 0.47842214 0.05852025 -0.08803549
0.57475891 -0.71242082 0.13380876 0.18833937 -0.29887117 -0.3404316
-0.20602361 0.49549507 0.28586179 -0.50184238 0.13554478 0.20771953
-0.34036264 0.58917825 -0.64217467 0.50229975 -0.10688611 0.21121129
0.24180869 -0.16437163 0.21708928 0.31926181 0.5094441 0.88834033
-0.52872772 0.4306761 0.01963711 -0.08813769 0.25318685 -0.00754995
0.8857401 0.01372768 0.40886117 -0.0653775 0.28540045 0.42779719
-0.03071681 -0.03044414 0.32906315 0.51420259 0.41354155 0.35587333
0.49042986 0.79412392 0.23132272 0.49668281 0.17143267]
2021-12-27 14:43:58,661 - pyswarms.single.global_best - INFO - Optimize for 15 iters with {'c1': 0.2, 'c2': 0.7, 'w': 0.5}
2021-12-27 14:44:18,178 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.1066666841506958, best pos: [ 0.28966182 0.45250627 0.43416587 -0.278652 -0.15382614 -0.18479012
-0.82378469 -0.4547403 0.55602612 0.45791286 0.02860133 0.37817132
-0.26606156 -0.23997573 0.68266276 0.16401721 -0.21648152 0.31164357
0.78137266 -0.32029621 -0.08514634 -0.29340587 0.16285816 0.23622608
-0.11389426 0.14829542 0.46198782 0.92281261 0.54425843 0.64597913
-0.1754799 0.17731139 -0.44779769 0.14646261 -0.46386435 0.04785579
-0.399135 -0.60586732 -0.41723371 0.38948381 0.4034997 -0.09820677
-0.73776478 0.9304774 -0.58821917 -0.48032035 0.93127301 0.18884777
-0.53450889 -0.76317915 -0.23971849 0.38696161 -0.56075888 -0.25855097
-0.31337059 -0.11677329 -0.03965003 0.38814743 -0.76704242 -0.81657884
0.67770852 -0.05981728 0.08041163 -0.52040466 -0.55172338 -0.55337449
0.10299661 0.15434216 0.0280546 -0.37309802 -0.90421363 -0.43750498
-0.38344901 0.56445772 -0.77567225 0.41375387 0.37271605 0.29244474
-0.06446453 0.86885119 0.28783124 -0.63007402 0.12298526]
2021-12-27 14:44:32,419 - pyswarms.single.global_best - INFO - Optimize for 15 iters with {'c1': 0.2, 'c2': 0.9, 'w': 0.5}
2021-12-27 14:44:53,500 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.07999998331069946, best pos: [-0.01190716 0.36115664 0.30925566 0.56105599 -0.37387753 0.4016454
0.20205158 -0.29294236 -0.02951952 0.01377342 -0.10917486 0.15942885
0.37529249 0.52200116 0.27803875 -0.28109416 0.10787536 0.39554953
-0.25351636 -0.21696629 0.02687271 0.0425818 -0.40002601 -0.09122532
0.47030172 0.07986547 0.20680493 0.04976737 0.74155522 0.72703081
-0.32773362 0.33735662 -0.33351438 0.01503773 0.00610775 -0.10878796
0.51583192 0.39880406 0.51684073 0.74471958 -0.14816833 -0.03897721
0.4112409 -0.67082263 -0.12219378 0.1529787 -0.25224176 -0.05508073
0.15842345 -0.25770947 -0.26928979 -0.02755425 -0.52292714 0.27185011
-0.74397526 -0.29625873 0.99343215 0.50815714 0.67340872 0.03295312
-0.68613905 0.07660079 -0.54076504 -0.38388266 0.31434097 -0.4413432
0.70948061 0.67525894 -0.504181 -0.12063507 0.4732777 0.05058818
0.40771743 0.89793709 -0.74517122 0.19507728 0.10394186 -0.01983183
0.40738195 0.90949763 0.65535238 0.45878406 -0.09882701]
2021-12-27 14:45:22,235 - pyswarms.single.global_best - INFO - Optimize for 15 iters with {'c1': 0.3, 'c2': 1.0, 'w': 0.5}
2021-12-27 14:45:42,215 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.29333335161209106, best pos: [-0.29473107 0.04267827 -0.36842276 -0.69826321 0.0854913 -0.47045524
-0.6781663 0.77010308 -0.26526061 0.42976963 -0.17747598 -0.25823816
-0.69581321 -0.93028234 0.10112303 0.33024186 0.72624807 -0.48972455
0.6633402 -0.95623965 -0.57764335 0.78013233 -0.88032168 0.27277736
-0.72957659 -0.86719558 0.11965377 -0.3320728 -0.02800443 -0.16950215
-0.63258041 -0.40225178 -0.03396038 -0.11740758 0.01607886 -0.70883247
0.69908631 0.55483079 0.74174535 0.53483464 0.45176924 0.50220312
0.76080167 0.54139103 0.62816326 -0.84166954 0.81703463 -0.99677117
-0.86751448 0.04400947 0.02288773 -0.61117122 -0.68083047 -0.75749394
-0.03465576 0.54807221 -0.15704523 -0.27130583 -0.13923121 0.33034809
0.83559039 0.51480579 -0.41578642 0.39517795 0.43667996 0.32273874
-0.50383567 0.26684676 0.91176676 0.03809098 0.9769872 0.93104134
-0.35685476 -0.34027199 -0.62289816 -0.00704368 0.22513556 -0.59491459
-0.23274571 -0.72617151 0.67678801 0.43690897 0.01917605]
2021-12-27 14:45:49,643 - pyswarms.single.global_best - INFO - Optimize for 15 iters with {'c1': 0.3, 'c2': 0.7, 'w': 0.5}
2021-12-27 14:46:09,179 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.18666666746139526, best pos: [-0.22991633 -0.98344331 0.13981579 -0.80638049 -0.61850586 -0.82772819
-0.7960318 -0.5900598 -0.26409838 -0.05317273 -0.9909943 -0.90856326
-0.21515438 0.86740762 0.67650638 0.23486994 -0.80526263 0.13100536
0.7271963 -0.59284224 0.61323308 -0.30509383 0.81240904 0.55506096
-0.59369305 -0.02726469 -0.79159003 -0.36129182 0.06010787 -0.21156148
0.37451736 0.680344 0.69375324 0.33245689 -0.36928186 -0.67376917
0.15701994 -0.40369163 0.11651648 0.33452488 -0.25205739 -0.14220536
-0.83928782 0.35231474 0.04543991 -0.01241857 -0.35525202 -0.6531874
0.26247691 0.3273663 -0.62695097 0.01620549 -0.13512627 -0.29910613
0.51316151 -0.66765386 -0.17700528 -0.24835239 0.89272145 0.71897588
0.37263734 -0.58306777 0.31414853 0.1938119 0.37955031 0.60781724
0.98000403 -0.90964218 -0.20421363 -0.82654934 0.32140048 0.1533377
-0.43223087 -0.93224682 -0.71770139 -0.59340158 -0.08067029 0.1271832
0.91626346 0.92062294 0.45120824 0.29649487 0.33859039]
2021-12-27 14:46:12,643 - pyswarms.single.global_best - INFO - Optimize for 15 iters with {'c1': 0.3, 'c2': 0.7, 'w': 0.5}
2021-12-27 14:46:32,558 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.06666666269302368, best pos: [ 0.36819572 -0.40967853 -0.3183838 -0.27323265 0.18150205 -0.28853268
-0.18861781 0.46827119 0.20693757 0.09198486 -0.00191133 0.13582476
0.03449998 0.7769838 -0.08180328 0.61466533 0.00658026 -0.63058711
-0.39875804 0.07968871 -0.32815355 -0.5157675 0.42628485 0.21509258
0.3109312 0.4690502 0.23599257 0.40238512 0.43025609 0.919412
0.6048394 0.61433315 0.61790024 0.0766576 0.96879601 -0.31814579
-0.88798859 0.04810096 -0.24383667 -0.10128081 0.44805474 -0.02126338
-0.00134503 0.18299918 -0.5765665 0.29809628 -0.53116396 -0.21970845
-0.04957775 -0.1709366 0.23229811 0.01726214 0.19361796 0.053706
-0.31555538 0.25214362 -0.0183354 0.25269382 -0.23929677 0.64471129
0.71380595 0.61292043 -0.53098003 -0.58922683 0.29795282 0.40333406
-0.25095993 -0.29509676 0.59063625 -0.04254306 0.74690152 0.05735515
-0.09308089 0.25766608 -0.04676218 0.49559506 0.29868545 -0.31300491
0.20736713 0.78175586 0.07615263 0.71202067 -0.06347839]
2021-12-27 14:47:10,974 - pyswarms.single.global_best - INFO - Optimize for 20 iters with {'c1': 0.3, 'c2': 0.8, 'w': 0.5}
2021-12-27 14:47:29,710 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.07999998331069946, best pos: [-0.4498062 -0.30246409 0.39208385 0.2718873 0.44222361 0.88706846
0.40795212 0.77696547 -0.45722332 0.43552644 0.31109425 0.51449368
0.7485612 0.48934931 0.57940523 0.78461862 -0.11559151 -0.62587716
0.18161506 0.3414393 0.63080074 -0.55381787 0.79540789 0.49684302
0.58110803 -0.4197936 -0.38426241 -0.19209114 0.75659695 0.49525412
0.74069656 -0.17518657 0.41039377 -0.11060824 -0.40956524 0.5040356
0.19955781 0.61011931 0.06045838 -0.22901987 0.77926548 0.85609028
0.03448528 -0.03521683 0.28458538 0.33480445 0.80357068 0.75555105
-0.16249739 -0.46935231 0.15605742 -0.34870804 0.10754213 0.32191863
0.15397241 0.48423699 0.39822775 -0.42963533 0.47465456 -0.23068544
-0.32628283 0.3852531 -0.41385996 -0.08006606 -0.30455312 0.71286812
0.84595204 0.35910328 -0.17016029 0.22397322 -0.80285276 -0.38449656
0.7531546 0.72891211 0.87247215 -0.21933949 0.19538717 0.32848999
-0.2428129 0.33511362 0.62359336 0.47917543 0.08175301]
2021-12-27 14:47:46,171 - pyswarms.single.global_best - INFO - Optimize for 20 iters with {'c1': 0.3, 'c2': 0.8, 'w': 0.5}
2021-12-27 14:48:03,965 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.3333333134651184, best pos: [ 0.09365613 0.98022946 0.48379901 -0.14483807 -0.48223007 0.44800003
-0.44367567 -0.15658325 -0.59383612 0.32222266 -0.56223931 0.1118812
0.25724424 0.05173023 -0.591798 -0.40365229 0.82174825 0.10335747
0.68636853 -0.66503053 0.77155742 -0.52156236 0.49025455 -0.5336973
0.2612 -0.03623317 -0.13858955 0.31861205 0.39656822 -0.24447041
0.1418439 -0.59911151 -0.62235702 0.90079811 0.08623204 0.07967098
-0.71666502 -0.23617168 -0.62056106 0.21651175 0.54120627 0.18821532
0.45028587 0.83567487 0.01632829 0.36587522 -0.03283039 0.51201217
-0.66117762 0.77958058 -0.71501233 0.346009 -0.34044298 -0.02698635
0.34816149 0.7980558 0.33104762 0.8024259 -0.28018233 0.20228665
-0.43671167 0.72615613 0.13972528 0.03195389 -0.64170035 0.15435992
0.02222907 -0.69302639 0.73506774 -0.55617605 -0.25197684 -0.94776343
0.43065689 0.35500114 -0.19747487 -0.19289565 0.07155325 -0.12164826
0.40355183 -0.12895343 0.7052657 0.20413183 0.31842181]
2021-12-27 14:48:30,639 - pyswarms.single.global_best - INFO - Optimize for 20 iters with {'c1': 0.3, 'c2': 0.6, 'w': 0.5}
2021-12-27 14:48:48,233 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.19999998807907104, best pos: [-9.31128444e-01 5.44359553e-01 1.86716090e-01 -7.88326618e-02
5.09479510e-01 8.45056721e-01 -1.65106601e-01 4.13118955e-01
-5.46427751e-01 -2.65327986e-01 -1.73320953e-03 -4.30668929e-01
-2.74152652e-01 -5.88267049e-02 -4.85118127e-02 7.45790490e-01
-5.78784703e-01 -4.06433759e-01 -1.34338176e-01 7.89712086e-02
6.43344051e-01 -6.38168558e-01 -4.47960073e-01 3.61000776e-01
-7.88377081e-01 3.78819815e-01 -6.35229442e-01 2.47084470e-01
-8.25086291e-01 -4.87624867e-01 -9.38988546e-01 -6.75355927e-01
1.12829269e-01 5.66751215e-01 -6.92759180e-01 -7.17352160e-01
7.23909516e-01 -9.90231004e-02 9.38119760e-01 1.36556867e-01
-6.58028197e-01 -1.05355618e-04 6.97758906e-01 -2.30772826e-01
-5.49632236e-01 4.89319359e-01 4.48198068e-01 7.29572598e-01
-3.75519744e-02 5.35267123e-01 -2.51731543e-01 -9.37085918e-01
-3.48043702e-01 8.01815957e-01 7.42289096e-01 2.42072081e-03
-3.13202334e-03 7.70627428e-01 -3.54829585e-01 -3.85516937e-01
-2.94535935e-01 1.23094351e-02 2.88582873e-01 2.62212581e-01
-2.76160011e-02 9.66048601e-02 -6.94407847e-01 5.84314578e-01
8.90629475e-02 -4.98323509e-02 7.95437554e-01 -2.34918453e-01
-2.11011763e-01 -2.28069782e-01 -7.61012496e-01 -1.52829310e-01
-6.90248659e-01 -8.97419446e-02 -2.21218841e-01 -5.42206147e-01
2.73012165e-01 2.14618776e-01 1.58351769e-01]
2021-12-27 14:49:00,631 - pyswarms.single.global_best - INFO - Optimize for 15 iters with {'c1': 0.3, 'c2': 0.6, 'w': 0.5}
2021-12-27 14:49:20,462 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.06666666269302368, best pos: [ 0.22545308 0.87672022 0.35665654 0.22887652 0.16194167 -0.23077973
-0.63447809 0.12503514 -0.14643256 0.74849617 0.59188637 -0.05257858
-0.15701932 0.55097784 0.24638514 0.29592627 -0.05398625 0.1598681
0.22097327 -0.00827419 0.02091296 0.13951267 0.10085786 -0.08667713
0.00137319 -0.44757309 0.53292956 -0.17801448 0.34713751 0.3180601
-0.25177654 -0.5871957 -0.27057626 -0.11866551 0.07418241 0.3359912
0.43476424 0.27334784 0.35198237 0.62150536 0.02854309 -0.00976363
0.53719189 -0.06929995 0.12827441 0.3712091 0.76451684 0.25584442
0.12352474 -0.47663038 0.48394558 0.01133888 0.56768916 0.15170875
0.2779047 -0.23185455 0.27307058 0.31055593 0.26334401 -0.46244068
0.47299117 0.07457105 0.45070819 0.43520534 0.227312 0.83277638
-0.14255519 -0.14802559 -0.38185156 0.1358361 -0.30655809 -0.08124036
0.55849315 -0.43905748 0.20475512 -0.04792657 0.84232798 -0.19311355
-0.26445173 0.59340355 0.25533571 0.35135273 0.38199461]
2021-12-27 14:49:38,751 - pyswarms.single.global_best - INFO - Optimize for 15 iters with {'c1': 0.4, 'c2': 0.6, 'w': 0.6}
2021-12-27 14:50:02,088 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.06666666269302368, best pos: [-0.60220676 -0.58286835 -0.01567905 0.20074938 -0.36720154 -0.09639719
-0.12604942 -0.54381252 -0.27819096 -0.37938088 0.44392741 -0.33052096
0.5697465 0.09962912 -0.46000889 -0.15415693 -0.07261318 -0.008388
0.22016477 -0.50629476 -0.47642232 0.17478048 0.72267413 0.05016081
-0.22719057 -0.33016732 -0.44551889 -0.94743423 -0.44095533 0.08304262
-0.42659795 -0.97587801 0.03294494 -0.62015355 -0.10185528 0.63340578
0.5239142 0.22879632 -0.15914099 0.4758752 0.38671455 0.53540241
0.21153889 0.48580751 -0.59700915 -0.14559216 -0.22316893 -0.30440288
0.50462802 0.5360252 0.95059384 -0.45803778 -0.17108415 -0.30905583
0.61771562 -0.09099565 -0.92827815 -0.58449117 0.74969179 0.5600015
-0.07500971 -0.51113066 0.61368111 0.41481536 -0.11308684 0.14367641
-0.27911472 0.58388655 -0.04584122 0.20461829 0.19285598 0.16170988
-0.22855619 -0.68686918 0.16305326 0.0617621 0.39546242 0.35844034
0.05815138 0.37705539 -0.04471007 0.56365004 -0.26635895]
2021-12-27 14:50:18,514 - pyswarms.single.global_best - INFO - Optimize for 15 iters with {'c1': 0.4, 'c2': 0.6, 'w': 0.6}
2021-12-27 14:50:37,330 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.07999998331069946, best pos: [ 0.51427521 0.60889124 -0.8602218 0.77254627 0.64788968 -0.03314936
-0.38754057 -0.3556006 -0.14447145 0.43084996 0.02688447 0.43945201
-0.12306563 0.04472649 0.06273852 0.0777083 -0.43466979 -0.91057681
-0.41187101 -0.37442153 -0.42212571 0.55871562 0.27965007 -0.331097
0.05005154 -0.5455402 0.2233374 0.07498371 0.43121884 0.40293527
0.35504209 0.51602476 0.9797599 0.86781787 0.69808377 0.20457581
0.09522149 -0.56125847 -0.44949306 0.91639907 -0.4532775 0.28316198
-0.17693002 0.35357579 0.06611036 -0.46229452 0.93250986 0.24458961
-0.65350846 0.8571527 0.02759454 -0.72847444 0.26540274 -0.3033425
0.02545591 0.30341852 -0.42208895 0.10276525 0.90112653 -0.1973952
0.78845105 0.56899699 0.81574803 -0.34112934 0.79621893 0.45055221
0.78507066 0.30382138 0.31586447 0.31764874 -0.05500519 -0.147197
-0.13357714 0.04807603 0.27616171 0.19975097 0.88569341 0.05161933
0.64761814 0.40498162 0.39155726 0.02610184 -0.46975417]
2021-12-27 14:51:00,174 - pyswarms.single.global_best - INFO - Optimize for 15 iters with {'c1': 0.4, 'c2': 0.6, 'w': 0.6}
2021-12-27 14:51:20,286 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.0533333420753479, best pos: [ 0.506638 -0.17648458 0.33916372 -0.45322381 -0.03338564 0.14846573
-0.37809668 0.36651767 0.30824086 -0.59592735 0.22352675 0.92805672
0.55621078 -0.29255502 0.6764515 0.14071336 0.19835446 0.05830566
0.62671337 0.11193747 0.2530154 0.33376244 0.05063003 0.46240404
-0.03983307 0.34907935 -0.06688962 0.11192596 -0.11177232 0.39401537
0.58025136 -0.40254297 -0.37193385 0.21301378 0.64056757 -0.48993506
-0.76721091 0.26001754 -0.60046713 0.62717815 -0.10173074 0.41800279
0.0035946 -0.71556841 -0.00138043 0.22938558 0.02984154 0.21762943
0.57038788 -0.2525416 -0.39343682 -0.26037357 0.57159442 0.71310208
0.10055828 0.04374227 -0.04756798 0.00400712 -0.45025011 -0.48501882
0.00228613 -0.11289701 -0.57786029 0.57926917 0.15579991 0.30726137
0.15788747 0.46563075 0.92284979 0.357428 -0.36145792 0.17684252
-0.10826783 0.16305172 0.81468411 0.31354053 -0.55813085 0.28717673
0.3093727 0.33022389 -0.19556021 0.3934854 0.0754949 ]
2021-12-27 14:51:38,780 - matplotlib.animation - WARNING - MovieWriter imagemagick unavailable; using Pillow instead.
2021-12-27 14:51:38,781 - matplotlib.animation - INFO - Animation.save using <class 'matplotlib.animation.PillowWriter'>
2021-12-27 14:52:49,240 - matplotlib.animation - WARNING - MovieWriter imagemagick unavailable; using Pillow instead.
2021-12-27 14:52:49,242 - matplotlib.animation - INFO - Animation.save using <class 'matplotlib.animation.PillowWriter'>
2021-12-27 14:53:37,270 - matplotlib.animation - WARNING - MovieWriter imagemagick unavailable; using Pillow instead.
2021-12-27 14:53:37,272 - matplotlib.animation - INFO - Animation.save using <class 'matplotlib.animation.PillowWriter'>
2021-12-27 14:53:43,680 - matplotlib.animation - WARNING - MovieWriter imagemagick unavailable; using Pillow instead.
2021-12-27 14:53:43,682 - matplotlib.animation - INFO - Animation.save using <class 'matplotlib.animation.PillowWriter'>
2021-12-27 14:54:03,035 - matplotlib.animation - WARNING - MovieWriter imagemagick unavailable; using Pillow instead.
2021-12-27 14:54:03,037 - matplotlib.animation - INFO - Animation.save using <class 'matplotlib.animation.PillowWriter'>
2021-12-27 14:54:42,248 - matplotlib.animation - WARNING - MovieWriter Pillow unavailable; using Pillow instead.
2021-12-27 14:54:42,249 - matplotlib.animation - INFO - Animation.save using <class 'matplotlib.animation.PillowWriter'>
2021-12-27 14:54:54,431 - matplotlib.animation - WARNING - MovieWriter imagemagick unavailable; using Pillow instead.
2021-12-27 14:54:54,432 - matplotlib.animation - INFO - Animation.save using <class 'matplotlib.animation.PillowWriter'>
2021-12-27 14:55:27,272 - matplotlib.animation - WARNING - MovieWriter PillowWriter unavailable; using Pillow instead.
2021-12-27 14:55:27,273 - matplotlib.animation - INFO - Animation.save using <class 'matplotlib.animation.PillowWriter'>
2021-12-27 14:55:40,606 - matplotlib.animation - WARNING - MovieWriter pillowwritter unavailable; using Pillow instead.
2021-12-27 14:55:40,607 - matplotlib.animation - INFO - Animation.save using <class 'matplotlib.animation.PillowWriter'>
2021-12-27 14:57:00,467 - matplotlib.animation - WARNING - MovieWriter PillowWriter unavailable; using Pillow instead.
2021-12-27 14:57:00,468 - matplotlib.animation - INFO - Animation.save using <class 'matplotlib.animation.PillowWriter'>
2021-12-27 14:58:22,515 - matplotlib.animation - INFO - Animation.save using <class 'matplotlib.animation.FFMpegWriter'>
2021-12-27 14:58:22,517 - matplotlib.animation - INFO - MovieWriter._run: running command: ffmpeg -f rawvideo -vcodec rawvideo -s 600x600 -pix_fmt rgba -r 6 -loglevel error -i pipe: -filter_complex 'split [a][b];[a] palettegen [p];[b][p] paletteuse' -y pso.gif
2021-12-27 14:58:39,759 - matplotlib.animation - WARNING - MovieWriter pillowwritter unavailable; using Pillow instead.
2021-12-27 14:58:39,760 - matplotlib.animation - INFO - Animation.save using <class 'matplotlib.animation.PillowWriter'>
2021-12-27 14:59:04,307 - pyswarms.single.global_best - INFO - Optimize for 15 iters with {'c1': 0.4, 'c2': 0.6, 'w': 0.5}
2021-12-27 14:59:23,496 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.1066666841506958, best pos: [ 0.21363629 0.10284107 -0.0531844 -0.21360044 0.68153599 0.86949142
0.02660094 0.28534483 0.71417217 0.33079357 -0.50865138 -0.55540479
-0.03361574 0.69170197 -0.43799696 -0.19868655 -0.22427096 0.56516412
-0.3584276 -0.06300797 -0.02576565 0.15673745 0.15286778 0.36760314
0.03207925 -0.11678929 -0.10987874 0.24944999 -0.79786928 0.57747453
-0.17145641 0.29712761 0.75921305 0.03759562 -0.03661941 0.63904857
-0.00679173 0.28775498 0.84303302 0.31337534 -0.34978741 0.23470839
-0.72071763 0.91006642 0.4538063 -0.46325465 0.50114949 -0.11479783
0.6948953 -0.33461569 -0.04956151 -0.20571767 -0.74335991 -0.58686616
0.66713557 0.820854 0.52319445 -0.23362581 -0.09922476 0.47073095
0.70284343 0.67826192 0.19675698 0.42653035 0.76924107 -0.06965439
0.38209472 -0.19611896 0.01002113 0.64923309 -0.40559179 0.18168842
-0.62768471 -0.02643331 -0.19030231 0.28330557 0.09248034 -0.07631875
-0.59268357 0.54967766 0.27072596 -0.56329765 -0.43685239]
2021-12-27 14:59:33,888 - pyswarms.single.global_best - INFO - Optimize for 15 iters with {'c1': 0.4, 'c2': 0.7, 'w': 0.5}
2021-12-27 14:59:53,570 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.07999998331069946, best pos: [ 0.24213213 0.52212875 0.16258733 -0.34922292 0.0584716 0.63715827
0.11128917 -0.52636114 -0.26698424 -0.30192752 0.11055153 -0.16643606
-0.06001551 -0.00604149 -0.48967647 -0.53812369 -0.60281073 0.10248906
0.1155675 -0.12289388 0.18559269 0.34860831 0.49768922 0.36800446
-0.58994828 -0.3063584 0.65909853 0.45944843 0.88075252 -0.17067047
0.82428988 -0.36408595 -0.1816687 0.08911258 0.34694604 0.71988778
-0.06938773 0.17155329 -0.42383912 0.64260312 -0.31800528 0.02937588
-0.36068021 -0.73509229 0.15419399 -0.49092026 0.38822386 0.00108065
0.29835819 0.7108881 -0.0535121 -0.25808607 0.47772965 -0.40609369
-0.03368905 -0.39475588 0.53425016 0.72977662 -0.22440167 0.02008445
0.55141082 0.14733887 -0.06010469 -0.09826619 -0.51841476 0.05345355
-0.22042619 -0.10750446 -0.76696586 0.72910013 0.91020188 0.85438487
-0.27423583 0.31358589 -0.61960963 0.01503131 -0.09287613 -0.1246309
0.06926968 -0.50404885 0.69237201 0.04574503 0.1820619 ]
2021-12-27 15:00:42,770 - pyswarms.single.global_best - INFO - Optimize for 15 iters with {'c1': 0.4, 'c2': 0.8, 'w': 0.4}
2021-12-27 15:01:02,524 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.013333320617675781, best pos: [ 0.27911979 -0.55757932 0.2766565 0.35975281 0.72009114 0.7119445
0.5776927 0.52077264 0.67504513 -0.49985279 0.15561242 0.49470757
0.07110754 -0.02335194 -0.13379514 -0.25769252 0.8009418 -0.09804629
-0.348635 0.69365033 0.48507372 -0.46965597 0.28400862 -0.36275778
0.40690129 0.39743728 0.20008803 0.44225043 0.14224802 0.37116319
-0.72829762 -0.07267608 0.63930366 0.29554009 0.39164875 -0.47153567
0.58262708 0.88472097 0.48328034 0.82359525 0.69474802 0.57502965
-0.31953482 -0.16788879 0.3410064 0.67655062 -0.14132718 0.21596682
-0.47949632 0.06507222 0.38206743 0.42513836 0.55036095 0.72995095
-0.00268799 -0.22573595 -0.59954994 -0.11589021 0.26917711 0.19832152
0.32279789 -0.18612947 -0.56184357 0.48579297 0.31049338 -0.27212689
0.2621756 -0.20972623 -0.03790394 0.34582685 0.24009114 0.01905155
-0.50147889 0.08106092 0.54838046 0.52252921 0.64339103 -0.63617195
0.52122359 0.4000399 0.33933303 0.70273747 0.47594656]
2021-12-27 18:03:22,946 - matplotlib.animation - WARNING - MovieWriter pillowwritter unavailable; using Pillow instead.
2021-12-27 18:03:22,948 - matplotlib.animation - INFO - Animation.save using <class 'matplotlib.animation.PillowWriter'>
2021-12-27 19:38:03,650 - pyswarms.single.global_best - INFO - Optimize for 15 iters with {'c1': 0.4, 'c2': 0.8, 'w': 0.4}
2021-12-27 19:38:24,781 - pyswarms.single.global_best - INFO - Optimize for 15 iters with {'c1': 0.4, 'c2': 0.8, 'w': 0.4}
2021-12-27 19:38:39,016 - pyswarms.backend.generators - ERROR - Bounds and/or init_pos should be of size (413,)
Traceback (most recent call last):
File "/usr/local/lib/python3.8/dist-packages/pyswarms/backend/generators.py", line 81, in generate_swarm
pos = center * np.random.uniform(
File "mtrand.pyx", line 1131, in numpy.random.mtrand.RandomState.uniform
File "_common.pyx", line 562, in numpy.random._common.cont
File "_common.pyx", line 479, in numpy.random._common.cont_broadcast_2
File "__init__.pxd", line 742, in numpy.PyArray_MultiIterNew3
ValueError: shape mismatch: objects cannot be broadcast to a single shape
2021-12-27 19:38:47,466 - pyswarms.single.global_best - INFO - Optimize for 15 iters with {'c1': 0.4, 'c2': 0.8, 'w': 0.4}
2021-12-27 19:39:06,241 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.30666667222976685, best pos: [-0.32386678 -0.07322314 -0.35442357 0.54252245 0.13324419 -0.25143025
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2021-12-27 19:39:28,959 - pyswarms.single.global_best - INFO - Optimize for 5 iters with {'c1': 0.4, 'c2': 0.8, 'w': 0.4}
2021-12-27 19:39:53,980 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.29333335161209106, best pos: [ 0.82772038 -0.13968144 -0.38491818 0.56417416 0.5631312 -0.53276349
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2021-12-27 19:40:22,661 - pyswarms.single.global_best - INFO - Optimize for 5 iters with {'c1': 0.4, 'c2': 0.5, 'w': 0.8}
2021-12-27 19:40:46,239 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.29333335161209106, best pos: [-8.11154932e-01 6.53511495e-01 -2.22872660e-01 -7.39702607e-01
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2021-12-27 19:40:56,018 - pyswarms.single.global_best - INFO - Optimize for 25 iters with {'c1': 0.4, 'c2': 0.5, 'w': 0.8}
2021-12-27 19:41:20,770 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.19999998807907104, best pos: [-2.35292454e-01 -1.26241661e-01 -1.34654851e-01 9.40110076e-01
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2021-12-27 19:41:57,800 - pyswarms.single.global_best - INFO - Optimize for 25 iters with {'c1': 0.4, 'c2': 0.5, 'w': 0.6}
2021-12-27 19:42:55,985 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.04000002145767212, best pos: [ 0.30876268 -0.31455976 0.41867619 0.34106923 -0.5301903 -0.63751163
0.00726144 0.11677877 0.33874853 0.14720319 0.65270724 0.29387629
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2021-12-27 20:17:17,160 - pyswarms.single.global_best - INFO - Optimize for 15 iters with {'c1': 0.4, 'c2': 0.8, 'w': 0.4}
2021-12-27 20:17:34,399 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.026666641235351562, best pos: [ 0.13146149 0.34448768 0.43372759 0.43013445 0.47076012 0.48541969
0.21558869 0.36753802 0.09343448 0.2265042 0.63224091 -0.00277912
0.22269271 -0.25046721 0.10962587 -0.04075956 -0.62986226 0.85388118
0.50769034 0.34109791 -0.00175132 -0.52779507 0.26711675 0.32152433
0.02351116 0.17823647 0.74820644 -0.41775743 0.29035641 0.28829481
0.18969641 0.41687872 0.04831428 0.38135823 0.72875706 -0.35311555
-0.71833168 0.52049789 0.25750087 0.89733974 0.17374207 0.11369666
0.44444548 0.10465407 -0.26030466 -0.0303583 -0.10071675 0.5321397
-0.05437313 0.1808118 0.46453241 -0.19452634 0.10588383 -0.0948509
-0.00572402 -0.20996948 -0.38838664 -0.22462156 -0.06986236 -0.47180943
-0.44742323 0.07108898 -0.19574616 0.25701307 0.29128843 -0.05892173
0.15987782 -0.50574763 -0.19575293 0.71100111 0.49871583 0.19796978
0.17130482 -0.18939163 -0.07254241 -0.12724571 0.05084781 -0.47833918
0.12547059 0.65795364 0.6939646 0.64000431 -0.25180211]
2021-12-27 20:17:44,585 - pyswarms.single.global_best - INFO - Optimize for 15 iters with {'c1': 0.4, 'c2': 0.6, 'w': 0.4}
2021-12-27 20:18:02,908 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.14666664600372314, best pos: [-5.02414563e-02 -4.66550885e-01 -4.65182144e-01 -4.84688896e-01
7.07151801e-01 6.87499643e-01 -4.38983488e-01 -1.22295409e-01
4.96086821e-01 -8.09544342e-05 4.78157204e-01 -1.39129958e-01
1.31996537e-01 -3.87472905e-01 -3.68916718e-01 -5.96245380e-01
1.22406173e-01 -2.89158318e-01 9.07750411e-02 4.08805549e-01
9.02857624e-02 5.37370487e-01 -2.96640486e-02 6.23382279e-01
1.56977823e-01 2.96645025e-01 7.00856087e-01 8.63271932e-01
7.40069587e-01 6.22898074e-01 5.87794988e-01 6.28132673e-01
-1.88285366e-01 2.93577746e-01 5.03478801e-01 -4.41528201e-01
1.23657572e-02 1.02113993e-01 3.17262684e-02 -9.34181467e-02
4.28074715e-01 -4.52612976e-01 -1.01905005e-01 5.69183506e-01
-4.25166682e-01 -1.04885058e-01 1.54321902e-02 4.78869502e-01
7.25227197e-01 7.13959328e-01 4.36310020e-01 -8.29934057e-02
1.34359070e-01 2.64952597e-01 -3.66103016e-01 6.40814565e-02
2.67097405e-01 -1.45647087e-02 -2.21034718e-01 1.06275924e-01
6.45511825e-01 7.23246526e-01 1.40472182e-01 -7.46182244e-01
8.60350078e-01 1.96540897e-01 3.93784254e-01 3.06902039e-01
-2.97859057e-01 2.90257952e-01 -3.18281420e-01 4.71531380e-01
7.77546901e-01 -3.79849538e-01 -6.38659858e-01 -3.41165645e-01
8.12077371e-01 3.98941358e-01 3.26836196e-01 4.84056846e-03
5.44025985e-01 5.61951980e-01 -5.01430772e-01]
2021-12-27 20:18:24,792 - pyswarms.single.global_best - INFO - Optimize for 15 iters with {'c1': 0.4, 'c2': 0.7, 'w': 0.4}
2021-12-27 20:18:41,685 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.07999998331069946, best pos: [-0.75733116 0.05340879 0.66992863 0.60881426 0.55454712 -0.20739635
0.39059609 -0.29126187 -0.76595297 0.15884227 0.07995326 0.69362904
0.68466348 -0.78957912 -0.13480058 0.37166663 0.28758522 -0.58852409
-0.87630979 -0.58504967 -0.68551357 0.03436954 -0.23226779 -0.38582991
0.54289781 -0.15730104 -0.22004645 -0.28010953 -0.03698392 -0.43921471
0.20900266 -0.28898702 0.01477884 0.33367077 0.55222298 -0.37080241
0.22268408 -0.11660796 0.46385081 0.04359757 0.25146723 -0.43703963
0.0501425 0.47803675 0.3266234 0.26048338 -0.06472018 -0.1388914
0.55914889 -0.20414418 0.49794259 -0.00648258 0.11026706 0.23101385
0.15138014 0.1880529 0.1177522 0.23159763 -0.94149946 0.29994506
0.17665214 -0.30864325 -0.19058251 -0.14105112 0.51184662 0.46240839
-0.33393123 -0.47509206 0.26513484 -0.04328845 -0.02192006 0.04130614
-0.49392233 0.0221075 -0.15433906 0.28235837 -0.32589686 -0.09143159
0.46517639 0.12689952 -0.37113475 0.13226123 0.3025991 ]
2021-12-27 20:31:59,762 - pyswarms.single.global_best - INFO - Optimize for 15 iters with {'c1': 0.4, 'c2': 0.7, 'w': 0.4}
2021-12-27 20:32:16,836 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.1499999761581421, best pos: [ 3.61646993e-02 7.57736737e-01 8.09693873e-01 7.53030227e-01
-2.07580885e-01 -5.20806267e-01 -3.22240624e-01 1.87312345e-01
-5.23003400e-01 4.43155402e-03 8.52065425e-01 -4.33461899e-01
-9.23723097e-04 4.56394966e-01 7.39598499e-02 3.80980516e-01
7.47560982e-01 1.76068521e-01 5.58881305e-01 -6.15799820e-01
-2.11584552e-01 8.86154245e-01 -4.56415266e-01 1.45173869e-01
-4.73364157e-01 -8.54184629e-01 -1.58615647e-02 7.45683894e-01
7.26001016e-01 6.22520547e-02 2.27229241e-01 4.14895653e-01