" !" ;&#% ;" :; ; ".'. // & " > !.+. 8% 660014, :, . " «: », 31, 8 vladimirzvonkov802@yandex.ru : +7-913-585-69-35 : , , , Abstract The most perspective and developing global optimization methods are competing methods, such as genetic algorithm (and its varieties) and particle swarm optimization algorithms at the modern stage of global optimization methods development of multiextremal functions, nondifferentiable functions, ravine surface functions and other complex criterion functions (functional, quality criteria, algorithmically and table of predefined functions) for classical methods optimization. The particle swarm optimization algorithms are studied to a lesser extent than genetic algorithms, many questions of tuning and the use of this approach are still remain uninvestigated, which is very important and the author of this research is interesting in this approach, as well as, probably, scientific community. In this scientific research two competing methods are compared and the correlation of some parameters of PSO and numerical efficiency criteria is obtained by author. "/0 < " # 9 " . 1975 " = " [1], " " 9 # # " , . <" " [2] «" "», # 9 " " , .. =. «< " , #$ # > ## % , # . < " – , " . > # » [3, c.125]. < " % [1]. " " % , , [4]. [5] > # ". 20-" "% , " " # % [6, 7, 8, 9, 10, 11, 12, 13, 14]: 9 , , , 9, " , , , #, "9 , $# K!, " " , 9, .. K # " # % " ( NP NP-) % - 786 , 9 " " , , , 99, . " ( 9, 9 ), , , # " ", .. % % # %# " (") # () [15, 16, 19]. " " # # , % %, F;8. ! (Particle Swarm Optimization) #$ 1995 ": . : .:. K [17]. B" PSO, " ", , $ #$ , , " . B" PSO "" " " (, , $ ). & " " , " - , $ ( ) ( "" " ). # # " 9, " (% "" , ) " (% "" , ) . @ $ " " PSO #: " # , " , , , (, .), " , ( , .). >99 $ " " PSO % . ;# - (.. " } ), % " PSO " , " %# " - : Eberhart R. C., Kennedy J., Simpson P. K., Dobbins R. W, Jenigiri S, Salerno J., Ozcan E., Mohan C., Clerc M., Fukuyama Y., Takayama S., Nakanishi Y., Yoshida H., Shi Y., Dozier G., Homaifar A., Tunstel E, Abido M. A., Adly A. A., Abd-El-Hafiz S. K., Kannan S., Slochanal S. M. R., Subbaraj P., Padhy N. P. " # > " " , # , $ K!, % # % " . M " " , " $ . @ "% $ # 9 % , #$ " , $ . 1 @ 4*2 / 40 E 4 ; #$ . ; $ 9, , . (1): 787 & f ( x ) o opt ; & & x ( x1, x2 ,..., xn ) X – % n, f (x ) - 9 ( 9, , " ). 9 " ( 2- 20-) $ ( 7- 25- ) [18] 23- 9 (, ">, "). # #$ : > , #$ "", " , " , , 9 , , 99 " , #$ " " , #$ >99 " . % MathCAD 14 ( , " > ) # %, > # ", MathCAD 14. L % " ", " 9 " " [19] ( " $ " ). ; " " > 48 (9 8", $ " 2, $ 0.000001) 600 (9 < , [-512; 512], $ " 20, $ 0.000001). " " " . ; $ . ; $ 9, . @ 9 " " () . @ ", . 9 (2). & f ( x ) o opt & (2) ­°a i ( x ) d 0, i 1, p ; ® & °̄bi ( x ) 0, i p 1, z & x ( x1, x2 ,..., xn ) X – % n, #$ z "& & & & & (p a ( x ) (a1 ( x ), a 2 ( x ),..., a p ( x )) d 0 z-p & & & & & & b ( x ) (b p 1 ( x ), b p 2 ( x ),..., bz ( x )) 0) , f (x ) - 9 ( 9, (1) , " ). " " " . Q : " " " %9 9 (, , , %9) " (, ). % 3- , . > #$ " " : – 100 300; – 100 300; 788 $ – 0.99; " – 500 1000. 8 [18]. $ ( "" ): 0.000001. >99 " $ #$ >99 : , , 9 . 8 , " . , .. , $ " &; " &, .. $ &, .. $ >99 n , " n=500, n=1000 – } " ). n 1 O - % , " #, $ " . & - ( 9, 9 "), " > #. * – ( ) " , ( ) # } " " , ". >99 " > , ##$ " " ( 1), #$ >99 " Statistica , !-Q, " ANOVA ( # >99 ", 2). & " " #$ . 1) & # : , ; 2) : (), (2), (4), (9), " ; 3) $ : , , ; 4) : , , ; 5) & 9 " : , % ; 6) : > (1 % #$ ) . , 360 " "" " 9 , ". M9 " 1. : " " 8 ": %9, %9, %9, «» %9, , «», «2», «+ %9», «2+ %9». $ " #$ " . @ 789 " " 9 . 5 % " # " " [24]. 1 – : " " . "" " & (Holland, Goldberg) B (L ) B $ (L ) B (L ) B $ (L ) B (L ) B $ (L ) B , $ (L ) ' 360 120 120 72 Q 1800 600 600 360 40 200 24 120 24 120 8 40 - 2 – ; & " " & #$ " " ( , , $ ) ; " " " " Statistica. ; " " #$" " Statistica. : " ( >99 , 2 " " 1000 ) > , " " " "" >99 " >99 " : , , 9 ( #$ >99 ). 790 @ #$ > % % $# " " PSO [17]. : 9 $ " $ " PSO 3. . Q " $ # " % >99 ( , , ). @ " 4. 3 – 8 " PSO 8 $ " 2 5 10 20 8 : : 100 100 300 300 1000 1000 5000 5000 : ( 9) 10 000 90 000 1000 000 25 000 000 4 – + " """ " " @ Ñ1 + 2 Ñ2 2 ; " " PSO 0.72 1000 «L » $ >99 " PSO $ >99 , > # " . >99 , " " >99 % . & * – 9 ( ) 9, " 9 . & * – > " , " " %, * . & – 9 ( ) * " (%") " " % " "" >. & – > " * " (%") " " % . " >99 " >99 - 791 " . B" # > # " . } " > ( 9 " , 9 ) # ", 9" " " 9 , " $ 9 " " # " ". 2 F 4, F44 4, F44 - B $ 9 " (Intelligence Technologies – Self-Adapting Genetic Algorithm (ITSAGA) - | " " " K! 8 2011611120 3.02.2011, " " > #" , , " ), #$ &;;8, > . " ## #$ " : " " (<B) % [19]; 5 " [20, 21] (%9 9 ); <B % " [19]; SPEA [22]; " ; " " ; " +. 8 $ (- , - , " , 9, .); (" " %, 9 " 9, #$ " ", " " "); (9 % 9 ) (M@&); M@& 9 $# 9 <B; <B M@& ; " " " , >99 , "9 <B, , , 9 ;; M@& > # % <B; "9 9; ; 9 - 9; " 9 " "" % " . 8 # (.. " " 9 #, ), # ( $# CppCheck) >99 , ( 2- 4- 9 ;: # , ) (Windows, Linux) 3- 9" K! ( Acer Extensa 5620, ;: A; Intel Core i7-920 (Bloomfield) 2.66<<, 3.6 <</ Cooler Master GeminII / Gigabyte GA-EX58-UD3R (rev. 1.0)/ '; Chieftec [APS-500S] 500W, ATX v2.2, EPS12V, Active PFC/ Radeon HD 4550 / LQ 2 * DIMM DDR3 2048MB PC10666 1333MHz Kingston/ 3.5” Hitachi 160 <, ;: c A; AMD Athlon-64 2800+ 1.8 <<, 2.2 <</ Radeon 9600 Pro / LQ DDR 512 ! / 3.5” Hitachi 160 <). :#$ 9" () 9" # , $# - 792 (Memtest, Everest, 3DMark, PC Mark, Linpack, Perfomance Test, SiSoftware Sandra, SpeedFan, System Stability Tester, Video Memory Stress Test .) % . B " «B % " "" " (GOLEM-SA)», | " " " K! 8 2011611158 4.02.2011. GOLEM-SA % " , "" " " (*9 &.@. . B " "" "/ &.@. *9 , *.&. &, ;.. <%, M.B. ;9 / ;" , |3, 2011). ! " , GOLEM-SA: " , " ", " ", (Particle swarm optimization, PSO), =- . B =- PSO (J. Kennedy, R. Eberhart). 3 6* 3 E 4/ " " 9 ". 5 " "" ", " " PSO 9 " [19], [24], [26] 23- ( $ " 2) , " . 8 [19] #$ <B # % % CEC-2005. 5 – 8 " 23- (1000 " ) " < " : <B (Holland J. H., Goldberg D.E.) B (L .'.) B (L .'.) B $ (L .'.) B (L .'.) 8 , 8 (c./1000), ". 8 , 0" (" " #) 8 " 9 ( " #) [0, 0.997] [22, 75] [20, 86] [2148, 8726] [0.684, 1] [34, 69] [24, 60] [2463, 6112] [0, 0.999] [21, 63] [21, 87] [2153, 8790] [0, 1] [28, 76] [22, 87] [2337, 8815] [0.862, 0.993] [33, 67] [27, 40] [2803, 4077] 793 B [0, 0.996] [27, 70] [22, 83] [2321, 8397] $ (L .'.) B [0.237, 1] [36, 61] [28, 69] [2883, 6989] $ (L .'.) & #$ [0.925, 1], [39, 61] [18, 27], [1888, 2801], <B (L .'.), [19] 0.954 22 2250 4 /L +2 [0.953, 1], [47, 76] [5, 15], [1128, 3204], ; + *+ 0.982 11 2104 (// ".'.), [19] :> # "[0.24, 1], [26, 50], [2580, 4960], 0.83 33 3259 (&" ( ( ( 8.'.), 8 [23]) [23]) [23]) [23] & " PSO (J. Kennedy, R. Eberhart) : PSO [0.013, 0.997], [30, 70] [15, 99], [1612, 9986] ( 0.384 44.698 Acer Extensa 5620) 8 % , ", [19], [24], [26]. . [19], [24], [26], % " [19], [26]. & " (PSO) " " # $ , 9 . ; , " % >99 #$ : 1) & " " " PSO " 9 % " , . . # >99 , ( , , 0 1), % % , " , , . : ", 9 9 % # , " . ; % ( # % ) " . 2) : " " " PSO >99 , " >99 " (" >99). 3) B #$ " " " ", $ " PSO 794 . 8 >99 #$" "" " " . 4) ' >99 " PSO $ " " , % " , . : >99 % >99 " PSO # ". 5) Q $ " " PSO # > " ( * ) $ " 2- 20-, " #$" " "" " # " ( * ) " ( 48 600 ), . 6. %#$ #$" "" " " $ " PSO " . 6 – L $ " PSO B #$ <B 100000 90000 80000 70000 60000 50000 40000 30000 $ . 20000 $ . 10000 0 0 5 10 15 20 6) @ $ $ " " PSO " , " " , #$ " ", %#$ " PSO ", () " . , %" % , " , , , # $ " , # % ( ), " #$" " "" " > > # , . . " " - 795 , . ; " #$" " <B # % >99 ", 9 , " >99 % [19]. @ , " > , . . $ > # " , > " (500, 1000) " #$ >99 " , # # , ( " > ) ANOVA ( >99 ). [1] Holland J. H. Adaptation in Natural and Artificial Systems / J.H. Holland - Ann Arbor: The University of Michigan Press, 1975.–228 p. [2] Goldberg D. E. Genetic algorithms in search, optimization, and machine learning / D.E. Goldberg. - Reading, MA: Addison-Wesley, 1989. [3] 8 , . @ , " " : ;. . M.. 8". / 8 ., ; !., 8 F – !.: < – , 2006. – 452 .: . [4] 8", F.B. & — 9, > . . . 33 (1978), . 3–16. [5] Schwefel H. P. Numerical optimization of computer models. Chichester: Wiley, 1981. [6] '" , B.&. & " " / B.&. '" , .'. < // M9 " . - | 3. - 2007. [7] , .M. M " " : Q. / .M. , '.<. M – Q9: Q<BQ, 1999. – 105 . [8] * , .&. 8% " "" ". / .&. * , .!. : // 9 - :MM-2008: 3- . - !.: URSS, 2008 . 157-161. [9] * , B.. 8 " " " % . . .9.-.. , 2000. [10] : , B.:. K # " % 9 / B.:. : , 8.M. // M %. - |4. – 2009 . 74-79. [11] : , F.<. ; # " " "" " / F.<. : // 9 - :MM-2008: 3- . - !.: URSS, 2008 c. 242-245. [12] :, .!. @ " " / .!. :, .&. :% // 9 - :MM-2008: 3- . .1 - !.: URSS, 2008 . 179-191. [13] F , !.. K " " # / !.. F , B.. &9 // M %. - |4. – 2009 c. 53-64. [14] Khloudova M. Classification of scheduling algorithms for real-time systems // Proc. of International Workshop on Nondestructive Testing and Computer Simulations in Science and Engineering. – 1999. – Vol. 3687. – P. 228-231. [15] Hallam N., Kendall G., and Blanchfield P. Solving Multi-objective Optimization Problems Using the Potential Pareto Regions Evolutionary Algorithm, in T.P. Runarsson et al (Eds.): Parallel problem solving from nature (PPSN IX: 9th international conference), LNCS 4193, pp. 503-512, Springer-Verlag Berlin Heidelberg 2006. 796 [16] Jurgen Branke, Kalyanmoy Deb, Henning Dierolf, and Matthias Osswald. Finding Knees in Multi-objective Optimization, in X. Yao et al. (Eds.): Parallel problem solving from nature (PPSN VIII: 8th international conference), LNCS 3242, pp. 722-731, Springer-Verlag Berlin Heidelberg 2004. [17] Kennedy, J. Particle swarm optimization / J. Kennedy, R. Eberhart // in Proc. of IEEE International Conference on Neural Networks. – Piscataway, 1995. – P. 1942 – 1948. [18] Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization // P. N. Suganthan, N. Hansen, J. J. Liang et al. / Technical Report, Nanyang Technological University, Singapore And KanGAL Report Number 2005005 (Kanpur Genetic Algorithms Laboratory, IIT Kanpur), May 2005 - 50 p. [19] L .'. 8 IT-SAGA % , " % " > / .'. L / M ": . & ! % , ( ; , 1-6 # 2011 ".). – : M- " " " " , 2011 – . 183-195. [20] Michalewicz, Z. Evolutionary algorithms for constrained parameter optimization problems / Z. Michalevicz, M. Schouenauer // Evolutionary Computation. - 4. - 1996. - P. 1 – 32. [21] Michalewicz, Z. Evolutionary algorithms for constrained engineering problems / Z. Michalewicz, D. Dasgupta, Riche Le, M. R. and M. Schoenauer // Computers & Industrial Engineering Journal - 30, 1996. – P. 851 – 870. [22] Zitzler E., Thiele L. Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach // IEEE Transactions on Evolutionary Computation, Vol. 3, No. 4, pp. 257-271, 1999. [23] &" 8.'. B 9 9 #$ > # " // 8.'. &" / B 9 , :, &<BQ, 29 2010, 20 . [24] L .'. M >99 " " > #" " / .'. L // & - 9 «@ . & ‘2010» ( 4 15 2010 ".). 2. . – : , 2010. - . 70-83. [25] ' : / .M. F %, M.. ! , @.M. [ .]; . . . . 8+, - >. , 9. .M. F %. 8- ., . – !.: :8@Q&, 2009 – 768 . [26] L .'. & #$ > # " % . 9 # 220100 – & (". '&), &<BQ, :, 2011 – 96 ., . Biography Zvonkov Vladimir B. Bachelor's degree of engineering and technology of "System analysis and control" (2011 year, honours degree diploma), undergraduate student of institute of informatics and telecommunication, system analysis and operations research department of Siberian state aerospace university named after academician M.F. Reshetnev (responsible attitude to learning, self-education and excellent knowledge of disciplines). I published over 25 scientific researches. Field of scientific interests is complex systems modelling and optimization, evolutionary algorithms, artificial neural networks, particle swarm optimization, committees of intelligent algorithms. The program system ITSAGA was awarded with 2nd rank in the nomination of "Research and experimental programs» as well as with absolute 2nd rank in the All-Russia student's competition of computer programs (taken part in Vologda, 2010 year). The author was awarded with the President of the Russian Federation Prize for the talented youth (order of the Ministry of education and science of the Russian Federation dated October 15, 2010, N 1031). The author is a laureate of the 3rd degree in a competition "Eureka2011" (taken part in Novocherkassk). The author has received 5 diplomas of the I-st degree, 4 diplomas of the II-nd degree, 1 diploma of III-th degree in the full-time different conferences. E-mail: vladimirzvonkov802@yandex.ru. Phone number: +7-913-585-69-35. 797