M odeling and sim ula ting the forag ing system in multi2source groups w ith random d isturbances

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1 3 4 Vol CAA I Transactions on Intelligent System s Aug. 2008,, (,150001) : ,,,. Starlogo,.,. : ;; ; Starlogo : TP18 : A: (2008) M odeling and sim ula ting the forag ing system in multi2source groups w ith random d isturbances L IU Bai2long, ZHAN G Ru2bo, SH I Chang2ting (College of Computer Science and Technology, Harbin Engineering University, Harbin , China) Abstract: Swarm intelligence ( SI) is artificial intelligence based on observable collective behavior of decentralized, self2organized system s, otherw ise known as social anim als, and is gaining more attention from researchers. SI sys2 tem s are typ ically made up of a population of simp le agents interacting locally with one another and with their envi2 ronment. The agents follow very simp le rules, and although there is no centralized control structure dictating how individual agents should behave, local interactions between such agents leao the em ergence of comp lex global be2 havior. Specially, the foraging behavior of ant colonies have be viewed as a p rototyp ical examp le of how comp lex group behavior can arise from simp le individual behaviors. In order to study the feature of self2organization in SI, first a macroscop ic serial parametric model for flock foraging was established, in which the richness and distribution of food sources were considered as well as the stochastic effects of a noisy environm ent. Numerical solutions were given for systematic models w ith two food sources. These showehat, in an environm ent w ith great noise, the op ti2 mal solution m ay not be found and a second2best solution may instead be reached. Simulations on the Starlogo p lat2 form showed a power law relationship between the number of ants and comp letion time as well as the flux of forag2 ers. The work p resented here may imp rove the understanding of self2organization and swarm intelligence. It can al2 so be useo design more efficient, adap tive, and reliable intelligent system s. Keywords: swarm intelligence; self2organization; foraging; modeling; Starlogo simulation.,. : :. E2mail: lbl624@163. com....,.

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3 344 3,.,,,.,,,.,.,.,., ,.,,.. :,.,,., ( ).,,...,( ). 2, 1. ( finite state machine, FSM ).,,. 1,,. 1 Fig. 1Control structure of swarm foraging system 2. 2, i ( i)c i, c i. c i 2 : i <q i F i, - vc i. <,. q i i,. v. F i i,f i F i = ) l. (1) s ) l j =1 : k, l c i,lasius niger

4 4,: 345, l = 2 [ 11 ]. s,. c i : d c i = <q i s i =1 ( k + c j - vc i, i = 1, 2,, s. (2) : q i i,,,.,, if i, < F i i, f i <F i,: d f i = - <F i = - <, s i =1 i = 1, 2,, s. (3), q i f i,, q i = f i,. =0. 1s =2.,.,d i i 1 / d i. c i 1 / d i., d c i = <f i ) - vc d s i, i i =1 2 i = 1, 2,, s. (4) (3) (4),4 : 1) c i, F i ; 2) ; 3) jii ; 4) F i f i.., 2 : 1 ) ; 2). 2: f i 0, c i 0. (5),,., d c i = <f i ) - vc d s i + i ( t), i ( k + c j j =1 i, 2 i = 1, 2,, s. (6) i ( t) = 0, (7) i ( t) i ( t ) = 2D( t - t ). (8) :, D.,, d f i = 0, d c i = 0. (9) (3) (5) f i 0., MatlabStarlogo ,, 2.. f i = 10, c i = 0, i = 1, 2, D = 1, < = , Matlab 3. 2,., 32,, 2..,.. 2 Fig. 2Sketch of different trails with different sources

5 ,d 1 = 1. 5d 2., (D = 1),,, 4.,,. 3. 2,,. f 1 = 8, f 2 = (5 ),,. 5 Fig. 5 The changes of system state in different sources of the same distance 4Starlogo 3 Fig. 3 The changes of system state in the same sources of same distance 4 Fig. 4The changes of system state in the same sources of different distance,starlogo. StarLogo 20 80, M IT Metchel ResnickSeymour Papert. Logo LEGO. StarLogo2:.,. 2:.,,., , 6., 180,., 2 (

6 4,: 347 1,2), ().,,.,,. 2..., 22. 8, ,,.. 6Starlogo Fig. 6The environment of foraging system in Starlogo 4. 1,, , , /3,1. 20,1 19.,, 2.,,,, Fig. 7The time evolution of pheromone concentration Fig. 8The comp leting time and conflict frequency in different number of ants 5, Starlogo.,.,,,.,, Starlogo.,.. (), ()..

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