The intelligent bion ic model ant colony

Transcription

1 3 3 Vol CAA I Transactions on Intelligent System s Jun (, ) :,., 10,.,,,.,,.,,,.,. : ;;; : Q : A : (2008) The intelligent bion ic model ant colony GAO W ei (Department of Civil Engineering,W uhan Polytechnic University, W uhan , China) Abstract: Imp roving the intelligent bionic ant colony model w ill require multidiscip linary research, and so its devel2 opment w ill p romote p rogress in related subjects. The ant colony model, a new intelligent bionic model which m im2 ics the behavior of an ant colony, has p rogressed substantially in the last ten years. However, there has been no systematic study of this field in China. To encourage more research this paper gives a detailed introduction to sever2 al m ajor ant colony models according to their underlying bionic p rincip les and simulation m ethods. The latest devel2 opments in this field are also described. Then, typ ical app lications of ant colony models are summ arized, and new areas where they can be used are p resented. Finally, comparisons are m ade between the ant colony algorithm, the particle swarm op tim ization algorithm, the imm une algorithm, and the evolutionary algorithm. The sim ilarities and differences between these algorithm s are pointed out. This comp rehensive introduction should p romote research on ant colony algorithm s in China. Keywords: intelligent bionic model; ant colony model; subjects intersection; biological p rincip les, 20,.,, 20., [ 1 ]. : :(D ). :. E2mail: gaow@whpu. edu. cn. ( artificial life, AL IFE) ( computational intelligence, C I) ( bioinformatics) ( natural computation, NC), 20..,.,.

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3 272 3.,,,.,, ;,, (,) , TSP,, TSP, AS ( ant system ). m n,: ; ( ) ( n ),. 2 : 1) ij ( t) t i j, ; 2) ij i j,,. TSP ij = 1 / d ij ( d ij i, j, ij )., t i kj P k ij ( t) : 1) k ij =, j N k i, P k ij ( t) = ij ( t) j j N k i. ij ( t) j : ; ; N k i,.,.,, tabu ( k),., n (),,,.,,: ij ( t + n) = m ij ( t) + k ij. k =1 : ; 1 -,.,, 1; k ij kt ( t + n), i j., k ij, M. Dorigo3, Ant Cycle, Ant Density, Ant Quantity. 3 Q L k, k t t + 1ij; 0,. : Q ; L k k. 2) k ij = Q, k t t + 1ij; 0,., ij (0) = C. 3) k ij = Q d ij, k t t + 1ij; 0,. 2, 2 (t ( t + 1) )

4 3 : 273, n ; Ant Density, k ij = Q; Ant Quantity, k ij = Q ( d ij i d ij j )., Ant Density, i j Q. Ant Quantity, d ij i j, Q / d ij,. Q [ 2 ]., : 1)., ; 2).,, ; 3).,.,,, :,, ; ( stagna2 tion behavior),,,,. 2. 2,,.,,,. Deneubourg [ 5 ], ( basic model, BM ),.,, ;,.,.,., Lumer Faieta [ 6 ] Deneubourg BM.,. BM.,,,. 2. 3,,,.,,.,,.,,,??,????. ( ),. ( ), ( )., ( ),,.,,.,., [ 7 ], ( Agent., )..,,., (,)., (

5 274 3, ). (, ).,., n ( ), f. f.,,..,.,.,,.,, ,, [ 8 ]. 1) ; 2), ( ) ; 3); 4), , ( ),.,, [ 9 ].,.,,,.,. 2. 5,,,., [ 10 ],,, ;,,.,. [ 11 ],,.,. [ 12 ],,,, Kong M in [ 13 ] binary ant system (BAS),,,. J iejin Cai [ 14 ] chaotic ant swarm op tim ization (CASO ),,. Bernd Scheuermann [ 15 ], Counter2based ACO,,. [ 16 ], 2,,.,., W. Traili [ 17 ],. M. D. Toksar [ 18 ]. B. M. T. L in [ 19 ],. 3, ( combination op ti2 m ization p roblem )., 2

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