Solving Agent coalition using adaptive particle swarm optimization algorithm

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1 2 2 Vol CAA I Trasactios o Itelliget Systems Apr Aget,, (,230009) : Aget, Aget A2 get., c0, cadp,.,. :Aget ;; ; : TP18 :A : (2007) Solvig Aget coalitio usig adaptive particle swarm optimizatio algorithm J IAN G Jia2guo,WU Qiog,XIA Na (College of Computer ad Iformatio, Hefei Uiversity of Techology, Hefei , Chia) Abstract :Coalitio Geeratio is a key issue i a Multi2Aget System which p rimarily focuses o geeratio of a optimal task2orieted coalitio i a dyamic maer. A particle swarm optimizatio ( PSO) algorit hm is adopted to solve t he problem. Ad a oveladaptive iertia weight is propo sed to improve PSO by t he illumiatio of f uctio of iertia weight,so as to avoid fallig ito local miimum. The result s of compar2 iso experimet s show that t his algorit hm is superior to other related met hods i both performace of solu2 tio ad covergece rate. Keywords :MAS ; PSO algorithm ; adaptive particle swarm optimizatiocoalitio,, Aget MAS. Aget Aget ( MAS),Aget. Aget, A2 get Aget,Aget Aget (coalitio). Shehory [1 ] Sadholm [2-3 ],. Aget,, Aget (MAS), Aget : : ( )., [4-5 ] [6 ] [7-8 ], : Aget N = { A 1, A 2,, A }, Ai : Ai = < a 1 i, a 2 i,, a r i >, a k i 0, (1 i,1 k r) A i. t N ( t) = < 1, 2,, r >, k 0, (1 k r), Aget t P ( t). C N. C C = < c 1, c 2,, c r >, C Aget, C = A i. C A i C

2 70 2 t : Π1 k r, k c k. [2-3,9 ] : (1), (character f uctio games, CF Gs). C V ( C). V ( C) 0, V ( C) = P ( t) - F ( C) - C( C). P( t) ; F( C) ; C( C) t,. C,V ( C) 0, V ( C). (2) [3 ] (ΠC1, C2 Α N, C1 C2 =, V ( C1 C2 ) V ( C1 ) + V ( C2 ),, ). Aget t C ( C Α N),V ( C). MAS, Aget, MAS A2 get [ 1 ],,. 112 MAS, N,Shapley. N,,Aget ;,,. MAS,,. Sad2 holm [2-3 ] [10 ] [ 11 ] Aget,.,,,. Se [12 ] [13 ] Aget,. [5 ] Aget, ;MAS, MAS [14 ] [15 ] [ 16 ] [17 ]., Aget ( PSO ) Keedy Eber2 hart [ ] 1995,.,,. PSO,,. PSO (),., : vk+1 = c0 vk + c1 (p best k - xk) + c2 (gbest k - xk). (1) xk+1 = xk + vk+1. (2) :p best k ; gbest k ; vk ; xk ; c0 c1 c2. vk + 1 vk p best k - xk gbest k - xk, 1 [20 ] : 1 Fig11 Sketch map of particle s movemet vector 212 PSO Aget Aget Aget MAS, t Aget,. PSO,, Aget, L = < l0, l1,, l - 1 >, li { 0, 1} (1 i ), li = 1 Aget i L. PSO 2, [13 ]. PSO,, : (1) : c0 vk, c1 (p best k - xk ) + c2 (gbest k - xk),

3 2,: Aget 71. : 1) m, N C, m L m = { L 0, L 1,, L m - 1 } ; 2) V i (V ( C) ), V i pbest, L i pbest L gbest,v gbest, While ( < N C) do for ( i = 0 ; i < m ; i + + ) 3) i L i L gbest L i 1 4) L i 1 L i pbest L i 2 5) L i 2 L i 6) L i V i ; 7) V i > V i pbest,l i pbest = L i,v i pbest = V i ; Ed 8) V i pbest, V gbest L gbest Ed 9) V gbest L gbest, 3 m, N C, O(3 m N C). 213 PSO 3 [13 ] : c0 c1 c2. c0,,,. c1,,. c2,. PSO Aget c0 c1 c2, c0, c0., cadp, cadp,, cadp PSO (A PSO). 1 cadp = c0,, cadp + 1, N,,cadp + 1 cmax.,a PSO,,ccdp = c0, PSO ; N,, cadp,;,, cmax cadp.,,cadp = c0,pso.,pso,a PSO c2 c1, c2 c1, 3) 4) ; cadp 0 1,,,. 3) 5),. O( m N C), 1/ A PSO Aget, : 1) m, N C, m L m = { L 0, L 1,, L - 1 } ; 2) V i (V ( C) ), V i pbest, L i pbest L gbest,v gbest, While ( < N C) do for ( i = 0 ; i < m ; i + + ) 3) i L i pbest L gbest c1 c2 L i,; L i cadp 0 1 ; 4) L i V i ; 5) V i > V pbest,l i pbest = L i,v i pbest = V i ; Ed 6) V i pbest, V gbest L gbest If ( N V gbest ) the if ( cadp + + cmax) the cadp + + else cadp = cmax else cadp = c0 Ed 7) V gbest L gbest 3 A PSO. : Aget = 30 A2 get Ai = < a 1 i, a 2 i,, a r i >, Aget dij t N ( t) ;

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