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Ant colony system with communication strategies

Ant colony system with

communication strategies

Shu-Chuan Chu

a,b ,John F.Roddick a ,Jeng-Shyang Pan c,*a School of Informatics and Engineering,Flinders University of South Australia,P.O.Box 2100,

Adelaide 5001,Australia

b Department of Industrial Engineering and Management,Kaohsiung University of Applied

Sciences,415Chien-Kung Road,Kaohsiung,Taiwan ROC

c Department of Electronic Engineering,Kaohsiung University of Applie

d Sciences,

415Chien-Kung Road,Kaohsiung,Taiwan ROC

Received 6August 2003;received in revised form 24October 2003;accepted 24October 2003

Abstract

In this paper an ant colony system (ACS)with communication strategies is devel-

oped.The arti?cial ants are partitioned into several groups.Seven communication

methods for updating the pheromone level between groups in ACS are proposed and

work on the traveling salesman problem using our system is presented.Experimental

results based on three well-known traveling salesman data sets demonstrate the pro-

posed ACS with communication strategies are superior to the existing ant colony system

(ACS)and ant system (AS)with similar or better running times.

Keywords:Ant colony system (ACS);Communication strategies;Ant system (AS)

1.Introduction

Swarm intelligence research originates from work into the simulation of the

emergence of collective intelligent behaviors of real ants.Ants are able to ?nd

Corresponding author.Tel.:+886-7-381-4526;fax:+886-7-389-9382.

E-mail addresses:shuchuan.chu@infoeng.?https://www.wendangku.net/doc/e43618133.html,.au (S.-C.Chu),roddick@infoeng.

?https://www.wendangku.net/doc/e43618133.html,.au (J.F.Roddick),jspan@https://www.wendangku.net/doc/e43618133.html,.tw (J.-S.Pan).

doi:10.1016/j.ins.2003.10.013Information Sciences 167(2004)

63–76

https://www.wendangku.net/doc/e43618133.html,/locate/ins

64S.-C.Chu et al./Information Sciences167(2004)63–76

good solutions to the shortest path problems between the nest and a food source by laying down,on their way back from the food source,a trail of an attracting substance––a pheromone.Based on the pheromone level communi-cation,the shortest path is considered that with the greatest density of pher-omone and the ants will tend to follow the path with more pheromone.Dorigo and his colleagues were the?rst to apply this idea to the traveling salesman problem[1,2].This algorithm is referred to as ant system(AS)algorithm.A more promising method was also developed and referred to as the algorithm of ant colony system(ACS)[3].The ant system and ant colony system have been applied successfully in many applications such as the quadratic assignment problem[4],data mining[5],space-planning[6],job-shop scheduling and graph coloring[7].

Parallelization strategies for AS[8]and ACS[9]have been investigated, however,these studies are based on simply applying AS or ACS on the multi-processor,i.e.the parallelization strategies simply share the computation load over several processors.No experiments demonstrate the sum of the compu-tation time for all processors can be reduced compared with the single pro-cessor works on the AS or ACS.

In this paper,we apply the concept of parallel processing to the ant colony system(ACS)and a parallel ant colony system(PACS)idea is proposed.ACS with communication strategies,which we termed PACS,will be used to run the experiments presented in the following of this paper.The purpose of the PACS is not just to reduce the computation time.Rather a parallel formulation is developed which gives not only reduces the elapsed and the computation time but also obtains a better solution.The arti?cial ants are?rstly generated and separated into several groups.The ant colony system is then applied to each group and communication between groups is applied according to some?xed cycles.The basic idea of the communication is to update the pheromone level for each route according to the best route found by neighbouring groups or,in some cases,all groups.Seven communication strategies are proposed for PACS.Experimental results based on the traveling salesman problem con?rm the e?ciency and e?ectiveness of the proposed PACS.

2.Ant system and ant colony system

Inspired by the food-seeking behavior of real ants,the ant system[1,2]is a cooperative population-based search algorithm.As each ant construct a route from nest to food by stochastically following the quantities of pheromone level, the intensity of laying pheromone will bias the path-choosing decision-make of subsequent ants.

The operation of ant system can be illustrated by the classical traveling salesman problem.A traveling salesman problem is seeking for a round route

covering all cities with minimal total distance.Suppose there are n cities and m ants.The entire algorithm is started with initial pheromone intensity set to s0 on all edges.In every subsequent ant system cycle,or called episode,each ant begins its tour from a randomly selected starting city and is required to visit every city once and only once.The experience gained in this phase is then used to update the pheromone intensity on all edges.

The algorithm of the ant system for the traveling salesman problem(TSP)is depicted as follows[2,3]:

Step1:Randomly select the initial city for each ant.The initial pheromone level between any two cities is set to be a small positive constant.

Set the cycle counter to be0.

Step2:Calculate the transition probability from city r to city s for the k th ant as

P ker;sT?

?ser;sT á?ger;sT b

u2J kerT

?ser;uT á?ger;uT b

if s2J kerT;

0otherwise;

>:e1T

where r is the current city,s is the next city,ser;sTis the pheromone

level between city r and city s,ger;sT?1=der;sTthe inverse of the

distance der;sTbetween city r and city s,J kerTis the set of cities that

remain to be visited by the k th ant positioned on city r,and b is a

parameter which determines the relative importance of pheromone

level versus distance.Select the next visited city s for the k th ant with

the probability P ker;sT.Repeat Step2for each ant until the ants have

toured all cities.

Step3:Update the pheromone level between cities as

ser;sTe1àaTáser;sTt

X m

k?1

D s ker;sT;e2T

D s ker;sT?

L k

ifer;sT2route done by ant k;

0otherwise;

:e3T

D s ker;sTis the pheromone level laid down between cities r and s by the

k th ant,L k is the length of the route visited by the k th ant,m is the

number of ants and0

number of cycles has reached,where a stagnation is indicated when

all ants take the same route.

S.-C.Chu et al./Information Sciences167(2004)63–7665

From Eq.(1)it is clear ant system(AS)needs a high level of computation to ?nd the next visited city for each ant.In order to improve the search e?ciency, the ant colony system(ACS)was proposed[3].ACS is based on AS but up-dates the pheromone level before moving to the next city(local updating rule) and updating the pheromone level for the shortest route only after completing the route for each ant(global updating rule)as

ser;sTe1àaTáser;sTtaáD ser;sT;e4T

D ser;sT?

eL gbTà1ifer;sT2global best route;

0otherwise;

(

e5T

where L gb is the length of the shortest route and a is a pheromone decay parameter.

3.Parallel ant colony system

A parallel computer consists of a large number of processing elements which can be dedicated to solving a single problem at a time.Pipeline processing and data parallelism are two popular parallel processing methods.The function of the pipeline processing is to separate the problem into a cascade of tasks where each task is executed by an individual processor,while data parallelism in-volves distributing the data to be processed amongst all processors which then executes the same procedure on each subset of the data.Data parallelism has been applied to genetic algorithm by dividing the population into several groups and running the same algorithm over each group using di?erent pro-cessor[10],and the parallel genetic algorithm has been successfully applied to noise reduction of vector quantization based communication[11].In this paper,we apply the idea of data parallelism to ant colony system(ACS)in order to reduce running time and obtain a better solution.The parallel ant colony system(PACS)is described as follows:

Step1:Initialization.Generate N j arti?cial ants for the j th group,j?0;1;...;Gà1.N j and G are the number of arti?cial ants for the j th

group and the number of groups,respectively.Randomly select an ini-

tial city for each ant.The initial pheromone level between any two cit-

ies is set to be a small positive constant s0.Set the cycle counter to be0. Step2:Movement.Calculate the next visited city s for the i th ant in the j th group according to

s?arg max

u2J i;jerT?s jer;uT á?ger;uT b if q6q0eexploitationT;

66S.-C.Chu et al./Information Sciences167(2004)63–76

visit city s with P i;jer;sTif q>q0ebiased explorationT;

P i;jer;sT?

?s jer;sT á?ger;sT b

u2J kerT

?s jer;uT á?ger;uT b

if s2J i;jerT; 0otherwise; 8

where P i;jer;sTis the transition probability from city r to city s for the

i th ant in the j th group.s jer;sTis the pheromone level between city r to

city s in the j th group.ger;sT?1=der;sTthe inverse of the distance

der;sTbetween city r and city s.J i;jerTis the set of cities that remain to

be visited by the i th ant in the j th group and b is a parameter which

determines the relative importance of pheromone level versus distance.

q is a random number between0and1and q0is a constant between0

and1.

Step3:Local pheromone level updating rule.Update the pheromone level be-tween cities for each group as

s jer;sTe1àqTás jer;sTtqáD ser;sT;

D ser;sT?s0?en?L nnTà1;

where s jer;sTis the pheromone level between cities r and s for the ants

in the j th group,L nn is an approximate distance of the route between

all cities using the nearest neighbour heuristic,n is the number of cities

and0

3until each ant in each group completes the route.

Step4:Evaluation.Calculate the total length of the route for each ant in each group.

Step5:Global pheromone level updating rule.Update the pheromone level be-tween cities for each group as

s jer;sTe1àaTás jer;sTtaáD s jer;sT;

D s jer;sT?eL jTà1ifer;sT2best route of j th group;

0otherwise;

where L j is the shortest length for the ants in the j th group and a is a

pheromone decay parameter.

Step6:Updating from communication.Seven communication strategies are proposed as follows:

?Strategy1:As shown in Fig.1,update the pheromone level between cities for each group for every R1cycles as

s jer;sTs jer;sTtkáD s bester;sT;

S.-C.Chu et al./Information Sciences167(2004)63–7667

D s best er ;s T?eL gb Tà1

if er ;s T2best route of all groups ;0otherwise ;

(

where k is a pheromone decay parameter and L gb is the length of the

best route of all groups,i.e.L gb 6L j ,j ?0;1;...;G à1.

?Strategy 2:As shown in Fig.2,update the pheromone level between

cities for each group for every R 2cycles as

s j er ;s T s j er ;s Ttk áD s ng er ;s T;D s ng er ;s T?eL ng Tà1if er ;s T2best route of neighbour group ;0otherwise ;

(where neighbour is de?ned as being the group whose binary rep-

resentation of the group number j di?ers by the least signi?cant bit.

k is a pheromone decay parameter and L ng is the length of the

shortest route in the neighbour group.

?Strategy 3:As shown in Fig.3,update the pheromone between cit-

ies for each group for every R 3cycles as

s j er ;s T s j er ;s Ttk áD s ng er ;s T;D s ng er ;s T?eL ng Tà1if er ;s T2best route of neighbour group ;0otherwise ;

(Fig.1.Update the pheromone level according to the best route of all

groups.

Fig.2.Update the pheromone level between each pair of groups.

68S.-C.Chu et al./Information Sciences 167(2004)63–76

where neighbour is de?ned as being the group arranged as the ring

structure.k is a pheromone decay parameter and L ng is the length of

the shortest route in the neighbour group.

?Strategy 4:As shown in Fig.4,update the pheromone between cit-

ies for each group for every R 4cycles as

s j er ;s T s j er ;s Ttk áD s ng er ;s T;D s ng er ;s T?eL ng Tà1if er ;s T2best route of neighbour group ;0otherwise ;

where neighbour is de?ned as being those groups where the binary

representation of the group number j di?ers by one bit.k is a

pheromone decay parameter and L ng is the length of the shortest

route in the neighbour group.

?Strategy 5:Update the pheromone between cities for each group

using both Strategies 1and 2.

?Strategy 6:Update the pheromone between cities for each group

using both Strategies 1and 3.

?Strategy 7:Update the pheromone between cities for each group

using both Strategies 1and 4.

Step 7:Termination.Increment the cycle counter.Move the ants to the origi-

nally selected cities and continue Steps 2–6until the stagnation or a

present maximum number of cycles has reached,where a stagnation

indicated by all ants taking the same

route.

Fig.3.Update the pheromone level according to the ring

structure.

Fig.4.Update the pheromone level to the neighbours according to the group number j di?ers by

one bit.

S.-C.Chu et al./Information Sciences 167(2004)63–7669

70S.-C.Chu et al./Information Sciences167(2004)63–76

4.Experimental results and performance study

To evaluate the e?ectiveness of PACS,we have performed an extensive performance study.In this section,we report our experimental results on comparing PACS with ant system(AS)and ant colony system(ACS).It is shown that PACS and various combinations outperform both ant system(AS) and ant colony system(ACS).

We used three generally available and typical data sets,EIL101,ST70and TSP225as the test material1to test the performance of the ant system(AS), ant colony system(ACS)and parallel ant colony system(PACS)for the traveling salesman problem.

To ensure a fair comparison among AS,ACS and PACS,?the number of groups·the number of ants per group’was kept constant––the number of ants for AS and ACS were set to be80,one swarm with80ants,as reported by 1·80.For PACS,the number of ants was also set to be80that was divided into4groups with20ants in each group(i.e.4·20)and8groups with10ants in each group(i.e.8·10),respectively.The parameters were set to the fol-lowing values:b?2,q0?0:9,a?q?k?0:1[3].The number of iterations for both EIL101and ST70were set to be1000and TSP225was set to be2000 as the cities of TSP225are more than EIL101and ST70data sets.The number of cycles(i.e.R1,R2,R3and R4)between updates of the pheromone level from communication for strategies1–7in PACS were set to be30.In order to test the performance of the di?erent approaches to the traveling salesman problem, variously proposed communication strategies for updating the pheromone level between groups in PACS were combined.Where appropriate,these seven communication strategies are applied to the PACS and compared to AS and ACS.

EIL101,ST70and TSP225are data sets with101,70and225cities, respectively.Experimental results were carried out to the average shortest length for10seeds.The performance of PACS(i.e.ACS with communication strategy)is better by in comparison with AS and ACS can be illustrated by Figs.5–7.As can be seen from Tables1–3,PACS outperforms both AS and ACS on e?ectiveness.

The EIL101data set was used for the?rst experiment.As shown in Table1, the average improvement on EIL101for proposed strategy5for4groups with 20ants in each group by comparing with AS and ACS were much better up to be10.57%and4.70%,respectively.In comparison with AS and ACS,the average improvement on EIL101for proposed strategy3for8groups with10 ants in each group were10.41%and4.52%,respectively.

1Available from http://www.iwr.uniheidelberg.de/groups/comopt/software/

Fig.6.Performance comparison among AS,ACS and two arbitrarily chosen strategies for ST70

data

set.

Fig.7.Performance comparison among AS,ACS and two arbitrarily chosen strategies for TSP225

data

set.

Fig.5.Performance comparison among AS,ACS and two arbitrarily chosen strategies for EIL101

data set.

S.-C.Chu et al./Information Sciences 167(2004)63–7671

Table1

The performance of ACS with communication strategies(Strategies1–7)obtained in comparison with AS and ACS for EIL101data set on TSP problem

Seed AS ACS Strategy1Strategy2Strategy3Strategy4Strategy5Strategy6Strategy7 1,801,804,208,104,208,104,208,104,208,104,208,104,208,104,208,10 1730683657655648653649645654644646651646653647646 2730680657655650647643648647649641650660643648643 3731681644646655655641646653646641648646645648642 4720678645648651654651647647647643646642647650652 5727676641643648663648656651651644650647651647647 6727673656655648644645655648649647653651653644655 7698675642644649658646648651650650646647648645645 8726679646651653662658645647645653650647653652655 9721672645646651656652642649650652647649651646650 10718685643651654647645645652651646647646652650648 Average72367864864965165464864865064864664964865064864872 S.-C.

Chu

al. / Information Sciences 167 (2004) 63–76

Table2

The performance of ACS with communication strategies(Strategies1–7)obtained in comparison with AS and ACS for ST70data set on TSP problem Seed AS ACS Strategy1Strategy2Strategy3Strategy4Strategy5Strategy6Strategy7 1,801,804,208,104,208,104,208,104,208,104,208,104,208,104,208,10 1734701679680683684678683681683684679680677682681 2721700681681677686688681677681679681677681677681 3722700681682678683678681678681677681681682678677 4717701677688682687678682679694685683686681681689 5721703678684678686678681678678678679682683677682 6713702691690694683678682678678692681689684689686 7714700682683678683677683681677681682678677677677 8730701677682677679677682681683681677678678678683 9730696677681679685678678677682683679683677678681 10736699678693682688678681681682680691682678677690

Average724700680684681684679681679682682681682680680683S.-C. Chu et al. / Information Sciences 167 (2004) 63–76 73

Table3

The performance of ACS with communication strategies(Strategies1–7)obtained in comparison with AS and ACS for TSP225data set on TSP problem

Seed AS ACS Strategy1Strategy2Strategy3Strategy4Strategy5Strategy6Strategy7 1,801,804,208,104,208,104,208,104,208,104,208,104,208,104,208,10 14587414539073933391339143879388539033949388239053866390538843943 24492421539033883387938793883387739553942391638713902389238913881 34454414938883926390039003889389639533916390638883878389439193902 44609416038923886390839523889388538953890387138853866387939193899 54538416338813869388838983879388538793880388239103878388438863922 64483414639423915391639783892390139613895388338773901386638823882 74555414939043911387639393881389138813887388138763892388538823912 84491414839503900391239253950388938903902395738913936395039523903 94500410839033916390339043889388738963886388238913904387538813898 104521416138773915387539113877389638763873388438763919391738953909 Average452341543905390538973920389138893909390238943887389438953899390574 S.-C.

Chu

al. / Information Sciences 167 (2004) 63–76

S.-C.Chu et al./Information Sciences167(2004)63–7675 The ST70data set was used for the second experiment.As can be seen from Table2,the average performance of proposed strategy3for4groups with20 ants in each group by compared with AS and ACS were6.20%and3.06%, respectively and that of proposed strategy6for8groups with10ants in each group were6.06%and2.92%,respectively.

The TSP225data set was also used for the?nal experiment.Experimental results shown in Table3,compared with AS and ACS,shows that the average performance of proposed strategy3for4groups with20ants in each group were13.97%and6.35%,respectively and that of proposed strategy5for8 groups with10ants in each group were14.06%and6.44%,respectively.

5.Conclusions

The main contribution of this paper is to propose the parallel formulation for the ant colony system(ACS).Seven communication strategies between groups which can be used to update the pheromone levels are presented.For our preliminary experiments,the proposed parallel ant colony system(PACS) outperforms both ACS and AS based on three available traveling salesman data sets.In general,our presented systems based on data set with large data can get much better performance such that the average improvement of TSP225is better than that of ST70.The proposed PACS may be applied to solve the quadratic assignment problem[4],data mining[5],space-planning[6], data clustering and the combinatorial optimization problems.We will apply the PACS to data clustering in future.

References

[1]A.Colorni,M.Dorigo,V.Maniezzo,Distributed optimization by ant colonies,in:F.Varela,

P.Bourgine(Eds.),First Eur.Conference Arti?cial Life,1991,pp.134–142.

[2]M.Dorigo,V.Maniezzo,A.Colorni,Ant system:optimization by a colony of cooperating

agents,IEEE Transactions on Systems,Man,and Cybernetics––Part B:Cybernetics26(1) (1996)29–41.

[3]J.M.Dorigo,L.M.Gambardella,Ant colony system:a cooperative learning approach to the

traveling salesman problem,IEEE Transactions on Evolutionary Computation1(1)(1997)53–

66.

[4]V.Maniezzo,A.Colorni,The ant system applied to the quadratic assignment problem,IEEE

Transactions on Knowledge and Data Engineering11(5)(1999)769–778.

[5]R.S.Parpinelli,H.S.Lopes,A.A.Freitas,Data mining with an ant colony optimization

algorithm,IEEE Transactions on Evolutionary Computation6(4)(2002)321–332.

[6]J.A.Bland,Space-planning by ant colony optimization,International Journal of Computer

Applications in Technology12(6)(1999)320–328.

[7]M.Dorigo,G.D.Caro,L.M.Gambardella,Ant algorithms for discrete optimization,Arti?cial

Life5(2)(1999)137–172.

76S.-C.Chu et al./Information Sciences167(2004)63–76

[8]B.Bullnheimer,G.Kotsis,C.Strauss,Parallelization strategies for the ant system,Technical

Report POM9/97,Institute of Applied Computer Science,University of Vienna,Austria,1997.

[9]T.St€u tzle,Parallelization strategies for ant colony optimization,in:Fifth International

Conference on Parallel Problem Solving for Nature,Lecture Notes in Computer Science,Vol.

1498,Springer-Verlag,Berlin,Heidelberg,1998,pp.722–731.

[10]J.P.Cohoon,S.U.Hegde,W.N.Martine,D.Richards,Punctuated equilibria:a parallel genetic

algorithm,in:Second International Conference on Genetic Algorithms,1987,pp.148–154.

[11]J.S.Pan,F.R.McInnes,M.A.Jack,Application of parallel genetic algorithm and property of

multiple global optima to vq codevector index assignment for noisy channels,Electronics Letters32(4)(1996)296–297.

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