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Distributed multi-ant algorithm for capacity vehicle route problem.

This paper proposes a Distributed Multi-ant Algorithm for capacity vehicle route problem (CVRP) where cooperation is helpful for accelerating prior solution by executing a decomposition-based separation methodology for the unsteady capacity constraints. It decreases the complex coupling network with others to solve small instances with less correlation in parallel processing. The main goal of this work is to play well on large scale CVRP with interaction between subsystems and certain state vectors. The results show that Distributed Multi-ant Algorithm plays better performance on average solution and the importance of potential action is analyzed.

Povzetek: Za problem CVRP je razvit izboljgan postopek, temeljec na algoritmih z mravljami.

Keywords: capacity vehicle route problem, distributed multi-ant algorithm, cellular ants, performance potential

1 Introduction

With decomposition, every subsystem could be described as a Markov Decision Process (MDP) model: Let M : {S, A, T, R, f v} be a five tuple model, where S = {s} is a set of states, A = {a} is a set of actions, T = {p(x | s, a), s [member of] S, a [member of] E A} is the next-state transition probability distribution, with p(x | s, a) describing the probability of action a in state s to s'. R(s, a, s') is the reward function, f v is the additional reward function. [pi](s) is a policy function in the state space. The discussion about applicability and feasibility is based on discount value MDP and Q-learning.

Algorithms works on CVRP are researched for years. Edge assembly (EAX) crossover with well-known local searches is employed to CVRP (Yuichi Nagata et al, 2009). Deoxyribonucleic acid (DNA) computing model and a modified Adleman-Lipton model accelerate the search on large nodes CVRP (Yeh Chung Wei, 2009). Ellipse rule approach reduces the average distance to the lower bound by about 44% (Santos Luis et al, 2009). A multi-objective evolutionary algorithm is used for CVRP (Borgulya Istvan, 2008). Particle swarm optimization (PSO) can also apply for CVRP, in two models (Ai The Jin et al, 2009). Cellular GAs has solved vehicle routing problem, minimized transportation cost and recombined a new problem (Carlos Bermudez et al, 2010) and genetic algorithm has worked at it fewer than 100 nodes (Wang Chungho et al, 2010). Normally, it is solved based on decentralized model. However, coupling among subsystems (called SCVRP in the paper) is not considered well and being trap into a local solution or no solution easily, deviating from what we expected. Multi-ant algorithm is extension of ant algorithm who plays a better performance in the best solution and used to VRP already (Yuvraj Gajpal et al, 2009). The ant isn't punished if the strategy misleads it to suboptimal policies. And if there isn't new knowledge during state s, the reward function is still working accumulation. In this paper, decomposition of CVRP based on a distributed model is presented with iteration and cooperation between subsystems searching for their own optimization by distributed multi-ant algorithm. As cooperation, a cellular ant contacts with other ants both in its own subsystem and others through reward strategies obtained whenever the related strategy is optimal by traditional relative reinforcement learning. Reward shaping undergoes through structuring additional reward function for distributed multi-ant algorithm, making it more efficient.

As the large scale of CVRP is divided into smaller SCVRPs in a distributed system where bunches of cellular ants set to seek an optimal solution of corresponding subgraph for SCVRP. On the one hand, cellular ants in the same subsystem collaborate with each other and refresh their own knowledge. On the other hand, cellular ants in different SCVRP regenerate strategies as they meet in the crossed arcs over several disparate SCVRPs.

The paper is organized as follows: Section 2 will introduce some basic knowledge of tree cutest and decomposition of CVRP for distributed system using tree cut-set. Section 3 describes how those SCVRPs figuring out the optimal solution respectively based on distributed multi-ant algorithm with reward shaping. The procedure of cooperation between subsystems is gotten. Then, the experimental results in several algorithms are contrasted using ArcView, matlab R2009a and GAMS softwares in Section 4. Finally, the discussion and conclusions is in Section 5, as well as directions for future work.

2 Decomposition algorithms for CVRP

For the limited capacity constraint, a large scale of CVRP is decomposed into SCVRPs with unsteady capacity constraints in the distributed system. The mathematical model of CVRP is transformed through Tree Description (Chen Yulin et al, 2002). An algorithm named Tree Cut-Set (TCS) is proposed for decomposition. The number of subsystems is determined by the carrying capability of vehicles, demands of customers and connection between customers.

A large scale of undirected graph can be transformed into complete trees and split into subgraphs where leaves of subgraphs are the compound boundaries. Then, the semantic representation of CVRP could be replaced by SCVRPs. Based on these definitions in (Y Xiang, 1996), calculation must comply for some principle as follows:

(1) a * b = ab

(2) (a * b)' = ab' + a,b = a + b

(3) a[DELTA]b = a'b + ab, = a + b

(4) (a[DELTA]b)' = (ab)' = 1

In ripping, two subgraphs are brought out and the summation of their semantic representation is equal to the original graph. We can call the original graph the father graph of the two subgraphs. It requires the knowledge of the interface between SCVRPs only, not the knowledge of the internal structure of them. TCS is applied to closed the structural details to separate the model. In the result, every SCVRP could obtain its own solution through an independent set of ants and the link between them is loosely coupled by employing TCS. Whatever the structure CVRP is, TCS is suitable for it, whether customers may join and leave or not.

We will explain the theory of t - sepset couple based on d - sepset (Li Yan et al, 2004).

Definition 1 In a tree, for tree-node i, there is only two parents and one child node j. For tree-node j, there is only two different children. Then, (i, j) is called as a t-sepset couple nodes.

Searching the tree created from Definition 1 from top to button by Greedy policy, t-sepset couple nodes for customers with uncertain demands are ripped. As what Bayesian Theory (Andrew Y Ng et al, 1999) says, every separate demand is weighted by [w.sub.i] a random variable that

must satisfy some limitation: w > 0 and [2.summation over (i=1)] [w.sub.i] = 1.

For the limited carrying capability of vehicles b, the total demands [t.sub.a] in each subsystem must be lower than b. The purpose is that the parameter e = [t.sub.a] - b is gradually close to zero. If every SCVRP meets this condition, the problem would be solved perfectly. The procedure would stop till e is lower than a by constant p. Otherwise, TCS will continue.

3 Distributed Multi-ant Algorithm

3.1 Reward shaping

(Andrew Y Ng et al, 1999) presents a method that if a potential function [PHI](s) exists so that R'(s, a, s') = R(s, a, s') + [PHI](s,) - [PHI](s) for any policy [pi](s), [V.sup.*](s) [pi](s) - [PHI](s). Reward shaping causes the optimal policies in M would be the optimal policies in M', to exchange the original reward function R of MDP M with new reward function R' of new MDP M'. In this paper, we definite the R' in M' :

R'(s, a, s') = R(s, a, s') + fv(s, a, s') (1)

Where fv(s, a, s') is a function, carrying ant colony information. The proof of this equation is in next chapter.

3.2 Cellular ant

Cellular automata (CA) is a discrete grid dynamics model both in time, space and state vector. It is utilized to imitate complex and abundant macroscopic phenomenon in single regulations of parallel evolutionary. The distributed cellular in grid net of SCVRP has finite discrete state, following the same action regulations to update its state by local rules synchronously.

As equations in reference (Moere Andrew Vande et al, 2005), the dynamic evolutionary of CAs is: F([S.sup.i.sub.t] + 1) f([s.sup.i-r.sub.t], ... , [s.sup.i+r.sub.t]). [S.sup.i.sub.t] describes the state of cellular ant in position i at time t and the local updating rule is f : [S.sup.2r+1.sub.t] [right arrow] [S.sub.t].

The structure of CAs contain four basic parts: cellular ants space, grid dynamics net, local rules and transfer function, discrete time set. For the speciality of SCVRP, cellular space has two dimensions with uncertainty states of cellular ant. The renewed knowledge function is composed of its information at time t and its neighbors' using the extended Moore neighbor model:

f: [s.sup.i.sub.t+1] = f([s.sup.i.sub.t], [s.sup.N.sub.t]) (2)

The key of C As is to gain the strategies of certain neighbors' by the extended Moore neighbor model, then it could compare decision strategies referred to the last state in MDP of cellular ant with its neighbors. Reward function of distributed multi-ant algorithm is gotten as follows by

f = f([s.sup.i.sub.t](a), [N.summation over (j=0)] [s.sup.j.sub.t] (a)) and f v (s, a, s') = F([s.sup.i.sub.t+1):

R'(s, a, s') = R(s, a, s') + [2r.summation over (r=0)] f ([s.sup.i+r.sub.t] (a), [N.summation over (j=0)] [s.sup.j.sub.t] (a)) (3)

Proof of Equation 3: From Equation 1, we can see the additional reward function should be potential function which is proven (Moere Andrew Vande et al, 1999). Equation 2 gives the description of f function that includes local rules and transfer principles. The action of f function is renewing information among neighbors for best states of each ant at the next moment. For this system, it is a discrete simple space model. Every state is a dot in the time vector, function f could be considered as the max value in each state dot. We could say f is the gradient function in the space/time vector space. Then, F is a vector field composed by a set of gradient field, in other words, F is deemed as potential field which is also called potential function.

3.3 Distributed Multi-ant Algorithm

Dynamic learning promotes the efficiency in Back Propagation (Yu Xiaohu et al, 1995). It is expected to accelerate the learning rate so that running time could cut down and final solution could be gained sooner. The value [[phi].sub.t] (i) describes the learning rate of ant i at time t being gradually decreased to zero in the limit of search procedure. Let [[phi].sub.t](i) > 0 be a series of constants for every ant at time t


There are some destabilization in the circumstance in SCVRP, for example, the weather will delay the arriving time of transportation, the difference performance of the vehicles may also influent the efficiency, and so on. The influence of disturbance in the system can be measured by Performance Potential (Cao Xien et al, 1997). As the definition, it could set performance potential of a random state being benchmark for any other states. We choose the last-step state as the basis of performance potential for current-step state where it helps to make decision more accurately. Let X = {[X.sub.t], t = 1, 2 ...} picture the decision progress of MDP. Considering the principles of reward function, the description of performance potential in state s, (s is the last-step state) is as following:


Standard ant colony algorithm is integrated with reward shaping function. New value function and strategy function are gained based on Q-learning (Bagnell J Andrew et al, 2006; Dietterich Thomas G, 2000):

[[pi].sup.*.sub.k] (i,j) = argminmax {[summation over a [member of]sup.A] [[pi].sub.a] (i,j) * [Q.sup.*} (4)

[Q.sup.*] = [max.sub.a[not equal to]k] Q(i, j, k) - Q(i, j, s)

Q(i, j, k) = (1 - [r.sub.k]/[R.sub.k]) x Q - [delta] x [g.sub.s], + [r.sub.k]/[R.sub.k] x [V.sup.*]

[V.sup.*] = R'(i, k, j) + [gamma][V.sup.*] (s')

Where [alpha] is the discounted factor. As the amount of cellular ants which arrived in city i is [R.sub.k], and the amount of cellular ants which chose city j for the next city is [r.sub.k] where [[phi].sub.t] (i) = 1 - [r.sub.k]/[R.sub.k].

Proof of Equation 4 Convergence : An equation is proposed and proven in (Jiang Lingyan et al, 2007) as [Q.sub.t+1] (s, a) [left arrow] (1 - [alpha])[Q.sub.t] (s, a) + [alpha] [[r.sub.k] + [gamma] * max[Q.sub.t] (s', a')], according to this equation, it is easy to say [gamma] * max[Q.sub.t] (s', a') is bounded. Because [R.sub.k] is greater than [r.sub.k], [DELTA][[tau].sub.ij] is bounded in each updating, in other words, the amount of trail every iteration is bounded. By reason of [[eta].sub.ij] is finite constant, then P(i, j) is bounded. To sum up,

f = f([s.sup.i.sub.t] (a), [N.summation over (j=0)] [s.sup.j.sub.t] (a))

should be bounded and that results

in bounded [pi](i,j). [absolute value of S] and [absolute value of A] are both finite sets, then

f = f([s.sup.i.sub.t] (a), [N.summation over (j=0)] [s.sup.j.sub.t] (a)) is the state transition in MDP that must be finite. Therefore, [pi](i, j) is bounded and finite, for every decision, there is a prior strategy [pi](i, j) to leading cellular ants towards global optimal solution.

3.4 Algorithm process

For the simulation, one formula should be presented firstly. Formula 1: Transition probability [P.sup.k.sub.ij] of each cellular ant facing the near city is defined as following:



Where [[tau].sub.ij] expresses the trail of arc (i, j), [[eta].sub.ij] describes heuristic degree and [j.sup.*] is the set of allowed cities for cellular ant j. The pseudo-codes are shown as following:

1: Put m cellular ants on [eta] cities of a decomposed SCVRP stochastically.

2: Choose the next city j for each ant by probability [P.sup.k.sub.ij] when it stands in city i;

3: Calculate values of target function (F function) for each cellular ant and list the best one;

4: Search for the best performance by evolving in neighbors' as the definition from equation 2;

5: Update R' function. If latest strategy is helpful for the program, reward value is positive and it becomes advanced step, vice versa;

6: Replace [g.sub.s] for [g'.sub.s] by rewards from the latest iteration to weaken influence from disturbance variables;

7: Renew Q(i, j, k) function for ant k through their knowledge and others';

8: Make the prior strategy [pi] following equation 4 and choose the next city according to strategy [pi];

9: End till no optimal solution figuring out.

4 Computational experiments

Computational experiments have been conducted to analyze the performance of the proposed algorithm and present the results along with comparative analyse. All these algorithms have been compared with result quality. In the experiments, corresponding parameters could be: The amount of cellular ants is 31; Parameter ct is trail evaporation coefficient, if it is over certain limit, the probability of revisiting the same city could be increased. If it is lower than certain limitation, it could influence the convergence; Parameter [beta] is the heuristic information. From (Jiang Lingyan et al, 2007), the best parameters regions are 0.1 [less than or equal to] [alpha] [less than or equal to] 0.3 3 [less than or equal to] [beta] [less than or equal to] 6; Combining the Q- learning, the parameters are set as following: [gamma] = 0.8, [alpha] 0.2, [beta] = 4, Q = 2,p = 0.7.

4.1 Case 1" computational analysis on benchmark problems

For decomposition, we establish the extended codes based on GrThoery toolbox (http: // in software Matlab R2009a. Some existing functions are utilized for simulations, such as grMinCutSet function, grMaxFlows function, grDecOrd function and grValidation function.

With further verification of our algorithm, standard CVRP ( is also solved by those three algorithms. For each algorithm variant, ten independent simulations are taken per benchmark. With different iteration value, the average distances are illustrated in Table 1. The convergence of distributed multi-ant algorithm (DMA) is around 100 to 150 shown in Figure 1 as the benchmark problem E-nl01-kl4 presented by (Christofides Nicos et al, 1979). Therefore, it is quick to find out best solutions with our algorithm. To test the determinacy, we make experiments under iteration 1000. In most benchmark problems, best solutions are almost the same as the one that under around iteration 150. To limit disturbance, iteration value of our algorithm is set as 200.


In Table 2, the figures stand for best obtained fitness values (column Best solution) and average objective values of the best found feasible solutions (column Average). Compared to adaptive ant colony (AAC) and distributed ant cooperation without decomposition (DAC), DMA obtains better solutions to those 10 problems. Moreover, DMA runs less time (The unit of CPU time is second.). With incremental scale of problem, executed time increases slowly.

Table 3 illustrates the comparative results of best performances on the benchmark problem proposed by (Christofides Nicos et a1,1979) according to the literature (Richard Eglese, 2009). For the experimental results of branch-and-cut algorithm and the algorithm presented by Richard Eglese, performing larger problems by exponential growing costs too much time. But the performance on DMA is stable. The fluctuation of time consume is steady growth and the quality of solution is outperformed at the large scale CVRE By reason of decomposition, DMA plays well even on large scale problems, inducing the disadvantage of DMA that behaviors on smaller problems also take high time consume.

4.2 Case 2: computational analysis with an AreView graph data

Based on the customers address and request, we try to set forth the location of cities by utilizing an GIS software, "ArcView", creating geographic data and transformed to network data containing latitude vector and longitude vector. The data set of original map is loading automatically in ArcView software shown as Figure 2.

We store the digital map data of autologous city in ArcView. Its information is shown through digital road map utilized in several areas and edited layer structured data of spatial objects with latitude and longitude, buildings and so on. It could be found in Figure 3 amplifying Figure 2 for details. The building with red square is the distributed center and the buildings with red triangle are part of the distributed destinations. For the readability, we scale down the latitude and longitude in the digital road map by one hundred times.



4.2.1 Results comparison

The number of places in the city is 500. In other words, the scale of CVRP is 500. Performance potential parameter [delta] is 1. The simulations have undergone under three algorithms: AAC, DAC and DMA. Time consume time ordered by AAC, DAC and DMA is 1.7514e+003 s | 202.8906 s | 179.5156 s. Shortest distance by the same order is 1.4263e+004 | 1.4164e+003 | 1.3541e+003. From these data, DMA gets prior performance on best solution and costs less running time. With the amount of places increasing, distributed multi-ant cooperation with decomposition will play more excellent. These graphs also display that DAC is easy to trap into local solution, even though DMA takes more iteration.

4.2.2 Performance potential analysis

With the scale of problem increasing, decentralized algorithm becomes weaker than distributed one according to its interrelate variables restrained with each others. Interaction and cooperation are critical characters of distributed model where each ant communicates through reinforcement learning and renovates its next strategy even under additional places. The experiments run from two points.

In Table 4, performance potential parameter [delta] is 1. The scales of CVRP are 50,100,200,300,500 and 1000. Three algorithms are executed: adaptive ant colony (AAC), distributed ant cooperation without decomposition (DAC) and DMA. The results are in table 4. Best solutions in DAC and AAC are trapping into local ones. While the scale is small, DMA costs more time than DAC. Because complex structure process in DMA needs additional operation. However, DMA shows disadvantages in a large scale one. It takes less running time and gains better solutions than DAC and AAC.

As the scale of problem is 1000, simulation runs with different [delta] value. Noise misleads to trivial solutions. The efficiency of performance potential who is utilized to reduce noisy impact on the system is determined by parameter [delta] value. If [delta] is too small to zero, the influence from performance potential can be ignored. From Table 5, because of noisy disturbance, smaller [delta] costs much iteration and more time for best solutions. The difference between best solution and average solution is in inverse proportion to [delta] value. Consequently, performance potential plays a crucial role in distributed multi-ant algorithm.

5 Conclusion

Decomposition is usually used in decentralized model to scale down the problem into subsystems we can handle with. However, the relationship between subsystems is ignored easily, leading to local solution or non-solution. In this analysis, decomposition is undergoing through hybrid algorithms for large scale of CVRP in a distributed model. Cooperation and interaction are considered and solved by distributed multi-ant algorithm. Disturbance from circumstance is conquered by Potential function whose efficiency is verified from simulations. From the experiments, the algorithm has solved the large scale CVRP better and efficiently. Furthermore, the next work is further control of fluctuation on solutions.


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Jie Li, Yi Chai and Cao Yuan

Chongqing University


Received: November 2, 2010
Table 1: Results comparison under different iterations

                 Iteration: 200         Iteration: 50

Benchmark        Best                    Best
problem        solution      Time      solution     Time

E-n22-k4        252.610     56.3131    305.696     12.8750
E-n30-k3        393.427     79.1482    463.636     18.9688
E-n33-k4        511.208     84.1334    640.079     19.8906
E-n51-k5        416.059    114.7873    525.942     34.3906
E-n76-k7        528.711    187.1651    677.091     53.2969
E-n76-k8        536.684    189.2290    668.377     57.0469
E-n76-k10       559.024    242.3379    672.265     59.3671
E-n76-kl4       610.793    250.3661    784.509     61.2823
E-n101-k8       635.468    307.6808    854.114     89.3750
E-n101-kl4      677.007    386.9579    757.438     87.7813

Table 2: Solutions comparison with the Christofides et al. instances

                     AAC                DAC                DMA

Benchmark      Best    Average    Best    Average    Best    Average
problem      solution           solution           solution

E-n22-k4      310.524  317.191   305.696  309.958   252.610  257.295
E-n30-k3      466.714  472.816   458.745  465.064   393.427  400.841
E-n33-k4      651.878  659.097   640.079  651.852   511.208  527.384
E-n51-k5      520.126  523.726   514.174  518.546   416.059  419.572
E-n76-k7      677.091  680.483   672.844  673.267   528.711  535.547
E-n76-k8      686.901  688.339   668.377  676.189   536.684  539.960
E-n76-k10     679.881  683.925   672.265  675.061   559.024  560.275
E-n76-k14     689.764  690.598   672.265  675.696   610.793  613.670
E-n101-k8     816.362  819.362   732.735  751.482   635.468  637.117
E-n101-k14    927.577  934.523   883.894  898.789   677.007  678.563

Table 3: Best solution and time consume comparison with the
Christofides et al. instances

               Branch-and-cut   Richard Eglese's
                  algorithm       algorithm             DMA

Benchmark       Best              Best              Best
problem       solution   Time   solution  Time    solution    Time

E-n22-k4         375.28  0.2    252.614   0.08    252.610    56.3131
E-n30-k3        535.797   2     393.512    1      393.427    79.1482
E-n33-k4        837.672   2     511.263   0.6     511.208    84.1334
E-n51-k5        524.611   11    416.063    16     416.059   114.7873
E-n76-k7        682.563  3600   530.022   1103    528.711   187.1651
E-n76-k8        733.686  3600   537.239   636     536.684   189.2290
E-n76-k10       828.655  3600   559.233   3600    559.024   242.3379
E-n76-k14       989.257  3600   614.442   3600    610.793   250.3661
E-n101-k8       820.552  3600   639.744   2379    635.468   307.6808
E-n101-kl4     1049.534  3600   699.985   3600    677.007   386.9579

Table 4: Experimental results comparison of DAC and DMA(6=1)

              Adaptive ant colony         Decentralized algorithm

amount of         Best        Cost time       Best        Cost time
places          solution                    solution

Amount=50         58.76        3.4875         58.69        3.8653
Amount=100      157.4581       25.1541       111.86        20.6354
Amount=200      451.5714       74.1572       296.26        57.1351
Amount=300      2874.1674     558.1547       882.29       198.7326
Amount=500     1.4263e+004   1.7514e+004     2967.53      289.5187
Amount=1000    4.8417e+005   7.1541e+005     9233.76     3.5763e+003

                Distributed multi-ant

amount of         Best        Cost time
places          solution

Amount=50         56.86        4.1250
Amount=100       109.22        24.7343
Amount=200       235.72        50.5688
Amount=300       719.18       103.2497
Amount=500       1354.11      179.5156
Amount=1000      4617.43      815.9327

Table 5: Experimental results under different [delta] value
(places= 1000)

                   Distributed ant algorithm

Parameter        Best      Average
value          solution    solution    Cost time    Iteration

[delta]=0       4822.31    6728.34    3.2644e+004      545
[delta]=0.1     4792.93    6577.19    2.9883e+004      357
[delta]=0.2     4881.27    6221.69    2.3458e+004      288
[delta]=0.5     4632.36    5994.62    1.2336e+004      214
[delta]=0.8     4723.56    5938.43     899.3654        194
[delta]=1       4617.43    5769.27     815.9327        106
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Author:Li, Jie; Chai, Yi; Yuan, Cao
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Date:Sep 1, 2011
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