The generalized traveling salesman problem solved with ant algorithms
 CameliaM. Pintea†^{1}Email authorView ORCID ID profile,
 Petrică C. Pop†^{1} and
 Camelia Chira†^{1}
https://doi.org/10.1186/s4029401700489
© The Author(s) 2017
Received: 3 July 2017
Accepted: 31 July 2017
Published: 7 August 2017
Abstract
A well known \(\mathcal{NP}\)hard problem called the generalized traveling salesman problem (GTSP) is considered. In GTSP the nodes of a complete undirected graph are partitioned into clusters. The objective is to find a minimum cost tour passing through exactly one node from each cluster. An exact exponential time algorithm and an effective metaheuristic algorithm for the problem are presented. The metaheuristic proposed is a modified Ant Colony System (ACS) algorithm called reinforcing Ant Colony System which introduces new correction rules in the ACS algorithm. Computational results are reported for many standard test problems. The proposed algorithm is competitive with the other already proposed heuristics for the GTSP in both solution quality and computational time.
Keywords
Metaheuristics Intelligent agents Graphs Data clusteringBackground
Many combinatorial optimization problems are \(\mathcal{NP}\)hard, and the theory of \(\mathcal{NP}\)completeness has reduced hopes that \(\mathcal{NP}\)hard problems can be solved within polynomially bounded computation times (Dahlke 2008; Dunne 2008). Nevertheless, suboptimal solutions are sometimes easy to find. Consequently, there is much interest in approximation and heuristic algorithms that can find near optimal solutions within reasonable running time. Heuristic algorithms are typically among the best strategies in terms of efficiency and solution quality for problems of realistic size and complexity.
In contrast to individual heuristic algorithms that are designed to solve a specific problem, metaheuristics are strategic problem solving frameworks that can be adapted to solve a wide variety of problems. Metaheuristic algorithms are widely recognized as one of the most practical approaches for combinatorial optimization problems. The most representative metaheuristics include genetic algorithms, simulated annealing, tabu search and ant colony. Useful references regarding metaheuristic methods can be found in Glover and Kochenberger (2006).
The generalized traveling salesman problem (GTSP) has been introduced in Laporte and Nobert (1983) and Noon and Bean (1991). The GTSP has several applications to location and telecommunication problems. More information on these problems and their applications can be found in Fischetti et al. (1997, 2007) and Laporte and Nobert (1983).
Several approaches were considered for solving the GTSP: a branchandcut algorithm for Symmetric GTSP is described and analyzed in Fischetti et al. (1997), and Noon and Bean (1991) is given a Lagrangianbased approach for Asymmetric GTSP, in Snyder and Daskin (2006) is described a randomkey genetic algorithm for the GTSP, in Renaud and Boctor (1998) it is proposed an efficient composite heuristic for the Symmetric GTSP etc.
The aim of this paper is to provide an exact algorithm for the GTSP as well as an effective metaheuristic algorithm for the problem. The proposed metaheuristic is a modified version of Ant Colony System (ACS). Introduced in (Maniezzo 1992; Dorigo 1992), Ant System is a heuristic algorithm inspired by the observation of real ant colonies. ACS is used to solve hard combinatorial optimization problems including the traveling salesman problem (TSP).
Definition and complexity of the GTSP
A definition of generalized traveling salesman problem (TSP) based on Laporte and Nobert (1983) and Noon and Bean (1991) follows.
Let \(G=(V,E)\) be an nnode undirected graph whose edges are associated with nonnegative costs. We will assume w.l.o.g. that G is a complete graph (if there is no edge between two nodes, we can add it with an infinite cost).
Let \(V_1,...,V_p\) be a partition of V into p subsets called clusters (i.e. \(V=V_1 \cup V_2 \cup ... \cup V_p\) and \(V_l \cap V_k = \emptyset\) for all \(l,k \in \{1,...,p\}\)). We denote the cost of an edge \(e=\{i,j\}\in E\) by \(c_{ij}\).
The GTSP asks for finding a minimumcost tour H spanning a subset of nodes such that H contains exactly one node from each cluster \(V_i\), \(i\in \{1,...,p\}\). The problem involves two related decisions: choosing a node subset \(S\subseteq V\), such that \(S \cap V_k  = 1\), for all \(k=1,...,p\) and finding a minimum cost Hamiltonian cycle in the subgraph of G induced by S.
Such a cycle is called a Hamiltonian cycle. The GTSP is called symmetric if and only if the equality \(c(i,j)=c(j,i)\) holds for every \(i,j \in V\), where c is the cost function associated to the edges of G.
An exact algorithm for the GTSP
In this section, we present an algorithm that finds an exact solution to the GTSP.
Given a sequence \((V_{k_{1}},...,V_{k_{p}})\) in which the clusters are visited, we want to find the best feasible Hamiltonian tour \(H^*\) (w.r.t cost minimization), visiting the clusters according to the given sequence. This can be done in polynomial time by solving \(V_{k_{1}}\) shortest path problems as described below.
We construct a layered network, denoted by LN, with \(p+1\) layers corresponding to the clusters \(V_{k_{1}},...,V_{k_{p}}\) and in addition we duplicate the cluster \(V_{k_{1}}\). The layered network contains all the nodes of G plus some extra nodes \(v'\) for each \(v\in V_{k_1}\). There is an arc (i, j) for each \(i\in V_{k_l}\) and \(j\in V_{k_{l+1}}\) (\(l=1,...,p1\)), with the cost \(c_{ij}\) and an arc (i, h), \(i,h \in V_{k_l}\), (\(l=2,...,p\)) with the cost \(c_{ih}\). Moreover, there is an arc \((i,j')\) for each \(i\in V_{k_p}\) and \(j'\in V_{k_1}\) with the cost \(c_{ij'}\).
For any given \(v\in V_{k_1}\), are considered paths from v to \(w'\), \(w'\in V_{k_1}\), that visits exactly one node from each cluster \(V_{k_{2}},...,V_{k_{p}}\), hence it gives a feasible Hamiltonian tour. Conversely, every Hamiltonian tour visiting the clusters according to the sequence \((V_{k_{1}},...,V_{k_{p}})\) corresponds to a path in the layered network from a certain node \(v\in V_{k_1}\) to \(w'\in V_{k_1}\).
Therefore the best (w.r.t cost minimization) Hamiltonian tour \(H^*\) visiting the clusters in a given sequence can be found by determining all the shortest paths from each \(v\in V_{k_1}\) to each \(w'\in V_{k_1}\) with the property that visits exactly one node from cluster. The overall time complexity is then \(V_{k_1}O(m+n\log n)\), i.e. \(O(nm+n\log n)\) in the worst case. We can reduce the time by choosing \(V_{k_1}\) as the cluster with minimum cardinality. It should be noted that the above procedure leads to an \(O(nm+n\log n)\) time exact algorithm for the GTSP. Therefore we have established the following result:
Theorem
The above procedure provides an exact solution to the GSTP in \(O((p1)!(nm+n\log n))\) time, where n is the number of nodes, m is the number of edges and p is the number of clusters in the input graph.
Clearly, the algorithm presented is an exponential time algorithm unless the number of clusters p is fixed.
Ant Colony System
Ant System proposed in Dorigo (1992) and Maniezzo (1992) is a multiagent approach used for various combinatorial optimization problems. The algorithms were inspired by the observation of real ant colonies.
An ant can find shortest paths between food sources and a nest. While walking from food sources to the nest and vice versa, ants deposit on the ground a substance called pheromone, forming a pheromone trail. Ants can smell pheromone and, when choosing their way, they tend to choose paths marked by stronger pheromone concentrations. It has been shown that this pheromone trail following behavior employed by a colony of ants can lead to the emergence of shortest paths.
When an obstacle breaks the path ants try to get around the obstacle randomly choosing either way. If the two paths encircling the obstacle have the different length, more ants pass the shorter route on their continuous pendulum motion between the nest points in particular time interval. While each ant keeps marking its way by pheromone the shorter route attracts more pheromone concentrations and consequently more and more ants choose this route. This feedback finally leads to a stage where the entire ant colony uses the shortest path. There are many variations of the ant colony optimization applied on various classical problems.
Ant System make use of simple agents called ants which iteratively construct candidate solution to a combinatorial optimization problem. The ants solution construction is guided by pheromone trails and problem dependent heuristic information.
An individual ant constructs candidate solutions by starting with an empty solution and then iteratively adding solution components until a complete candidate solution is generated. Each point at which an ant has to decide which solution component to add to its current partial solution is called a choice point.
After the solution construction is completed, the ants give feedback on the solutions they have constructed by depositing pheromone on solution components which they have used in their solution. Solution components which are part of better solutions or are used by many ants will receive a higher amount of pheromone and, hence, will more likely be used by the ants in future iterations of the algorithm. To avoid the search getting stuck, typically before the pheromone trails get reinforced, all pheromone trails are decreased by a factor.

m ants are initially positioned on n nodes chosen according to some initialization rule, for example randomly.

Each ant builds a tour by repeatedly applying a stochastic greedy rule—the state transition rule.

While constructing its tour, an ant also modifies the amount of pheromone on the visited edges by applying the local updating rule.

Once all ants have terminated their tour, the amount of pheromone on edges is modified again by applying the global updating rule. As was the case in ant system, ants are guided, in building their tours by both heuristic information and by pheromone information: an edge with a high amount of pheromone is a very desirable choice.

The pheromone updating rules are designed so that they tend to give more pheromone to edges which should be visited by ants.
In such hybrid algorithms, the ants can be seen as guiding the local search by constructing promising initial solutions, because ants preferably use solution components which, earlier in the search, have been contained in good locally optimal solutions.
Reinforcing Ant Colony System for GTSP
An ACS for the GTSP it is introduced. In order to enforces the construction of a valid solution used in ACS a new algorithm called reinforcing Ant Colony System (RACS) it is elaborated with a new pheromone rule as in Pintea and Dumitrescu (2005) and pheromone evaporation technique as in Stützle and Hoos (1997).

Initially the ants are placed in the nodes of the graph, choosing randomly the clusters and also a random node from the chosen cluster.

At iteration \(t+1\) every ant moves to a new node from an unvisited cluster and the parameters controlling the algorithm are updated.

Each edge is labeled by a trail intensity. Let \(\tau _{ij}(t)\) represent the trail intensity of the edge (i, j) at time t. An ant decides which node is the next move with a probability that is based on the distance to that node (i.e. cost of the edge) and the amount of trail intensity on the connecting edge. The inverse of distance from a node to the next node is known as the visibility, \(\eta _{ij}=\frac{1}{c_{ij}}\).

At each time unit evaporation takes place. This is to stop the intensity trails increasing unbounded. The rate evaporation is denoted by \(\rho\), and its value is between 0 and 1. In order to stop ants visiting the same cluster in the same tour a tabu list is maintained. This prevents ants visiting clusters they have previously visited. The ant tabu list is cleared after each completed tour.

To favor the selection of an edge that has a high pheromone value, \(\tau\), and high visibility value, \(\eta\) a probability function \({p^{k}}_{iu}\) is considered. \({J^{k}}_{i}\) are the unvisited neighbors of node i by ant k and \(u\in {J^{k}}_{i}, u=V_k(y)\), being the node y from the unvisited cluster \(V_k\). This probability function is defined as follows:where \(\beta\) is a parameter used for tuning the relative importance of edge cost in selecting the next node. \({p^{k}}_{iu}\) is the probability of choosing \(j=u\), where \(u=V_k(y)\) is the next node, if \(q>q_{0}\) (the current node is i). If \(q\le q_{0}\) the next node j is chosen as follows:$$\begin{aligned} {p^{k}}_{iu}(t)= \frac{[\tau _{iu}(t)] [\eta _{iu}(t)]^{\beta }}{\Sigma _{o\in {J^{k}}_{i}}[\tau _{io}(t)] [\eta _{io}(t)]^{\beta }} , \end{aligned}$$(1)where q is a random variable uniformly distributed over [0, 1] and \(q_{0}\) is a parameter similar to the temperature in simulated annealing, \(0\le q_{0}\le 1\).$$\begin{aligned} j=argmax_{u\in J^{k}_{i}} \{\tau _{iu}(t) {[\eta _{iu}(t)]}^{\beta }\} , \end{aligned}$$(2)

After each transition the trail intensity is updated using the correction rule from Pintea and Dumitrescu (2005):where \(L^{+}\) is the cost of the best tour.$$\begin{aligned} \tau _{ij}(t+1)=(1\rho )\tau _{ij}(t)+\rho \frac{1}{n \cdot L^{+}} . \end{aligned}$$(3)

In ACS only the ant that generate the best tour is allowed to globally update the pheromone. The global update rule is applied to the edges belonging to the best tour. The correction rule iswhere \(\Delta \tau _{ij}(t)\) is the inverse cost of the best tour.$$\begin{aligned} \tau _{ij}(t+1)=(1\rho ) \tau _{ij}(t)+\rho \Delta \tau _{ij}(t) , \end{aligned}$$(4)

In order to avoid stagnation we used the pheromone evaporation technique introduced in Stützle and Hoos (1997). When the pheromone trail is over an upper bound \(\tau _{max}\), the pheromone trail is reinitialized. The pheromone evaporation is used after the global pheromone update rule.
Representation and computational results
A graphic representation of RACS for solving GTSP is shown in Fig. 1. At the beginning, the ants are in their nest and will start to search food in a specific area. Assuming that each cluster has specific food and the ants are capable to recognize this, they will choose each time a different cluster. The pheromone trails will guide the ants to the shorter path, a solution of GTSP, as in Fig. 1.
Reinforcing Ant Colony System algorithm for the GTSP

Problem The name of the test problem. The digits at the beginning of the name give the number of clusters (nc); those at the end give the number of nodes (n).

Opt.val. The optimal objective value for the problem (Snyder and Daskin 2006).

ACS, RACS, NN, GI ^{3}, GA The objective value returned by the included algorithms.
Reinforcing Ant Colony System (RACS) versus other algorithms
Problem  Opt. val.  ACS  RACS  NN  \(GI^{3}\)  GA 

11EIL51  174  174  174  181  174  174 
14ST70  316  316  316  326  316  316 
16EIL76  209  209  209  234  209  209 
16PR76  64,925  64,925  64,925  76,554  64,925  64,925 
20RAT99  497  497  497  551  497  497 
20KROA100  9711  9711  9711  10,760  9711  9711 
20KROB100  10,328  10,328  10,328  10,328  10,328  10,328 
20KROC100  9554  9554  9554  11,025  9554  9554 
20KROD100  9450  9450  9450  10,040  9450  9450 
20KROE100  9523  9523  9523  9763  9523  9523 
20RD100  3650  3650.4  3650  3966  3653  3650 
21EIL101  249  249  249  260  250  249 
21LIN105  8213  8215.4  8213  8225  8213  8213 
22PR107  27,898  27,904.4  27,898  28,017  27,898  27,898 
22PR124  36,605  36,635.4  36,605  38,432  36,762  36,605 
26BIER127  72,418  72,420.2  72,418  83,841  76,439  72,418 
28PR136  42,570  42,593.4  42,570  47,216  43,117  42,570 
29PR144  45,886  46,033  45,886  46,746  45,886  45,886 
30KROA150  11,018  11,029  11,018  11,712  11,018  11,018 
30KROB150  12,196  12,203.6  12,196  13,387  12,196  12,196 
31PR152  51,576  51,683.2  51,576.6  53,369  51,820  51,576 
32U159  22,664  22,729.2  22,665.6  26,869  23,254  22,664 
39RAT195  854  856.4  854  1048  854  854.2 
40D198  10,557  10,575.2  10,557.6  12,038  10,620  10,557 
40KROA200  13,406  13,466.8  13,407.2  16,415  13,406  13,406 
40KROB200  13,111  13,157.8  13,111  17,945  13,111  13,113.4 
45TS225  68,345  69,547.2  68,360.6  72,691  68,756  68,435.2 
46PR226  64,007  64,289.4  64,028  68,045  64,007  64,007 
53GIL262  1013  1015.8  1015.2  1152  1064  1016.2 
53PR264  29,549  29,825  29,549.6  33,552  29,655  29,549 
60PR299  22,615  23,039.6  22,668.2  27,229  23,119  22,631 
64LIN318  20,765  21,738.8  20,790.2  24,626  21,719  20,836.2 
80RD400  6361  6559.4  6416.2  7996  6439  6509 
84FL417  9651  9766.2  9706.4  10,553  9697  9653 
88PR439  60,099  64,017.6  60,570.6  67,428  62,215  60,316.8 
89PCB442  21,657  22,137.8  21,806.4  26,756  22,936  22,134 
Conclusions
The basic idea of ACS is that of simulating the behavior of a set of agents cooperating to solve an optimization problem by means of simple communications. The algorithm introduced to solve the GTSP, called RACS, an ACSbased algorithm with new correction rules. The computational results of the proposed algorithm are good and competitive in both solution quality and computational time with the existing heuristics (Renaud and Boctor 1998; Snyder and Daskin 2006). The RACS results can be improved with better values of parameters or using hybrid algorithms. Some disadvantages refer the multiple parameters used for the algorithm and the high hardware resources requirements.
Notes
Declarations
Authors' contributions
Specifically, PCP conceived the idea of the paper. All the authors developed the simulation models. CMP executed the simulation experiments. All authors analyzed the tests results and wrote the paper. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Funding
The authors received no specific funding for the manuscript.
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