On investigation of interdependence between subproblems of the Travelling Thief Problem
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Abstract
In this paper, the interdependence between subproblems in a complex overall problem is investigated using a benchmark problem called Travelling Thief Problem (TTP), which is a combination of Travelling Salesman Problem (TSP) and Knapsack Problem (KP). First, the analysis on the mathematical formulation shows that it is impossible to decompose the problem into independent subproblems due to the nonlinear relationship in the objective function. Therefore, the algorithm for TTP is not straightforward although each subproblem alone has been investigated intensively. Then, two metaheuristics are proposed for TTP. One is the Cooperative Coevolution (CC) that solves the subproblems separately and transfers the information between them in each generation. The other is the Memetic Algorithm (MA) that solves TTP as a whole. The comparative results showed that MA consistently obtained much better results than both the standard and dynamic versions of CC within comparable computational budget. This indicates the importance of considering the interdependence between subproblems in an overall problem like TTP.
Keywords
Combinatorial optimization Evolutionary computation Cooperative Coevolution Travelling Thief Problem Interdependence1 Introduction
Realworld problems often involve a large number of decision variables and constraints, making it impossible to find the global optimal solution within the given time budget. When tackling largescale optimization problems, the divideandconquer approach is commonly adopted to decompose the overall problem into smaller subproblems (Boyd et al. 2007; Omidvar et al. 2014; Mei et al. 2014a). For many realworld problems, the subproblems are naturally defined. For example, in supply chain management (Thomas and Griffin 1996; Stadtler 2005; Melo et al. 2009), each stage or operation such as procurement, production and distribution can correspond to a subproblem. However, it is often inevitable that such subproblems are still interdependent on each other. As mentioned in Michalewicz (2012), one of the main complexity of realworld problems is the interdependence between subproblems, which makes many conventional approaches ineffective. As a result, even if each subproblem has been intensively investigated, it is still an open question how to integrate the highquality partial solutions for the subproblems to obtain a global optimum or at least a highquality solution for the overall problem. Therefore, it is important to investigate how to tackle the interdependence between subproblems.
To facilitate such investigation, Bonyadi et al. (2013) recently defined a benchmark problem called Travelling Thief Problem (TTP). TTP is a combination of two wellknown combinatorial optimization problems, i.e., Travelling Salesman Problem (TSP) and Knapsack Problem (KP). Specifically, a thief is to visit a set of cities and pick some items from the cities to put in a rented knapsack. Each item has a value and a weight. The knapsack has a limited capacity that cannot be exceeded by the total weight of the picked items. In the end, the thief has to pay the rent for the knapsack, which depends on the travel time. TTP aims to find a tour for the thief to visit all the cities exactly once, pick some items along the way and finally return to the starting city, so that the benefit of the visit, which is the total value of the picked items minus the rent of the knapsack, is maximized. Since TSP and KP have been intensively investigated, TTP facilitates to concentrate on the interdependence between subproblems.
An example of potential relevant realworld applications of TTP is the capacitated arc routing problem (Dror 2000) with service profit. Although there have been extensive studies for solving various forms of the capacitated arc routing problem depending on different practical scenarios [e.g., the classic model (Mei et al. 2009a, b; Tang et al. 2009; Fu et al. 2010), the multiobjective model (Mei et al. 2011a), the stochastic model (Mei et al. 2010), the periodic model (Mei et al. 2011b) and the largescale model (Mei et al. 2013, 2014a, b)], two important practical issues have been overlooked so far. One is the service profit, which is the profit that can be gained by serving the customers. Each customer may have a different profit/demand ratio. Thus, given the limited capacity of the vehicle, one may need to serve only a subset of the customers with higher profit/demand ratios to maximize the final benefit. The other factor is the dependency of the travel cost of the vehicle on its load. Obviously, a heavier load of the vehicle leads to a higher consumption of petrol, and thus a higher travel cost. In this case, it would be more desirable to serve the customers with a higher demand first to save the travel cost of the subsequent route. With the above factors taken into account, the resultant arc routing problem can be modelled as a TTP.
In this paper, the interdependence of TSP and KP in TTP is investigated both theoretically and empirically. First, the mathematical formulation of TTP is developed and analysed to show how the two subproblems interact with each other. Then, a Cooperative Coevolution algorithm (CC) (including a standard and a dynamic version) and a Memetic Algorithm (MA) are developed. CC solves TSP and KP separately, and transfers the information between them in each generation. MA solves TTP as a whole. Standard crossover and mutation operators are employed. The proposed algorithms were compared on the benchmark instances proposed in Bonyadi et al. (2013), and the results showed that MA managed to obtain much better solutions than CC for all the test instances. In other words, with the same crossover and mutation operators for each subproblem, a more proper way of integrating the optimization process of the subproblems can result in a significantly better solution. This demonstrates that considering the interdependence between subproblems is important for obtaining highquality solution for the overall problem. Moreover, the theoretical analysis establishes the fundamental understanding of the problem.
The rest of the paper is organized as follows: TTP is formulated and analysed in Sect. 2. After that, CC and MA are depicted in Sect. 3. Then, the experimental studies are carried out in Sect. 4. Finally, the conclusion and future work are described in Sect. 5.
2 Travelling Thief Problem
In this section, TTP is introduced. The mathematical formulation is first described in Sect. 2.1 and then analysed in Sect. 2.2, particularly in terms of the interdependence between the TSP and KP decision variables in the objective function.
2.1 Mathematical formulation
2.2 Problem analysis
From Eqs. (3)–(10), one can see that in the constraints, the decision variables \(\mathbf {x}\), \(\mathbf {y}\) and \(\mathbf {z}\) are independent of each other. Obviously, Eqs. (3)–(5) only consist of \(\mathbf {x}\), Eq. (6) only includes \(\mathbf {y}\), and Eqs. (7)–(9) solely involve \(\mathbf {z}\). However, as shown in Eqs. (11)–(15), there is a nonlinear relationship between the variables in the objective \(\mathcal {G}(\mathbf {x},\mathbf {y},\mathbf {z})\). For example, Eq. (15) includes the product of the \(z_{ij}\)’s and \(x_{ij}\)’s, and Eq. (13) involves the quotient of the \(x_{ij}\)’s and \(z_{ij}\)’s. In the above formulation, it is difficult to find an additively separation of \(\mathcal {G}(\mathbf {x},\mathbf {y},\mathbf {z})\), if not impossible. That is, one cannot find the functions \(\mathcal {G}_1(\mathbf {x})\), \(\mathcal {G}_2(\mathbf {y})\) and \(\mathcal {G}_3(\mathbf {z})\) such that \(\mathcal {G}(\mathbf {x},\mathbf {y},\mathbf {z}) = \mathcal {G}_1(\mathbf {x})+\mathcal {G}_2(\mathbf {y})+\mathcal {G}_3(\mathbf {z})\). In other words, it is impossible to decompose the overall problem \(\mathcal {P}(\mathbf {x},\mathbf {y},\mathbf {z})\) into independent subproblems \(\mathcal {P}_1(\mathbf {x})\), \(\mathcal {P}_2(\mathbf {y})\) and \(\mathcal {P}_3(\mathbf {z})\) such that \(\mathcal {OBJ}(\mathcal {P}) = \mathcal {OBJ}(\mathcal {P}_1)+\mathcal {OBJ}(\mathcal {P}_2)+\mathcal {OBJ}(\mathcal {P}_3)\), where \(\mathcal {OBJ}(\mathcal {P})\) stands for the objective function of the problem \(\mathcal {P}\).
The above analysis enables us to better understand the reason why solving the subproblems individually can hardly lead to highquality solutions. Take TTP as an example, in Bonyadi et al. (2013), a simple decomposition of TTP into TSP and KP was designed by setting \(\mathcal {G}_1(\mathbf {x},\mathbf {y}) = \mathcal {G}(\mathbf {x},\mathbf {y},\mathbf {0})\cdot v_{\max }/R = td(\mathbf {x})\) and \(\mathcal {G}_3(\mathbf {z}) = \sum _{i=1}^{m}\sum _{j=1}^{n}b_iz_{ij}\), where \(td(\mathbf {x})\) stands for the total distance of the TSP tour \(\mathbf {x}\), which is independent of the starting city decision variables \(\mathbf {y}\). In other words, TTP was decomposed into TSP and KP with standard objective functions (minimizing total distance for TSP and maximizing total value for KP). However, the preliminary experimental results showed that such decomposition cannot lead to good TTP solutions. Based on the above analysis, the reason is that the original objective \(\mathcal {G}(\mathbf {x},\mathbf {y},\mathbf {z})\) is not the summation of the \(\mathcal {G}_1(\mathbf {x},\mathbf {y})\) and \(\mathcal {G}_3(\mathbf {z})\). Thus, optimizing \(\mathcal {G}_1(\mathbf {x},\mathbf {y})\) and \(\mathcal {G}_3(\mathbf {z})\) is not directly related to optimizing \(\mathcal {G}(\mathbf {x},\mathbf {y},\mathbf {z})\) itself.
To summarize, the mathematical formulation of TTP shows that the objective \(\mathcal {G}(\mathbf {x},\mathbf {y},\mathbf {z})\) is not additively separable. Therefore, one cannot expect that solving the TSP and KP subproblems individually will obtain competitive TTP solutions since their objectives are not fully correlated. In this paper, each solution is evaluated directly with respect to the original objective \(\mathcal {G}(\mathbf {x},\mathbf {y},\mathbf {z})\) provided that there is no TSP and KP objective functions strongly correlated to \(\mathcal {G}(\mathbf {x},\mathbf {y},\mathbf {z})\) so far.
3 Solving TTP with metaheuristics
According to the mathematical formulation described in Sect. 2.1, it is seen that TTP is a complex nonlinear integer optimization problem. It is also obvious that TTP is NPhard, since it can be reduced to the TSP when \(w_i = b_i = 0, \forall i = 1,\ldots ,m\), which has been proved to be NPhard (Papadimitriou 1977). In this situation, metaheuristics are good alternatives as it has been demonstrated to be able to obtain competitive solutions within a reasonable computational budget for various NPhard combinatorial optimization problems (Mei et al. 2009a, 2011a, b; Tang et al. 2009; Fuellerer et al. 2010; Bolduc et al. 2010; De Giovanni and Pezzella 2010; Sbihi 2010). In the following, two metaheuristic approaches are proposed for solving TTP. The former is a Cooperative Coevolution algorithm (CC) (Potter and De Jong 1994) that optimizes the TSP and KP decision variables separately and exchange the information between them regularly. The latter is a Memetic Algorithm (MA) (Moscato 1989) which considers TTP as a whole and optimizes all the decision variables simultaneously. Next, the two algorithms are described respectively. Then, their computational complexities are analysed.
3.1 Cooperative Coevolution
The two subproblems of TTP, i.e., TSP and KP, are both wellknown combinatorial optimization problems. They have been investigated intensively, and various algorithms have been proposed for solving them (Lin and Kernighan 1973; Dorigo and Gambardella 1997; Horowitz and Sahni 1974; Fidanova 2007). However, the algorithm for TTP is not straightforward due to the interdependence between the TSP and KP decision variables in the objective. In this case, an intuitive approach is to optimize the TSP and KP decision variables separately and transfer the information between them during the optimization. The Cooperative Coevolution (CC) (Potter and De Jong 1994) is a standard approach to this end. It decomposes the decision variables into a number of subcomponents and evolves them separately. The transfer of information is conducted by the collaboration between the subcomponents occurring in evaluation. When evaluating an individual of a subcomponent, it is combined with the collaborators (e.g., the individual with the best fitness value) that are selected from the other subcomponents. Then, its fitness is set corresponding to that of the combined individual(s) of the overall problem.

Collaborator selection pressure: The degree of greediness of selecting a collaborator. In general, if the subcomponents are independent from each other, then one should set the strongest selection pressure, i.e., select the bestsofar individuals as collaborators. On the other hand, for the nonlinearly interdependent subcomponents, a weak selection pressure is more promising, e.g., selecting the collaborators randomly (Wiegand et al. 2001). Another empirical study (Stoen 2006) also showed that proportional selection performs better than random selection.

Collaboration pool size: The number of collaborators selected from each other subcomponent. A larger pool size leads to a more comprehensive exploration of the solution space and thus a better final solution quality. However, it induces a higher time complexity since it requires more fitness evaluations to obtain the fitness of an individual. A better alternative is to adaptively change the pool size during the optimization process (Panait and Luke 2005).

Collaboration credit assignment: The method of assigning the fitness value based on the objective values obtained together with the collaborators. The empirical studies (Wiegand et al. 2001) showed that the optimistic strategy that assigns the fitness of an individual as the objective value of its best collaboration generally leads to the best results.
The issue of collaboration in CC has been overlooked so far, and most of the limited studies are focused on continuous optimization problems (Potter and De Jong 1994; Potter 1997; Wiegand et al. 2001; Bull 2001; Panait and Luke 2005; Stoen 2006). For the combinatorial optimization problems, Bonyadi and Moghaddam (2009) proposed a CC for multiprocessor task scheduling, in which the collaboration pool size equals the population size, i.e., all the individuals are selected as collaborators. Ibrahimov et al. (2012) proposed a CC for a simple twosilo supply chain problem, which selects the best individual plus two random individuals for collaboration.
Conventional crossover and mutation operators for the TSP and KP are adopted here. Specifically, the ordered crossover (Oliver et al. 1987) and 2opt (Croes 1958) operators are used for the TSP, and the traditional onepoint crossover, flip and exchange operators are used for the KP. They are described in details as follows:
Ordered Crossover (OX): Given two tours \(\mathbf {x}_1 = (x_{11}, \ldots , x_{1n})\) and \(\mathbf {x}_2 = (x_{21}, \ldots , x_{2n})\), two cutting positions \(1\le p \le q \le n\) are randomly selected, and \((x_{1p},\ldots ,x_{1q})\) is copied to the corresponding positions of the offspring \((x'_p,\ldots ,x'_q)\). After that, \(\mathbf {x}_2\) is scanned from position \(q+1\) to the end and then from beginning to position \(q\). The unduplicated elements are placed one after another in \(\mathbf {x}'\) from position \(q+1\) to the end, and then from beginning to position \(p1\). The complexity of the OX operator is \(O(n)\).
2opt: Given a tour \(\mathbf {x} = (x_1, \ldots , x_n)\), two cutting positions \(1\le p < q \le n\) are chosen and the subtour in between is inverted. The offspring is \(\mathbf {x}' = (x_1, \ldots , x_{p1}, x_q, x_{q1}, \ldots , x_p, x_{q+1}, \ldots , x_n)\). During the local search, the neighbourhood size defined by the 2opt operator is \(O(n^2)\).
OnePoint Crossover (OPX): Given two picking plans \(\mathbf {z}_1 = (z_{11}, \ldots , z_{1m})\) and \(\mathbf {z}_2 = (z_{21}, \ldots , z_{2m})\), a cutting position \(1 \le p \le m\) is picked, and then the offspring is set to \(\mathbf {z}' = (z_{11}, \ldots z_{1(p1)}, z_{2p}, \ldots z_{2m})\). The OPX operator has a computational complexity of \(O(m)\).
Flip: Given a picking plan \(\mathbf {z} = (z_1, \ldots , z_m)\), a position \(1 \le p \le m\) is selected, and \(z_p\) is replaced by a different value \(z_p' \in A_p \cup \{0\}\). During the local search, the neighbourhood size defined by the Flip operator is \(O(\prod _{i=1}^{m}A_i) = O(nm)\).
Exchange (EX): Given a picking plan \(\mathbf {z} = (z_1, \ldots , z_m)\), two positions \(1 \le p < q \le m\) are selected, and the values of \(z_p\) and \(z_q\) are exchanged. To keep feasibility, it is required that \(z_q \in A_p \cup \{0\}\) and \(z_p \in A_q \cup \{0\}\). During the local search, the neighbourhood size defined by the EX operator is \(O(m^2)\).
3.2 Memetic Algorithm
Based on the above crossover and local search operators, a MA is proposed for the overall problem. In the MA, the TSP and KP are solved together by combining the aforementioned operators. To be specific, the crossover of a TTP solution is conducted by applying the OX and OPX operators to its tour and picking plan simultaneously. Then, during the local search, the neighbourhood of the current solution is defined as the union of the neighbourhoods induced by all the 2opt, Flip and EX operators.
3.3 Computational complexity analysis
4 Experimental studies
In this section, the proposed CC and MA are compared on the TTP benchmark instances to investigate their performance.
4.1 Experimental settings
A representative subset of the TTP benchmark instances generated by Bonyadi et al.^{1} is selected to compare the performance of the proposed algorithms. The benchmark set includes instances with various features with the number of cities \(n\) from 10 to 100 and number of items \(m\) from 10 to 150. For each parameter setting of the problem, 10 instances were generated randomly. As a result, there are totally 540 instances. For the sake of simplicity, for each parameter setting with \(10 \le n,m \le 100\), only the first instance is chosen from the 10 generated instances as a representative. The selected subset consists of 39 instances. Note that for some instances, the benefit may be negative due to the insufficient values of the items compared to the knapsack rent.
The parameter settings of the compared algorithms
Parameter  Description  Value 

\(k\)  Number of collaborators in CC  1, 3 
\(N\)  Population (subpopulation for CC) size  30 
\(N_{off}\)  Number of offsprings  \(6 \cdot psize\) 
\(P_{ls}\)  Probability of local search  0.2 
\(g_{\max }\)  Maximal generations  100 for MA; \(100/k\) for CCs 
4.2 Results and discussions
Mean and standard deviation of the benefits obtained by the 30 independent runs of the proposed algorithms on the benchmark instances from 1010125 to 2030175
Instance  \(k=1\)  \(k=3\)  MA  

SCC  DCC  SCC  DCC  
1010125  
Mean  \(\)17,115.3  \(\)17,192.8  \(\)16,820.7  \(\)16,773.1  \(\) 16,566.3 
SD  461.5  427.1  406.6  322.5  0.0 
1010150  
Mean  1,887.2  1,775.1  1,925.8  1,904.8  1,994.5 
SD  134.7  192.5  66.1  59.2  0.0 
1010175  
Mean  \(\)2,258.4  \(\)2,629.4  \(\)1,925.7  \(\)1,980.2  \(\) 1,877.6 
SD  402.5  417.1  137.9  223.0  0.0 
1015125  
Mean  217.6  111.7  349.2  260.7  389.4 
SD  154.6  196.6  65.2  115.3  0.0 
1015150  
Mean  1,028.6  846.7  1,188.2  1,119.6  1,295.1 
SD  270.7  287.6  144.2  197.1  0.0 
1015175  
Mean  \(\)6,525.7  \(\)6,680.0  \(\)6,332.4  \(\)6,341.8  \(\) 6,261.8 
SD  390.0  345.1  97.3  92.5  0.0 
2010125  
Mean  721.3  620.6  683.8  662.5  901.6 
SD  141.5  99.4  47.9  70.1  0.0 
2010150  
Mean  1,995.5  1,955.7  1,939.4  1,933.5  2,238.2 
SD  148.9  180.4  87.6  96.6  1.0 
2010175  
Mean  \(\)1,935.1  \(\)2,105.2  \(\)1,729.8  \(\)1,859.7  \(\) 1,596.7 
SD  249.2  306.4  180.5  256.3  0.0 
2020125  
Mean  \(\)1,777.8  \(\)1,920.3  \(\)1,644.1  \(\)1,800.5  \(\) 1,581.7 
SD  202.8  361.8  106.1  192.3  0.0 
2020150  
Mean  \(\)2,518.9  \(\)2,635.6  \(\)2,048.0  \(\)2,037.7  \(\) 1,685.8 
SD  603.7  612.4  394.9  469.6  0.0 
2020175  
Mean  \(\)44,352.9  \(\)45,522.7  \(\)44,058.3  \(\)44,309.7  \(\) 43,541.8 
SD  933.4  1,386.7  542.3  756.7  0.0 
2030125  
Mean  \(\)1,624.3  \(\)1,814.8  \(\)1,350.0  \(\)1,515.0  \(\) 1,219.5 
SD  543.6  500.9  111.8  329.9  4.8 
2030150  
Mean  \(\)1,013.0  \(\)989.3  \(\)598.6  \(\)760.0  \(\) 337.0 
SD  708.6  803.3  316.8  409.6  0.0 
2030175  
Mean  \(\)18,494.8  \(\)19,204.0  \(\)18,393.9  \(\)18,640.2  \(\) 17,226.9 
SD  1,186.5  1,443.7  852.4  1,171.0  135.1 
Mean and standard deviation of the benefits obtained by the 30 independent runs of the proposed algorithms on the benchmark instances from 5015125 to 5075175
Instance  \(k=1\)  \(k=3\)  MA  

SCC  DCC  SCC  DCC  
5015125  
Mean  \(\)1,266.1  \(\)1,493.3  \(\)1,326.6  \(\)1,358.1  \(\) 1,151.7 
SD  136.1  217.5  112.9  142.9  15.6 
5015150  
Mean  \(\)1,476.9  \(\)1,775.1  \(\)1,474.4  \(\)1,709.2  \(\) 1,100.2 
SD  232.4  347.4  222.7  297.8  62.9 
5015175  
Mean  \(\)23,999.8  \(\)24,523.5  \(\)24,104.9  \(\)24,372.0  \(\) 23,221.5 
SD  431.7  525.1  305.5  470.5  0.0 
5025125  
Mean  \(\)12,569.7  \(\)12,964.8  \(\)12,393.4  \(\)12,363.0  \(\) 11,701.4 
SD  523.7  599.8  415.4  412.2  0.0 
5025150  
Mean  \(\)153,764.3  \(\)154,996.7  \(\)153,233.6  \(\)154,297.0  \(\) 150,781.9 
SD  1,766.8  943.0  1,962.8  1,746.6  210.8 
5025175  
Mean  \(\)27,582.0  \(\)27,816.8  \(\)27,460.2  \(\)27,311.9  \(\) 26,022.0 
SD  667.3  884.9  708.3  814.9  153.0 
5050125  
Mean  \(\)20,895.9  \(\)21,536.8  \(\)20,599.1  \(\)21,280.3  \(\) 19,495.6 
SD  1,002.3  1,120.2  971.7  980.2  283.6 
5050150  
Mean  \(\)125,718.1  \(\)126,632.7  \(\)124,665.5  \(\)125,709.3  \(\) 123,097.5 
SD  1,759.2  3,111.9  1,347.8  2,246.2  344.1 
5050175  
Mean  \(\)258,700.5  \(\)262,492.2  \(\)257,906.2  \(\)259,926.5  \(\) 253,588.4 
SD  3,610.6  4,451.3  4,150.0  4,269.2  930.4 
5075125  
Mean  \(\)57,809.0  \(\)59,733.5  \(\)57,730.3  \(\)58,590.6  \(\) 56,247.7 
SD  1,651.9  1,631.1  1,507.8  2,077.8  615.9 
5075150  
Mean  \(\)11,871.2  \(\)13,018.4  \(\)11,549.8  \(\)12,519.2  \(\) 8,988.6 
SD  1,899.1  2,085.6  2,094.8  2,028.2  47.4 
5075175  
Mean  17,035.7  15,965.1  17,174.0  16,964.9  18,931.6 
SD  1,445.8  1,772.3  870.0  948.1  73.6 
Mean and standard deviation of the benefits obtained by the 30 independent runs of the proposed algorithms on the benchmark instances from 10010125 to 100100175
Instance  \(k=1\)  \(k=3\)  MA  

SCC  DCC  SCC  DCC  
10010125  
Mean  \(\)1,598.6  \(\)1,603.8  \(\)1,521.1  \(\)1,550.6  \(\) 1,452.0 
SD  104.0  81.1  58.4  71.8  8.7 
10010150  
Mean  \(\)1,708.5  \(\)1,919.0  \(\)1,940.0  \(\)1,965.5  \(\) 1,620.0 
SD  77.0  202.0  90.2  108.0  36.0 
10010175  
Mean  \(\)9,974.2  \(\)10,270.7  \(\)9,838.7  \(\)9,835.8  \(\) 9,420.8 
SD  318.8  326.5  254.7  266.9  38.1 
10025125  
Mean  \(\)17,731.5  \(\)17,990.0  \(\)17,534.1  \(\)17,734.0  \(\) 16,916.2 
SD  440.9  705.2  320.3  414.5  92.3 
10025150  
Mean  \(\)12,558.3  \(\)12,861.6  \(\)12,474.2  \(\)12,642.6  \(\) 11,708.5 
SD  450.9  593.0  321.2  422.2  147.1 
10025175  
Mean  \(\)83,477.8  \(\)84,017.5  \(\)83,679.1  \(\)83,612.2  \(\) 81,099.3 
SD  765.9  1,064.0  754.0  1,319.4  597.7 
10050125  
Mean  \(\)89,396.4  \(\)89,761.7  \(\)89,699.0  \(\)89,785.8  \(\) 87,898.0 
SD  1,090.1  956.5  1,241.0  1,232.4  477.6 
10050150  
Mean  \(\)26,801.8  \(\)27,615.3  \(\)26,980.4  \(\)27,363.8  \(\) 25,571.8 
SD  745.9  1,262.0  652.4  969.4  95.2 
10050175  
Mean  \(\)47,060.8  \(\)47,577.0  \(\)47,162.0  \(\)47,789.6  \(\) 44,965.7 
SD  1,046.4  1,492.4  1,255.3  1,107.5  296.5 
100100125  
Mean  1,222.8  1,148.5  957.8  829.8  2,282.0 
SD  795.3  845.4  429.9  505.3  150.1 
100100150  
Mean  \(\)66,873.1  \(\)67,377.2  \(\)66,590.0  \(\)67,453.6  \(\) 62,986.7 
SD  1,885.5  2,244.6  1,776.7  2,199.2  729.8 
100100175  
Mean  \(\)141,786.2  \(\)141,796.0  \(\)141,958.2  \(\)142,773.6  \(\) 135,169.7 
SD  3,118.6  3,503.9  3,435.2  3,688.5  1,237.0 
It can been seen that MA obtained significantly better results than SCC and DCC with both \(k=1\) and \(k=3\) on all the 39 benchmark instances, with larger mean and smaller standard deviation. This implies that MA can obtain better solutions more reliably. For both tested \(k\) values, SCC generally obtained better solutions than DCC, which indicates that it is better to update the collaborators after solving all the subproblems. This is because updating the collaborators too frequently will mislead the search to a local optimum quickly and make it difficult to jump out of the local optimum due to the strong selection pressure.
Among the proposed CC algorithms, SCC and DCC with \(k=3\) outperformed the ones with \(k=1\) for all the instances except the large ones (\(n=100\) and \(m \ge 50\)). This shows that for the instances with small or medium solution space, a larger \(k\) can lead to a better result since it has a wider neighborhood and thus is stronger in exploration. On the other hand, for the largescale instances, a smaller \(k\) is a better option to allow more generations given a fixed total number of fitness evaluations.
The benefits of the best solution and average number of fitness evaluations of the proposed algorithms from 1010125 to 2030175
Instance  \(k=1\)  \(k=3\)  MA  

SCC  DCC  SCC  DCC  
1010125  
Benefit  \(\) 16,566.3  \(\) 16,566.3  \(\) 16,566.3  \(\) 16,566.3  \(\) 16,566.3 
No. eval  1.82e+06  2.08e+06  1.59e+06  1.64e+06  1.78e+06 
1010150  
Benefit  1,994.5  1,994.5  1,963.6  1,963.6  1,994.5 
No. eval  1.88e+06  1.88e+06  1.57e+06  1.47e+06  2.17e+06 
1010175  
Benefit  \(\) 1,877.6  \(\) 1,877.6  \(\) 1,877.6  \(\) 1,877.6  \(\) 1,877.6 
No. eval  2.28e+06  2.62e+06  1.89e+06  1.70e+06  2.41e+06 
1015125  
Benefit  389.4  389.4  389.4  389.4  389.4 
No. eval  3.10e+06  3.39e+06  2.57e+06  2.39e+06  3.53e+06 
1015150  
Benefit  1,295.1  1,241.8  1,295.1  1,241.8  1,295.1 
No. eval  3.53e+06  3.99e+06  2.51e+06  2.25e+06  3.41e+06 
1015175  
Benefit  \(\) 6,261.8  \(\) 6,261.8  \(\) 6,261.8  \(\)6,317.3  \(\) 6,261.8 
No. eval  4.57e+06  5.61e+06  3.27e+06  2.67e+06  5.24e+06 
2010125  
Benefit  901.6  828.7  716.8  709.2  901.6 
No. eval  4.74e+06  4.44e+06  5.05e+06  4.68e+06  5.03e+06 
2010150  
Benefit  2,238.4  2,238.4  2,064.8  2,064.8  2,238.4 
No. eval  6.18e+06  5.73e+06  5.66e+06  5.22e+06  6.53e+06 
2010175  
Benefit  \(\) 1,596.7  \(\) 1,596.7  \(\) 1,596.7  \(\) 1,596.7  \(\) 1,596.7 
No. eval  5.82e+06  5.88e+06  5.56e+06  4.87e+06  8.71e+06 
2020125  
Benefit  \(\) 1,581.7  \(\) 1,581.7  \(\) 1,581.7  \(\) 1,581.7  \(\) 1,581.7 
No. eval  8.63e+06  1.12e+07  8.34e+06  7.54e+06  1.17e+07 
2020150  
Benefit  \(\) 1,685.8  \(\) 1,685.8  \(\) 1,685.8  \(\) 1,685.8  \(\) 1,685.8 
No. eval  9.41e+06  8.90e+06  1.05e+07  7.40e+06  1.16e+07 
2020175  
Benefit  \(\) 43,541.8  \(\) 43,541.8  \(\) 43,541.8  \(\) 43,541.8  \(\) 43,541.8 
No. eval  1.24e+07  1.49e+07  1.11e+07  8.43e+06  1.26e+07 
2030125  
Benefit  \(\) 1,218.3  \(\)1,237.3  \(\) 1,218.3  \(\) 1,218.3  \(\) 1,218.3 
No. eval  1.63e+07  2.21e+07  1.47e+07  1.04e+07  2.22e+07 
2030150  
Benefit  \(\) 337.0  \(\) 337.0  \(\) 337.0  \(\) 337.0  \(\) 337.0 
No. eval  1.85e+07  2.31e+07  1.73e+07  1.17e+07  1.34e+07 
2030175  
Benefit  \(\) 17,191.4  \(\) 17,191.4  \(\) 17,191.4  \(\) 17,191.4  \(\) 17,191.4 
No. eval  2.42e+07  1.78e+07  2.41e+07  1.14e+07  1.28e+07 
The benefits of the best solution and average number of fitness evaluations of the proposed algorithms from 5015125 to 5075175
Instance  \(k=1\)  \(k=3\)  MA  

SCC  DCC  SCC  DCC  
5015125  
Benefit  \(\) 1,136.1  \(\)1,160.6  \(\)1,213.4  \(\)1,236.9  \(\) 1,136.1 
No. eval  4.12e+07  2.92e+07  4.12e+07  3.36e+07  4.30e+07 
5015150  
Benefit  \(\) 1,059.5  \(\)1,189.2  \(\)1,194.9  \(\)1,294.1  \(\) 1,059.5 
No. eval  4.85e+07  3.10e+07  4.43e+07  3.24e+07  4.36e+07 
5015175  
Benefit  \(\) 23,221.5  \(\)23,542.6  \(\)23,347.0  \(\) 23,221.5  \(\) 23,221.5 
No. eval  3.94e+07  3.32e+07  3.63e+07  3.10e+07  4.26e+07 
5025125  
Benefit  \(\) 11,701.4  \(\) 11,701.4  \(\)11,893.3  \(\)11,893.3  \(\) 11,701.4 
No. eval  5.19e+07  4.26e+07  5.52e+07  3.89e+07  5.90e+07 
5025150  
Benefit  \(\) 150,705.8  \(\)154,033.0  \(\)150,729.4  \(\)150,729.4  \(\) 150,705.8 
No. eval  3.73e+07  3.01e+07  3.99e+07  3.64e+07  3.60e+07 
5025175  
Benefit  \(\)26,017.8  \(\)26,259.9  \(\)26,017.8  \(\) 25,911.7  \(\) 25,911.7 
No. eval  6.07e+07  5.47e+07  5.40e+07  4.12e+07  7.47e+07 
5050125  
Benefit  \(\) 19,391.0  \(\)19,410.1  \(\)19,646.6  \(\)19,674.5  \(\) 19,391.0 
No. eval  1.18e+08  5.73e+07  1.10e+08  5.62e+07  8.86e+07 
5050150  
Benefit  \(\)123,524.3  \(\)122,964.5  \(\) 122,793.1  \(\) 122,793.1  \(\) 122,793.1 
No. eval  1.30e+08  5.26e+07  1.47e+08  6.65e+07  9.26e+07 
5050175  
Benefit  \(\) 253,204.8  \(\)254,506.7  \(\)253,247.2  \(\)253,247.2  \(\) 253,204.8 
No. eval  1.53e+08  5.03e+07  1.33e+08  6.39e+07  7.39e+07 
5075125  
Benefit  \(\)55,895.0  \(\)56,600.1  \(\)56,005.5  \(\)55,931.8  \(\) 55,836.8 
No. eval  1.92e+08  5.08e+07  1.91e+08  6.79e+07  1.04e+08 
5075150  
Benefit  \(\) 8,961.4  \(\) 8,961.4  \(\)9,441.9  \(\)9,403.1  \(\) 8,961.4 
No. eval  3.64e+08  9.42e+07  3.53e+08  9.55e+07  1.52e+08 
5075175  
Benefit  18,952.0  18,952.0  17,998.9  17,998.9  18,952.0 
No. eval  4.37e+08  4.01e+08  4.74e+08  1.25e+08  1.43e+08 
The benefits of the best solution and average number of fitness evaluations of the proposed algorithms from 10010125 to 100100175
Instance  \(k=1\)  \(k=3\)  MA  

SCC  DCC  SCC  DCC  
10010125  
Benefit  \(\)1,450.3  \(\)1,448.0  \(\)1,445.4  \(\)1,440.1  \(\) 1,437.2 
No. eval  1.65e+08  1.11e+08  1.94e+08  1.64e+08  1.40e+08 
10010150  
Benefit  \(\) 1,589.3  \(\)1,619.8  \(\)1,805.4  \(\)1,800.5  \(\) 1,589.3 
No. eval  2.10e+08  1.29e+08  2.34e+08  1.95e+08  1.69e+08 
10010175  
Benefit  \(\)9,423.4  \(\)9,663.8  \(\)9,485.5  \(\)9,460.3  \(\) 9,331.3 
No. eval  1.91e+08  1.39e+08  2.51e+08  2.16e+08  1.99e+08 
10025125  
Benefit  \(\)16,866.6  \(\)16,858.7  \(\)17,039.0  \(\)17,008.3  \(\) 16,817.0 
No. eval  2.10e+08  1.31e+08  2.19e+08  1.86e+08  1.92e+08 
10025150  
Benefit  \(\)11,710.2  \(\)11,624.1  \(\)11,910.4  \(\)11,878.1  \(\) 11,562.8 
No. eval  2.53e+08  1.50e+08  2.69e+08  2.09e+08  2.34e+08 
10025175  
Benefit  \(\)82,287.8  \(\)82,290.6  \(\)82,378.5  \(\)80,833.9  \(\) 80,596.2 
No. eval  2.41e+08  1.26e+08  2.51e+08  1.93e+08  2.35e+08 
10050125  
Benefit  \(\)87,315.8  \(\)87,931.1  \(\)87,674.8  \(\)87,933.0  \(\) 87,229.0 
No. eval  2.73e+08  1.37e+08  3.36e+08  2.03e+08  2.29e+08 
10050150  
Benefit  \(\)25,511.2  \(\)25,590.5  \(\)25,751.7  \(\)25,719.4  \(\) 25,504.9 
No. eval  4.30e+08  2.13e+08  4.26e+08  2.29e+08  3.90e+08 
10050175  
Benefit  \(\)44,720.3  \(\)45,194.4  \(\)44,990.5  \(\)45,700.9  \(\) 44,524.1 
No. eval  4.67e+08  2.25e+08  4.47e+08  2.31e+08  3.43e+08 
100100125  
Benefit  2,044.5  2,199.2  1,498.4  1,577.6  2,434.0 
No. eval  9.27e+08  6.11e+08  1.09e+09  3.82e+08  6.92e+08 
100100150  
Benefit  \(\)63,072.6  \(\)63,160.9  \(\)63,343.3  \(\)64,201.6  \(\) 61,957.9 
No. eval  1.25e+09  2.39e+08  1.25e+09  3.66e+08  6.66e+08 
100100175  
Benefit  \(\)134,104.9  \(\)135,781.0  \(\)134,622.6  \(\)135,440.8  \(\) 133,676.2 
No. eval  9.48e+08  2.76e+08  1.10e+09  4.17e+08  7.48e+08 
From the tables, one can see that the best performance of the algorithms is consistent with the average performance. MA performed the best. It managed to obtain the best solutions on all the benchmark instances. SCC with \(k=1\) comes next, obtaining the best solutions on 25 out of the 39 instances. DCC with \(k=1\) performed worse than the corresponding SCC, only achieving the best solutions on 15 instances. Both SCC and DCC with \(k=3\) obtained the best solutions on 13 instances. In terms of computational effort, one can see that the compared algorithms have comparable average number of fitness evaluations when the problem size is not large. This is consistent with the analysis in Eqs. (26) and (27) and indicates that the average number of local search steps \(L_1\), \(L_2\) and \(L_3\) are nearly the same for the small and mediumsized instances. For the larger instances (\(m, n \ge 50\)), SCCs require much more fitness evaluations than the other compared algorithms. Note that DCC generally needs less fitness evaluations than SCC, especially on the larger instances. This is because the dynamic change of the collaborators speeds up the convergence of the search process and thus reduces the number of steps (\(L_1\) and \(L_2\) in Eq. (26)) to reach the local optimum. Besides, given the same number of generations, the number of fitness evaluations increases significantly with the increase of \(n\) and \(m\), which is mainly induced by the increase of the neighbourhood sizes \(S_1=O(n^2)\), \(S_2=O(nm)+O(m^2)\) and \(S_3=O(n^2)+O(nm)+O(m^2)\).
Between the CC algorithms, one can see that DCC converges much faster, but generally obtained worse final results than the corresponding SCC. This implies that the combination of \(k=1\) and dynamic update of the collaborators leads to such a strong selection pressure that the search process become stuck in a local optimum at very early stage and can hardly jump out of it. In most of the instances, the CC algorithms with \(k=3\) converged slower than the ones with \(k=1\) at the earlier stage of the search. This is due to the much larger number of fitness evaluations (nearly \(k\) times) within each generation. However, their curves intersect the ones with \(k=1\) (e.g., Figs. 4, 7), and finally outperformed the CCs with \(k=1\).
In summary, the competitiveness of the proposed MA sheds a light on developing algorithms for complex realworld problems consisting of interdependent subproblems. First, by solving TTP as a whole, MA can be seen as considering the interdependence between the subproblems more comprehensively than CC. Second, the properly designed framework and employed operators leads to a comparable computational complexity with CC. In other words, MA explores the solution space more effectively than CC by choosing better “directions” during the search process. This is similar to the ideas of the numerical optimization methods that use the gradient information such as the steepest descent and QuasiNewton methods, and CMAES (Hansen 2006) in the evolutionary computation field. This implies that when tackling the interdependence between the subproblems, the major issues should be designing a proper measure that can reflect the gradient or dependence of the objective value on the change of decision variables in the complex combinatorial solution space, based on which one can find the best “direction” during the search process.
5 Conclusion
This paper investigates the interdependence between subproblems of a complex problem in the context of TTP, which is a simple but representative benchmark problem. The analysis is conducted both theoretically and empirically. At first, the mathematical formulations of TTP show that the nonlinear interdependence of the subproblems lying in the objective function makes it difficult to decompose the problem into independent subproblems, if not impossible. The NPhardness also makes the exact methods only applicable for smallsized instances. Then, a CC, which further consists of a standard and a dynamic version, and a MA is proposed to solve the problem approximately. The former optimizes the subproblems separately and exchanges the information in each generation, while the latter solves the problem as a whole. The outperformance of MA over CC on the benchmark instances illustrates the importance of considering the interdependence between subproblems. The significance of the research reported here may go beyond just TTP because there are other similar problems that are composed of two or more subproblems, each of which is an NPhard problem. For example, Gupta and Yao (2002) described a combined Vehicle Routing with Time Windows and Facility Location Allocation Problem, which is composed of Vehicle Routing with Time Windows and Facility Location Allocation. The research in this paper will help to understand and solve the above problem as well.
In the future, more sophisticated operators such as the 3opt and Lin–Kernighan (LK) heuristic Lin and Kernighan (1973) can be employed in an attempt to enhance the search capability of the algorithm. More importantly, measures that take the interdependence between the subproblems into account to reflect the dependence of the objective value on the change of the decision variables are to be designed so that frameworks can be developed more systematically by identifying the best “direction” during the optimization process rather than heuristically.
Footnotes
 1.
The benchmark instances can be downloaded from http://cs.adelaide.edu.au/~ec/research/ttp.php.
Notes
Acknowledgments
This work was supported by an ARC Discovery Grant (No. DP120102205) and an EPSRC Grant (No. EP/I010297/1). Xin Yao is supported by a Royal Society Wolfson Research Merit Award.
References
 Bolduc M, Laporte G, Renaud J, Boctor F (2010) A tabu search heuristic for the split delivery vehicle routing problem with production and demand calendars. Eur J Oper Res 202(1):122–130CrossRefzbMATHGoogle Scholar
 Bonyadi M, Moghaddam M (2009) A bipartite genetic algorithm for multiprocessor task scheduling. Int J Parallel Program 37(5):462–487CrossRefzbMATHGoogle Scholar
 Bonyadi M, Michalewicz Z, Barone L (2013) The travelling thief problem: the first step in the transition from theoretical problems to realistic problems. In: Proceedings of the 2013 IEEE congress on evolutionary computation, Cancun, Mexico, pp 1037–1044Google Scholar
 Boyd S, Xiao L, Mutapcic A, Mattingley J (2007) Notes on decomposition methods. Notes for EE364B, Stanford UniversityGoogle Scholar
 Bull L (2001) On coevolutionary genetic algorithms. Soft Comput 5(3):201–207CrossRefzbMATHGoogle Scholar
 Croes G (1958) A method for solving travelingsalesman problems. Oper Res 6(6):791–812MathSciNetCrossRefGoogle Scholar
 De Giovanni L, Pezzella F (2010) An improved genetic algorithm for the distributed and flexible jobshop scheduling problem. Eur J Oper Res 200(2):395–408CrossRefzbMATHGoogle Scholar
 Dorigo M, Gambardella L (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66CrossRefGoogle Scholar
 Dror M (2000) Arc routing: theory, solutions and applications. Kluwer Academic Publishers, BostonCrossRefGoogle Scholar
 Fidanova S (2007) Ant colony optimization and multiple knapsack problem. In: Handbook of research on nature inspired computing for economics and management, pp 498–509Google Scholar
 Fuellerer G, Doerner K, Hartl R, Iori M (2010) Metaheuristics for vehicle routing problems with threedimensional loading constraints. Eur J Oper Res 201(3):751–759CrossRefzbMATHGoogle Scholar
 Fu H, Mei Y, Tang K, Zhu Y (2010) Memetic algorithm with heuristic candidate list strategy for capacitated arc routing problem. In: Proceedings of the 2010 IEEE congress on evolutionary computation (CEC). IEEE, pp 1–8Google Scholar
 Gupta K, Yao X (2002) Evolutionary approach for vehicle routing problem with time windows and facility location allocation problem. In: Bullinaria JA (ed) Proceedings of the 2002 UK workshop on computational intelligence (UKCI’02). Birmingham, UKGoogle Scholar
 Hansen N (2006) The cma evolution strategy: a comparing review. In: Towards a new evolutionary computation. Springer, Berlin, pp 75–102Google Scholar
 Horowitz E, Sahni S (1974) Computing partitions with applications to the knapsack problem. J ACM (JACM) 21(2):277–292MathSciNetCrossRefzbMATHGoogle Scholar
 Ibrahimov M, Mohais A, Schellenberg S, Michalewicz Z (2012) Evolutionary approaches for supply chain optimisation: part i: single and twocomponent supply chains. Int J Intell Comput Cybern 5(4):444–472MathSciNetCrossRefGoogle Scholar
 Lin S, Kernighan B (1973) An effective heuristic algorithm for the travelingsalesman problem. Oper Res 21(2):498–516MathSciNetCrossRefzbMATHGoogle Scholar
 Mei Y, Tang K, Yao X (2009a) A global repair operator for capacitated Arc routing problem. IEEE Trans Syst Man Cybern Part B Cybern 39(3):723–734Google Scholar
 Mei Y, Tang K, Yao X (2009b) Improved memetic algorithm for capacitated arc routing problem. In: Proceedings of the 2009 IEEE congress on evolutionary computation, pp 1699–1706Google Scholar
 Mei Y, Tang K, Yao X (2011a) Decompositionbased memetic algorithm for multiobjective capacitated arc routing problem. IEEE Trans Evol Comput 15(2):151–165Google Scholar
 Mei Y, Tang K, Yao X (2011b) A memetic algorithm for periodic capacitated Arc routing problem. IEEE Trans Syst Man Cybern Part B Cybern 41(6):1654–1667Google Scholar
 Mei Y, Li X, Yao X (2014a) Cooperative coevolution with route distance grouping for largescale capacitated arc routing problems. IEEE Trans Evol Comput 18(3):435–449Google Scholar
 Mei Y, Li X, Yao X (2014b) Variable neighborhood decomposition for large scale capacitated arc routing problem. In: Proceedings of the 2014 IEEE congress on evolutionary computation (CEC2014). IEEE, pp 1313–1320Google Scholar
 Mei Y, Li X, Yao X (2013) Decomposing largescale capacitated arc routing problems using a random route grouping method. In: Proceedings of 2013 IEEE congress on evolutionary computation (CEC). IEEE, pp 1013–1020Google Scholar
 Mei Y, Tang K, Yao X (2010) Capacitated arc routing problem in uncertain environments. In: Proceedings of the 2010 IEEE congress on evolutionary computation, pp 1400–1407Google Scholar
 Melo M, Nickel S, SaldanhaDaGama F (2009) Facility location and supply chain management—a review. Eur J Oper Res 196(2):401–412MathSciNetCrossRefzbMATHGoogle Scholar
 Michalewicz Z (2012) Quo vadis, evolutionary computation? On a growing gap between theory and practice. In: Advances in computational intelligence. Lecture notes in computer science, vol. 7311. Springer, Berlin, pp 98–121Google Scholar
 Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech concurrent computation program, C3P Report 826Google Scholar
 Oliver I, Smith D, Holland J (1987) A study of permutation crossover operators on the traveling salesman problem. In: Grefenstette JJ (ed) Proceedings of the second international conference on genetic algorithms on genetic algorithms and their application. L. Erlbaum Associates Inc., pp 224–230Google Scholar
 Omidvar M, Li X, Mei Y, Yao X (2014) Cooperative coevolution with differential grouping for large scale optimization. IEEE Trans Evol Comput 18(3):378–393CrossRefGoogle Scholar
 Panait L, Luke S (2005) Timedependent collaboration schemes for cooperative coevolutionary algorithms. In: AAAI fall symposium on coevolutionary and coadaptive systemsGoogle Scholar
 Papadimitriou C (1977) The euclidean travelling salesman problem is npcomplete. Theor Comput Sci 4(3):237–244MathSciNetCrossRefzbMATHGoogle Scholar
 Potter M (1997) The design and analysis of a computational model of cooperative coevolution. Ph.D. thesis, George Mason UniversityGoogle Scholar
 Potter M, De Jong K (1994) A cooperative coevolutionary approach to function optimization. In: Parallel problem solving from nature (PPSN), pp 249–257Google Scholar
 Sbihi A (2010) A cooperative local searchbased algorithm for the multiplescenario max–min knapsack problem. Eur J Oper Res 202(2):339–346MathSciNetCrossRefzbMATHGoogle Scholar
 Stadtler H (2005) Supply chain management and advanced planning—basics, overview and challenges. Eur J Oper Res 163(3):575–588CrossRefzbMATHGoogle Scholar
 Stoen C (2006) Various collaborator selection pressures for cooperative coevolution for classification. In: International conference of artificial intelligence and digital communications, AIDCGoogle Scholar
 Tang K, Mei Y, Yao X (2009) Memetic algorithm with extended neighborhood search for capacitated Arc routing problems. IEEE Trans Evol Comput 13(5):1151–1166CrossRefGoogle Scholar
 Thomas D, Griffin P (1996) Coordinated supply chain management. Eur J Oper Res 94(1):1–15CrossRefzbMATHGoogle Scholar
 Wiegand R, Liles W, De Jong K (2001) An empirical analysis of collaboration methods in cooperative coevolutionary algorithms. In: Proceedings of the genetic and evolutionary computation conference (GECCO), pp 1235–1242Google Scholar
 Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1(6):80–83CrossRefGoogle Scholar