Approaching the biobjective critical node detection problem with a smart initializationbased evolutionary algorithm
 Published
 Accepted
 Received
 Academic Editor
 Lisu Yu
 Subject Areas
 Algorithms and Analysis of Algorithms, Artificial Intelligence, Optimization Theory and Computation
 Keywords
 Complex networks, Critical nodes, Biobjective critical node detection, Multiobjective algorithms, Memetic algorithms
 Copyright
 © 2021 Béczi and Gaskó
 Licence
 This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
 Cite this article
 2021. Approaching the biobjective critical node detection problem with a smart initializationbased evolutionary algorithm. PeerJ Computer Science 7:e750 https://doi.org/10.7717/peerjcs.750
Abstract
Determining the critical nodes in a complex network is an essential computation problem. Several variants of this problem have emerged due to its wide applicability in network analysis. In this article we study the biobjective critical node detection problem (BOCNDP), which is a new variant of the wellknown critical node detection problem, optimizing two objectives at the same time: maximizing the number of connected components and minimizing the variance of their cardinalities. Evolutionary multiobjective algorithms (EMOA) are a straightforward choice to solve this type of problem. We propose three different smart initialization strategies which can be incorporated into any EMOA. These initialization strategies take into account the basic properties of the networks. They are based on the highest degree, random walk (RW) and depthfirst search. Numerical experiments were conducted on synthetic and realworld network data. The three different initialization types significantly improve the performance of the EMOA.
Introduction
In recent years, complex networks have received a lot of attention due to their applicability in various domains. Several optimization problems were studied within complex networks like community detection (Fortunato, 2010), maximal influence node detection (Kempe, Kleinberg & Tardos, 2003) and link prediction (LibenNowell & Kleinberg, 2007). All of the aforementioned problems reveal major insights into the networks studied.
Identifying critical nodes (the critical node detection problem, or CNDP) in a complex network is a crucial task. The base problem consists of minimizing pairwise connectivity by removing a subset of K nodes in a given graph. In Arulselvan et al. (2009) it was proven to be an NPhard problem.
The general formulation of the problem is Lalou, Tahraoui & Kheddouci (2018): given a G = (V, E) graph and a connectivity metric λ, find the set of nodes S⊆V such that G[V∖S] satisfies the metric λ. This metric is usually defined as an objective function that needs to be optimized (for example, maximize the number of components, minimize the component size, etc.).
CNDP has a wide field of applicability, for example in social network analysis (Fan & Pardalos, 2010), epidemic control (Tao, Zhongqian & Binghong, 2006), network immunization (Kuhlman et al., 2010) and biological networks (Liu et al., 2020). Several algorithms were designed for the CNDP. The majority of the exact methods are based on the integer linear programming formulation of the problem (Summa, Grosso & Locatelli, 2012). In Addis, Summa & Grosso (2013), a dynamic programming approach is proposed for a special class of graphs. As approximation algorithms, we can mention, for example, a simulated annealing algorithm (Ventresca, 2012). A thorough survey of existing methods for the CNDP can be found in Lalou, Tahraoui & Kheddouci (2018).
The CNDP has several variants exploring the connectivity metric λ. Other variants with constraints were introduced, such as the cardinality constrained critical node detection problem (CCCNDP) (Arulselvan et al., 2011) and the componentcardinalityconstrained critical node problem(3CCNDP) (Lalou, Tahraoui & Kheddouci, 2016). One of the existing biobjective variants of the CNDP is proposed in Li et al. (2019). In this variant the cost of removing the node counts. Another biobjective variant proposed in Ventresca, Harrison & OmbukiBerman (2018) is the base of our study (described in ‘The Biobjective Critical Node Detection Problem’).
Evolutionary algorithms are powerful tools in optimization problems. Multiobjective optimization problems involve multiple objective functions which need to be optimized at the same time, so they can be used in realworld optimization problems. Because the BOCNDP is an NPhard problem (Ventresca, Harrison & OmbukiBerman, 2018), the use of the evolutionary algorithms is straightforward. To increase the performance of evolutionary algorithms, several techniques were designed, for example, hybridization, a special case of memetic algorithms which incorporates a local search in the initialization phase. Kazimipour, Li & Qin (2014) emphasizes the importance of population initialization techniques, introduces a new categorization, and mentions some concrete examples.
Due to the wide applicability of the critical node detection problem, this article introduces new smart initialization strategies that can be incorporated into any multiobjective optimization algorithm that treats the BOCNDP to increase its performance. These strategies can be used in other variants of the CNDP, or even for other computationally graphed theoretical problems because they take into account structural information about the network.
To summarise, the main goal of this article is as follows:

a smart initialization which is based on a depthfirst search: nodes lying on a path are chosen to be in the initial population;

a random walkbased smart initialization strategy: a random walk is simulated on the graph, and nodes that appear more times in the walk are considered more important;

a degreebased smart initialization strategy: nodes with a higher degree are more likely to be chosen in the initial population;

statistical analysis of the three smart initialization strategies introduced here and their comparison with random initialization.
The rest of the article is organized as follows: In the second section, we describe the biobjective critical node detection problem and the existing solving algorithms. In the third section, we present the proposed initialization algorithms. The next section describes the numerical experiments. The article ends with conclusions and further work.
The biobjective critical node detection problem
Let G = (V, E) be an undirected graph, where V is the set of nodes, and E is the set of edges.
Let G = (V, E) be an undirected graph, where V is the set of nodes, and E is the set of edges. The biobjective critical node detection problem was proposed in Ventresca, Harrison & OmbukiBerman (2018) and consists of finding a fixed number of k nodes, which, if deleted from graph G, will optimize the following two objectives:

maximize the number of connected components;

minimize the variance of the cardinality of the connected components.
Formally the objectives are the following: (1)$max\phantom{\rule{10.00002pt}{0ex}}\leftH\right,$ (2)$min\phantom{\rule{10.00002pt}{0ex}}var\left(H\right),$ (3)$\text{such that}\sum _{i\in S}{w}_{i}\le W,$
where w_{i} are the weights associated to the vertices of the graph and W > 0 is a constraint, H denotes $G\left[V\setminus S\right]$ the set of the connected components and var(H) denotes the variance of the cardinality of the connected components and can be calculated in the following way: (4)$\frac{1}{\leftH\right}\sum _{h\in H}{\left(\lefth\right\frac{{n}^{\ast}}{\leftH\right}\right)}^{2},$ where n^{∗} = ∑_{h∈H}h is the number of nodes in $G\left[V\setminus S\right]$. The BOCNDP is different from the CNDP (Ventresca, Harrison & OmbukiBerman, 2018).
Let us consider a simple example, the graph presented in Fig. 1. If we need to identify k = 2 critical nodes, then $S=\left\{2,3\right\}$ is the optimal solution. The $G\left[V\setminus S\right]$ will have 5 components, H = 5 and $var\left(H\right)=\frac{1}{5}\cdot \left[{\left(1\frac{13}{5}\right)}^{2}+4\cdot {\left(3\frac{13}{5}\right)}^{2}\right]=\frac{16}{25}=0.64.$
Because the BOCNDP is a relatively new problem formulation, the literature is not rich in proposed algorithms. In Ventresca, Harrison & OmbukiBerman (2018), six existing multiobjective algorithms are used to solve the BOCNDP. In Li et al. (2019), a different variant of the BOCNDP, called BiCNDP, is introduced and studied with decompositionbased multiobjective evolutionary algorithms.
Evolutionary computation method
Evolutionary algorithms are powerful optimization tools, especially in multiobjective optimization problems. To increase the performance of these algorithms, hybrid versions are designed and analysed. Smart initialization of the population of an evolutionary algorithm can increase the performance of the algorithm significantly (Maaranen, Miettinen & Penttinen, 2007).
We present three strategies that can be used in the initialization phase of any multiobjective algorithm. The first one is based on a depth search algorithm, outlined in the algorithm 1. A depthfirst search (DFS) algorithm is started with a random initial node, and every xth element will be added to the chromosome, where $x=\frac{\leftV\right}{k}$, V is the number of nodes and k is the number of critical nodes.
The second initialization method is based on the degree distribution of the nodes. The first x nodes with the highest degree are set in the chromosome, and the rest of the k − x nodes are selected randomly, to preserve the stochastic nature of the initialization (Algorithm 2).
The third method is based on a random walk. We start the walk from a random node, t is the length of the walk and p_{r} is the probability to restart the walk. In each step, the decision is to continue the walk or to restart. If we fail to walk through k different nodes, the algorithm will restart from another initial point. In the walk, we keep counting how many times a node appears. The more times it appears, the higher the probability it is a gene in the chromosome. The main steps are presented in the algorithm 3.
These initialization strategies can be used in any kind of multiobjective evolutionary algorithm. The outline of the smart initializationbased algorithm is depicted in the algorithm 4.
_______________________
Algorithm 1 Depthfirst search solution generator (DFS)___________________________
Require: G,k,x
1: start ← SELECT(V )
2: S ← DFS(G,start)
3: return S [::x] ⊳ Take every xth element
_______________________________________________________________________________________________________
Algorithm 2 Degree solution generator (Deg)__________________________________________
Require: G,k,x
1: V ′ ← SORTED(V ) ⊳ Sort nodes according to their degree in DESC order
2: S ← V ′ [:x] ⊳ Take first x nodes with the highest degree
3: while S < k do
4: node ← SELECT(V ′)
5: if node / ∈ S then
6: S ← S ∪{node}
7: end if
8: end while
9: SHUFFLE(S)
10: return S_________________________________________________________________________
________________________________________________________________________________________
Algorithm 3 Random walk solution generator (RW)_________________________________
Require: G,k,t,pr
1: visited ←∅
2: while True do
3: core ← SELECT(V )
4: current ← core
5: for i ← 1,t + 1 do
6: if current ∈ visited then
7: visited[current] ← visited[current] + 1
8: else
9: visited[current] ← 1
10: end if
11: restart ← RAND INT(1,100)
12: if restart ≤ pr then
13: current ← core
14: else
15: neighbors ← NEIGHBORS(G,current)⊳ Neighbors of the current
node
16: current ← SELECT(neighbors)
17: end if
18: end for
19: if visited≥ k then
20: break
21: else
22: visited ←∅
23: end if
24: end while
25: SORT(visited) ⊳ Sort nodes in visited according to visits paid in DESC
order
26: return visited[:k] ⊳ Take the first k most visited nodes
_______________________________________________________________________________________________________
Algorithm 4 Evolutionary algorithm with smart initialization_____________________
Require: G,k
1: initialize1 population Ps;
2: run a multiobjective Pareto based optimization algorithm2, where
Pinitial = Ps;
3: return Pareto front________________________________________________________________________
1for the initialization we use: random initialization, depthfirst search, degree,
random walk;
2e.g., NSGAII, SPEA__________________________________________________________________________
Numerical experiments
Benchmarks
Synthetic data
We use the synthetic graph set proposed in Ventresca (2012). The benchmark set contains four different types of graphs: BarabásiAlbert (BA), ErdsRényi (ER), Forestfire (FF) and Watts–Strogatz (WS). BarabásiAlbert graphs are scalefree networks, using a preferential attachment mechanism and some high degree nodes (hubs). ErdsRényi graphs are random networks in which each link between nodes is generated randomly based on a probability. Forestfire graphs are random graphs with a preferential attachment mechanism. Watts–Strogatz graphs are random graphs with short average path lengths, so they have a dense structure.
Table 1 presents some basic properties of the benchmarks used: number of nodes (V), number of edges (E), the number of critical nodes (k), average degree (〈d〉), density of the graph (ρ), and average path length (l_{G}).
Graph  V  E  k  〈d〉  ρ  l_{G} 

BA500  500  499  50  1.996  0.004  5.663 
BA1000  1000  999  75  1.998  0.002  6.045 
BA2500  2500  2499  100  1.999  0.001  6.901 
BA5000  5000  4999  150  2.000  0.000  8.380 
ER250  235  350  50  2.979  0.013  5.338 
ER500  466  700  80  3.004  0.006  5.973 
ER1000  941  1400  140  2.976  0.003  6.558 
ER2500  2344  3500  200  2.986  0.001  7.516 
FF250  250  514  50  4.112  0.017  4.816 
FF500  500  828  110  3.312  0.007  6.026 
FF1000  1000  1817  150  3.634  0.004  6.173 
FF2000  2000  3413  200  3.413  0.002  7.587 
WS250  250  1246  70  9.968  0.040  3.327 
WS500  500  1496  125  5.984  0.012  5.304 
WS1000  1000  4996  200  9.992  0.010  4.444 
WS1500  1500  4498  265  5.997  0.004  7.554 
Real dataset Nine real datasets are used for the numeric experiments. The real datasets come from different areas: transportation networks (USAir97, TrainsRome, EUFlights), biological networks (Bovine, EColi, HumanDis), social networks (Oclinks, Facebook), and an electric network (Circuit). The size of the graphs varies from 121 to 4039 nodes. The density of the networks varies from 0.008 to 0.044 and the average path length is from 2.622 to 43.496. The basic properties of the networks are outlined in Table 2.
Graph  V  E  k  〈d〉  ρ  l_{G}  Ref. 

Bovine  121  190  12  3.140  0.026  2.861  Reimand et al. (2008) 
Circuit  252  399  25  3.167  0.012  5.806  Milo et al. (2004) 
EColi  328  456  15  2.780  0.008  4.834  Yang, Huang & Lai (2008) 
USAir97  332  2126  33  12.807  0.038  2.738  Rossi & Ahmed (2015) 
HumanDis  516  1188  52  4.605  0.008  6.509  Goh et al. (2007) 
TrainsRome  255  272  26  2.133  0.008  43.496  Cacchiani, Caprara & Toth (2010) 
EUFlights  1191  31610  119  53.081  0.044  2.622  Opsahl (2011) 
Oclinks  1839  13838  190  14.574  0.008  3.055  Opsahl & Panzarasa (2009) 
4039  88234  404  43.691  0.010  3.693  Leskovec & Krevl (2014) 
Statistical analysis of the smart initialization strategies
To analyse the behaviour of the initialization strategies introduced, we generated 100 independent solutions and calculated the values of H and var(H). A statistical test was conducted to mark the differences between the methods. Table 3 presents the results. In almost all cases, the degreebased initialization outperformed the other strategies. Almost in all cases the degree based initialization outperformed the other strategies, but all of them outperformed the random initialization.
Graph  Random  DFS  Deg  RW  

H  var(H)  H  var(H)  H  var(H)  H  var(H)  
BA500  49.36 ± 21.74  3097.82 ± 2112.76  41.15 ± 11.15  3695.26 ± 1462.78  244.28 ± 18.65^{*}  3.05 ± 1.36^{*}  103.81 ± 32.89  441.45 ± 2365.44 
BA1000  73.31 ± 36.73  10002.81 ± 5993.79  60.37 ± 24.97  12836.08 ± 5227.38  474.91 ± 29.23^{*}  4.21 ± 1.51^{*}  176.79 ± 61.87  1652.69 ± 4710.80 
BA2500  98.03 ± 39.55  54123.97 ± 24512.87  89.26 ± 32.17  60285.58 ± 24166.17  860.18 ± 63.00^{*}  18.64 ± 6.30^{*}  252.58 ± 97.64  9704.90 ± 18968.13 
BA5000  139.40 ± 34.40  148039.52 ± 47033.66  142.23 ± 42.43  152707.13 ± 54715.66  1533.22 ± 104.56^{*}  26.19 ± 6.59^{*}  337.75 ± 138.00  30651.02 ± 37103.39 
ER250  14.38 ± 3.15  1827.15 ± 497.41  19.33 ± 3.90  1154.09 ± 1274.76  26.00 ± 4.17^{*}  765.19 ± 220.02^{*}  12.81 ± 2.55  2090.96 ± 485.71 
ER500  21.02 ± 3.67  5936.26 ± 1206.28  24.69 ± 4.62  4796.56 ± 3592.21  36.89 ± 5.12^{*}  3007.85 ± 584.29^{*}  18.31 ± 3.49  6946.33 ± 1509.55 
ER1000  42.20 ± 5.01  12845.73 ± 1675.54  46.76 ± 7.35  11765.73 ± 7683.25  77.09 ± 8.15^{*}  5947.44 ± 927.67^{*}  38.99 ± 4.92  13974.21 ± 2125.06 
ER2500  55.97 ± 7.00  75946.14 ± 10701.36  56.94 ± 5.94  73241.04 ± 7760.67  105.14 ± 10.41^{*}  38069.15 ± 4333.11^{*}  50.61 ± 6.81  84401.80 ± 12291.82 
FF250  20.12 ± 4.54  1360.18 ± 518.16  21.27 ± 3.95  1164.85 ± 315.27  51.89 ± 7.85^{*}  36.68 ± 41.49^{*}  38.25 ± 9.23  221.69 ± 248.40 
FF500  53.97 ± 7.37  1449.86 ± 401.19  66.10 ± 7.41  895.44 ± 214.06  130.62 ± 12.81^{*}  9.31 ± 3.27^{*}  77.74 ± 12.84  164.61 ± 180.47 
FF1000  68.47 ± 9.04  7344.12 ± 1498.93  79.71 ± 8.85  5491.09 ± 966.88  173.03 ± 15.29^{*}  49.80 ± 20.63^{*}  110.50 ± 19.33  1402.81 ± 1121.88 
FF2000  97.52 ± 10.87  24888.22 ± 3761.80  99.17 ± 10.83  24149.41 ± 3287.01  270.92 ± 31.20^{*}  121.23 ± 67.59^{*}  135.65 ± 31.73  6694.04 ± 8302.33 
WS250  1.00 ± 0.00  0.00 ± 0.00^{*}  1.00 ± 0.00  0.00 ± 0.00^{*}  1.00 ± 0.00  0.00 ± 0.00^{*}  1.09 ± 0.29^{*}  711.12 ± 2272.67 
WS500  1.21 ± 0.50  5633.93 ± 12549.71  1.01 ± 0.10  318.62 ± 3186.22^{*}  2.08 ± 1.00^{*}  20254.58 ± 14955.09  1.78 ± 0.98  15701.24 ± 16313.11 
WS1000  1.00 ± 0.00  0.00 ± 0.00^{*}  1.00 ± 0.00  0.00 ± 0.00^{*}  1.01 ± 0.10  1592.01 ± 15920.10  1.35 ± 0.61^{*}  44980.28 ± 70915.49 
WS1500  1.18 ± 0.41  63783.38 ± 141708.20  1.11 ± 0.31  38209.49 ± 109239.42  2.47 ± 1.12  263515.70 ± 136371.28^{*}  3.54 ± 1.48^{*}  276008.24 ± 98915.66 
Bovine  6.58 ± 10.11  1218.83 ± 1179.19  2.63 ± 4.59  1171.03 ± 1351.67  94.91 ± 5.24^{*}  1.02 ± 0.94^{*}  87.56 ± 11.08  4.82 ± 8.35 
Circuit  4.03 ± 1.66  9182.68 ± 2622.23  3.69 ± 1.56  9007.61 ± 3059.59  7.72 ± 2.50^{*}  4853.82 ± 2099.33^{*}  4.50 ± 1.82  8127.30 ± 2820.42 
EColi  9.24 ± 8.12  12020.10 ± 7415.24  11.01 ± 8.97  8373.55 ± 6239.28  103.58 ± 16.47^{*}  189.90 ± 118.58^{*}  45.93 ± 22.61  2568.58 ± 3649.19 
USAir97  7.53 ± 4.84  11035.84 ± 5756.78  7.55 ± 5.48  12066.09 ± 6284.24  54.00 ± 5.28  1038.70 ± 160.83  62.99 ± 7.90^{*}  794.91 ± 451.27^{*} 
HumanDis  18.53 ± 5.79  9095.70 ± 4116.18  20.01 ± 5.46  7639.00 ± 3162.30  76.58 ± 8.19^{*}  160.28 ± 80.79^{*}  30.62 ± 10.36  4623.64 ± 3005.66 
TrainsRome  17.85 ± 1.79  273.49 ± 140.99  21.05 ± 0.80^{*}  64.73 ± 37.06^{*}  21.32 ± 1.86^{*}  184.62 ± 55.62  1.87 ± 0.82  5223.65 ± 5085.48 
EUFlights  18.89 ± 9.15  66944.36 ± 28610.83  16.98 ± 7.30  73118.63 ± 34156.37  25.84 ± 5.72  42239.73 ± 8314.82  44.30 ± 15.39^{*}  25945.04 ± 8952.33^{*} 
Oclinks  41.93 ± 12.59  70563.01 ± 22486.35  45.30 ± 10.31  61325.98 ± 15792.50  294.12 ± 23.86  6728.72 ± 789.91  349.02 ± 19.11^{*}  5189.44 ± 466.75^{*} 
10.76 ± 9.60  929504.70 ± 865354.95  15.64 ± 11.70  642310.12 ± 473911.43  75.16 ± 6.14^{*}  164435.23 ± 15504.56^{*}  40.67 ± 18.65  382094.53 ± 230206.80 
Notes:
Algorithm
For the numerical experiments, we used the NSGAII (Deb et al., 2002) algorithm within the Platypus (https://github.com/quaquel/Platypus, last accessed 3/12/2019) framework. NSGAII is a multiobjective evolutionary algorithm in which every member of the population is sorted according to the level of nondomination. To maintain diversity, a crowding distance is applied.
Parameter setting
For the numerical experiments, parameters of the NSGAII algorithm are the default values of the Platypus framework, with a total evaluation number of 10000. All the weights of the nodes are set to 1, and W equals the number of nodes. Parameters of the smart initialization strategies are as follows: for the DFS, x is the number of nodes divided by the value of k; for the random walk, the number of steps is 10000 and the probability of restart is 0.2; and for the Deg algorithm, $x=\frac{k}{3}$.
Performance evaluation
For the performance evaluation, we use the hypervolume indicator (Zitzler & Thiele, 1998; Zitzler & Thiele, 1999), a popular measure for multiobjective optimization algorithms. The hypervolume indicator measures the volume of the region of the dominated points in the objective space bounded by a reference point.
Results and discussion
In the case of synthetic benchmarks, we conducted ten independent runs for each initialization strategy (depthfirst search, degreebased, random walk) and made comparisons with random initialization. Table 4 presents the mean values and the standard deviation of the hypervolume indicators. For a reference point, we set the nadir point of all unified Pareto fronts. We conducted a Wilcoxon sign rank nonparametric test for the hypervolume indicator reported by each method. The Wilcoxon sign rank assesses if there is a significant difference between the two sample means. An (*) is used to indicate the statistical significance of differences. All initialization strategies which are not statistically different from the best one are marked. Figure 2 presents the Pareto front obtained within a single run.
Table 5 describes the mean value and standard deviation obtained for the real datasets. Best results are marked with an (*).
Based on the results, we can draw a general conclusion for the synthetic benchmarks about the best initialization strategy. The structure of the graph determines which initialization is worth using, but based on the numerical experiments, all of them give better results than the random initialization. In the case of BarabásiAlbert graphs, which contain hubs, the degreebased initialization gets the best result. ErdsRényi graphs are random graphs, in which case the depthfirst search algorithm seems to be best. For Forestfire graphs, which are random graphs, the three proposed initialization types gave almost the same result. The Watts–Strogatz graphs have a dense structure, and the best results were provided by the random walkbased algorithm.
Graph  Random  DFS  Deg  RW 

BA500  15.65 ± 4.37  13.03 ± 5.37  28.05 ± 2.62^{*}  20.72 ± 4.37 
BA1000  36.54 ± 21.58  58.38 ± 24.17  154.27 ± 10.54^{*}  68.21 ± 26.51 
BA2500  6138.01 ± 1701.47  2924.91 ± 2828.77  18607.82 ± 364.64^{*}  8643.83 ± 1993.09 
BA5000  1078479.10 ± 319614.94  449069.24 ± 396267.77  3409301.92 ± 47950.42^{*}  1706543.15 ± 182869.56 
ER250  738.68 ± 217.41  2387.05 ± 342.45^{*}  1162.31 ± 252.49  1204.39 ± 319.17 
ER500  1607.75 ± 920.84  8045.09 ± 1848.71^{*}  2762.01 ± 1572.09  2644.67 ± 919.06 
ER1000  1133.40 ± 949.75  9407.93 ± 4141.55^{*}  6385.57 ± 3487.82^{*}  5176.12 ± 1499.51 
ER2000  39323.23 ± 24560.07  65449.57 ± 34152.98^{*}  56123.63 ± 12683.44^{*}  72259.64 ± 26198.93^{*} 
FF250  38.01 ± 6.84  36.34 ± 12.26^{*}  41.88 ± 5.39^{*}  45.61 ± 7.52^{*} 
FF500  24.51 ± 8.19  51.42 ± 9.68^{*}  37.72 ± 4.93  32.98 ± 6.86 
FF1000  1402.85 ± 599.70  1718.09 ± 647.33  3274.96 ± 387.73^{*}  3194.72 ± 409.27^{*} 
FF2000  30560.10 ± 20460.26  16206.67 ± 13027.87  136548.86 ± 12378.66^{*}  107945.88 ± 12371.82 
WS250  8132.90 ± 268.63  9577.11 ± 1390.61  8495.50 ± 636.66  16193.42 ± 3306.69^{*} 
WS500  48526.56 ± 14388.26  35879.19 ± 8263.24  110772.29 ± 31047.79  203301.59 ± 30420.58^{*} 
WS1000  159361.50 ± 503.68  159202.10 ± 0.32  162811.19 ± 11412.20  337726.99 ± 90573.11^{*} 
WS1500  484347.05 ± 140137.66  708776.63 ± 223377.92  1923479.18 ± 351573.85  5058526.43 ± 1741808.94^{*} 
Notes:
Graph  Random  DFS  Deg  RW 

Bovine  275259.54 ± 57721.31  263669.06 ± 62622.52  4524666.52 ± 3.17^{*}  3148983.90 ± 3.24 
Circuit  77, 116.15 ± 29, 312.55  57695.87 ± 31444.12  1419610.33 ± 95327.38^{*}  1086345.36 ± 77486.85 
EColi  498186.23 ± 84010.17  368140.36 ± 172105.46  9742206.66 ± 165771.44^{*}  6820902.13 ± 71640.00 
USAir  193223.67 ± 115193.40  361106.95 ± 137617.76  5895064.90 ± 123050.07^{*}  4010784.11 ± 95995.05 
HumanDis  667579.05 ± 18327.70  762880.99 ± 14569.84  7944126.85 ± 156209.02^{*}  5659398.93 ± 72630.76 
TrainsRome  138525.07 ± 5559.37  154874.11 ± 4437.33  1727489.76 ± 41083.54^{*}  1017365.41 ± 59551.06 
EUFlights  59657.61 ± 34396.03  111198.59 ± 28682.44  9274581.78 ± 279065.65^{*}  6373823.26 ± 143830.93 
OClinks  291207.54 ± 92826.46  313517.98 ± 163921.22  26002131.74 ± 584868.91^{*}  18601730.00 ± 296910.80 
4920.00 ± 0.00  5273.00 ± 0.00  1031865.98 ± 1185922.59  1409016.39 ± 817382.53^{*} 
Notes:
In the case of the real networks, all three proposed initialization strategies outperformed the random initialization. In most cases, the degreebased initialization seemed to give the best results.
Conclusions and further work
In this paper, we propose three smart population initialization methods for the BOCNDP problem. Numerical experiments show the effectiveness of the proposed approaches. All three methods outperformed the traditional random initialization.
As further work, other initialization strategies will be investigated and an adaptive algorithm can be developed to find the best initialization, taking into account the basic properties of the graph.