American Institute of Mathematical Sciences

Advanced
June  2021, 11(2): 321-332. doi: 10.3934/naco.2020028

Design of experiment for tuning parameters of an ant colony optimization method for the constrained shortest Hamiltonian path problem in the grid networks

Mohsen Abdolhosseinzadeh 1,, and  Mir Mohammad Alipour 2,

1. 

Department of Mathematics, University of Bonab, Bonab, Iran
2. 

Department of Computer Engineering, University of Bonab, Bonab, Iran

* Corresponding author: Mohsen Abdolhosseinzadeh

The authors would like to thank anonymous reviewers for their useful comments

Received  September 2019 Revised  January 2020 Published  June 2021 Early access  May 2020

In a grid network, the nodes could be traversed either horizontally or vertically. The constrained shortest Hamiltonian path goes over the nodes between a source node and a destination node, and it is constrained to traverse some nodes at least once while others could be traversed several times. There are various applications of the problem, especially in routing problems. It is an NP-complete problem, and the well-known Bellman-Held-Karp algorithm could solve the shortest Hamiltonian circuit problem within $ {\rm O(}{{\rm 2}}^{{\rm n}}{{\rm n}}^{{\rm 2}}{\rm )} $ time complexity; however, the shortest Hamiltonian path problem is more complicated. So, a metaheuristic algorithm based on ant colony optimization is applied to obtain the optimal solution. The proposed method applies the rooted shortest path tree structure since in the optimal solution the paths between the restricted nodes are the shortest paths. Then, the shortest path tree is obtained by at most $ {\rm O(}{{\rm n}}^{{\rm 3}}{\rm )} $ time complexity at any iteration and the ants begin to improve the solution and the optimal solution is constructed in a reasonable time. The algorithm is verified by some numerical examples and the ant colony parameters are tuned by design of experiment method, and the optimal setting for different size of networks are determined.

Keywords: Shortest Hamiltonian path, constrained shortest path, ant colony optimization, grid networks, design of experiments, response surface method.
Mathematics Subject Classification: Primary: 62K20, 58F17; Secondary: 53C35.
Citation: Mohsen Abdolhosseinzadeh, Mir Mohammad Alipour. Design of experiment for tuning parameters of an ant colony optimization method for the constrained shortest Hamiltonian path problem in the grid networks. Numerical Algebra, Control and Optimization, 2021, 11 (2) : 321-332. doi: 10.3934/naco.2020028
References:
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R. K. Ahuja, T. L. Magnanti and J. B. Orlin, Network Flows: Theory, Algorithms, and Applications, 1$^st$ edition, Prentice hall, New York, 1993.

[2]

M. M. Alipour and S. N. Razavi, A new multiagent reinforcement learning algorithm to solve the symmetric traveling salesman problem, Multiagent Grid Syst., 11 (2015), 107-119. 

[3]

M. M. Alipour, S. N. Razavi, M. R. Feizi Derakhshi and M. A. Balafar, A hybrid algorithm using a genetic algorithm and multiagent reinforcement learning heuristic to solve the traveling salesman problem, Neural Comput. Appl., 30 (2018), 2935-2951. 

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B. Appleton and C. Sun, Circular shortest paths by branch and bound, Pattern Recognit., 36 (2003), 2513-2520. 

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A. A. Bertossi, The edge Hamiltonian path problem is NP-complete, Inf. Process. Lett., 13 (1981), 157-159.  doi: 10.1016/0020-0190(81)90048-X.

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B. Bontoux, C. Artigues and D. Feillet, A memetic algorithm with a large neighborhood crossover operator for the generalized traveling salesman problem, Comput. Oper. Res., 37 (2010), 1844-1852.  doi: 10.1016/j.cor.2009.05.004.

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G. A. Bula, C. Prodhon, F. A. Gonzalez, H. M. Afsar and N. Velasco, Variable neighborhood search to solve the vehicle routing problem for hazardous materials transportation, J. Hazard. Mater., 324 (2017), 472-480. 

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E. Cao, M. Lai and H. Yang, Open vehicle routing problem with demand uncertainty and its robust strategies, Expert Syst. Appl., 41 (2014), 3569-3575. 

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T. S. Chang, Y. W. Wan and W. T. Ooi, A stochastic dynamic traveling salesman problem with hard time windows, Eur. J. Oper. Res., 198 (2009), 748-759.  doi: 10.1016/j.ejor.2008.10.012.

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S. S. Choong, L. P. Wong and C. P. Lim, An artificial bee colony algorithm with a modified choice function for the traveling salesman problem, Swarm Evol. Comput., 44 (2019), 622-635. 

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A. Colorni, M. Dorigo, V. Maniezzo, D. Elettronica and P. Milano, Distributed optimization by ant colonies, The 1991 European Conference on Artificial Life, (1991), 134–142.

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D. Ferone, P. Festa, F. Guerriero and D. Laganá, The constrained shortest path tour problem, Comput. Oper. Res., 74 (2016), 64-77.  doi: 10.1016/j.cor.2016.04.002.

[13]

D. Ferone, P. Festa, F. Guerriero and D. Laganá, An integer linear programming model for the constrained shortest path tour problem, Electron. Notes Discret. Math., 69 (2018), 141-148.  doi: 10.1016/j.endm.2018.07.019.

[14]

A. Gunawan, H. C. Lau and Li ndawati, Fine-tuning algorithm parameters using the design of experiments approach, Lect. Notes Comput. Sci., 6683 (2011), 278-292. 

[15]

M. Held and R. M. Karp, A dynamic programming approach to sequencing problems, J. Soc. Ind. Appl. Math., 10 (1962), 196-210. 

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J. Jana and S. Kumar Roy, Solution of matrix games with generalised trapezoidal fuzzy payoffs, Fuzzy Inf. Eng., 10 (2018), 213-224. 

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J. Jana and S. K. Roy, Dual hesitant fuzzy matrix games: based on new similarity measure, Soft Comput., 23 (2019), 8873-8886. 

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M. Kuby, O. M. Araz, M. Palmer and I. Capar, An efficient online mapping tool for finding the shortest feasible path for alternative-fuel vehicles, Int. J. Hydrogen Energy, 39 (2014), 18433-18439. 

[19]

S. Kumar Roy, M. Pervin and G. Wilhelm Weber, Imperfection with inspection policy and variable demand under trade-credit: a deteriorating inventory model, Numer. Algebr. Control Optim., 10 (2020), 45-74. 

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T. H. Lai and S. S. Wei, The edge Hamiltonian path problem is NP-complete for bipartite graphs, Inf. Process. Lett., 46 (1993), 21-26.  doi: 10.1016/0020-0190(93)90191-B.

[21]

C. P. Lam, J. Xiao and H. Li, Ant colony optimisation for generation of conformance testing sequences using characterising sequences, The 3rd IASTED International Conference on Advances in Computer Science and Technology (ACS2007), (2007), 140–146.

[22]

E. B. De Lima, G. L. Pappa, J. M. De Almeida, M. A. Goncalves and W. Meira, Tuning genetic programming parameters with factorial designs, IEEE World Congr. Comput. Intell., IEEE Congr. Evol. Comput. 2010.

[23]

Y. H. Liu, Different initial solution generators in genetic algorithms for solving the probabilistic traveling salesman problem, Appl. Math. Comput., 216 (2010), 125-137.  doi: 10.1016/j.amc.2010.01.021.

[24]

S. de Mesquita, A. R. Backes and P. Cortez, Texture analysis and classification using shortest paths in graphs, Pattern Recognit. Lett., 34 (2013), 1314-1319.  doi: 10.1109/TIP.2014.2333655.

[25]

M. Mobin, S. M. Mousavi, M. Komaki and M. Tavana, A hybrid desirability function approach for tuning parameters in evolutionary optimization algorithms, Meas. J. Int. Meas. Confed., 114 (2018), 417-427. 

[26]

D. C. Montgomery, Design And Analysis of Experiments, 5$^th$ edition, Wiley, New York, 1984.

[27]

C. M. Papadimitriou, Computational Complexity, 1$^st$ edition, Addison-Wesley, New York, 1994.

[28]

M. Pervin, S. K. Roy and G. W. Weber, A two-echelon inventory model with stock-dependent demand and variable holding cost for deteriorating items, Numer. Algebr. Control Optim., 7 (2017), 21-50.  doi: 10.3934/naco.2017002.

[29]

M. Pervin, S. K. Roy and G. W. Weber, An integrated inventory model with variable holding cost under two levels of trade-credit policy, Numer. Algebr. Control Optim., 8 (2018), 169-191.  doi: 10.3934/naco.2018010.

[30]

B. Richard, Dynamic programming treatment of the travelling salesman problem, J. Assoc. Comput. Mach., 9 (1962), 61-63.  doi: 10.1145/321105.321111.

[31]

E. Ridge and D. Kudenko, Tuning an algorithm using design of experiments, Experimental Methods for the Analysis of Optimization Algorithms, (eds. T. Bartz-Beielstein, M. Chiarandini, L. Paquete and M. Preuss), Springer, New York, (2010), 265–286. doi: 10.1007/978-3-642-02538-9.

[32]

M. Salari, M. Reihaneh and M. S. Sabbagh, Combining ant colony optimization algorithm and dynamic programming technique for solving the covering salesman problem, Comput. Ind. Eng., 83 (2015), 244-251. 

[33]

R. De Santis, R. Montanari, G. Vignali and E. Bottani, An adapted ant colony optimization algorithm for the minimization of the travel distance of pickers in manual warehouses, Eur. J. Oper. Res., 267 (2018), 120-137.  doi: 10.1016/j.ejor.2017.11.017.

[34]

V. Saw, A. Rahman and W. E. Ong, Shortest path problem on a grid network with unordered intermediate points, J. Phys. Conf. Ser., 893 (2017). doi: 10.1088/1742-6596/893/1/012066.

[35]

P. I. Stetsyuk, Problem statements for k-node shortest path and k-node shortest cycle in a complete graph, Cybern. Syst. Anal., 52 (2016), 71-75.  doi: 10.1007/s10559-016-9801-x.

[36]

D. Sudholt and C. Thyssen, Running time analysis of ant colony optimization for shortest path problems, J. Discret. Algorithms, 10 (2012), 165-180.  doi: 10.1016/j.jda.2011.06.002.

[37]

D. Sudholt and C. Thyssen, A simple ant colony optimizer for stochastic shortest path problems, Algorithmica, 64 (2012), 643-672.  doi: 10.1007/s00453-011-9606-2.

[38]

T. Vidal, M. Battarra, A. Subramanian and G. Erdogan, Hybrid metaheuristics for the clustered vehicle routing problem, Comput. Oper. Res., 58 (2015), 87-99.  doi: 10.1016/j.cor.2014.10.019.

[39]

Y. Wang, The hybrid genetic algorithm with two local optimization strategies for traveling salesman problem, Comput. Ind. Eng., 70 (2014), 124-133. 

[40]

J. Xiao, Y. Zhang, X. Jia and X. Zhou, A schedule of join operations to reduce I/O cost in spatial database systems, Data Knowl. Eng., 35 (2000), 299-317. 

[41]

J. Yang, X. Shi, M. Marchese and Y. Liang, Ant colony optimization method for generalized TSP problem, Prog. Nat. Sci., 18 (2008), 1417-1422.  doi: 10.1016/j.pnsc.2008.03.028.

show all references

References:
[1]

R. K. Ahuja, T. L. Magnanti and J. B. Orlin, Network Flows: Theory, Algorithms, and Applications, 1$^st$ edition, Prentice hall, New York, 1993.

[2]

M. M. Alipour and S. N. Razavi, A new multiagent reinforcement learning algorithm to solve the symmetric traveling salesman problem, Multiagent Grid Syst., 11 (2015), 107-119. 

[3]

M. M. Alipour, S. N. Razavi, M. R. Feizi Derakhshi and M. A. Balafar, A hybrid algorithm using a genetic algorithm and multiagent reinforcement learning heuristic to solve the traveling salesman problem, Neural Comput. Appl., 30 (2018), 2935-2951. 

[4]

B. Appleton and C. Sun, Circular shortest paths by branch and bound, Pattern Recognit., 36 (2003), 2513-2520. 

[5]

A. A. Bertossi, The edge Hamiltonian path problem is NP-complete, Inf. Process. Lett., 13 (1981), 157-159.  doi: 10.1016/0020-0190(81)90048-X.

[6]

B. Bontoux, C. Artigues and D. Feillet, A memetic algorithm with a large neighborhood crossover operator for the generalized traveling salesman problem, Comput. Oper. Res., 37 (2010), 1844-1852.  doi: 10.1016/j.cor.2009.05.004.

[7]

G. A. Bula, C. Prodhon, F. A. Gonzalez, H. M. Afsar and N. Velasco, Variable neighborhood search to solve the vehicle routing problem for hazardous materials transportation, J. Hazard. Mater., 324 (2017), 472-480. 

[8]

E. Cao, M. Lai and H. Yang, Open vehicle routing problem with demand uncertainty and its robust strategies, Expert Syst. Appl., 41 (2014), 3569-3575. 

[9]

T. S. Chang, Y. W. Wan and W. T. Ooi, A stochastic dynamic traveling salesman problem with hard time windows, Eur. J. Oper. Res., 198 (2009), 748-759.  doi: 10.1016/j.ejor.2008.10.012.

[10]

S. S. Choong, L. P. Wong and C. P. Lim, An artificial bee colony algorithm with a modified choice function for the traveling salesman problem, Swarm Evol. Comput., 44 (2019), 622-635. 

[11]

A. Colorni, M. Dorigo, V. Maniezzo, D. Elettronica and P. Milano, Distributed optimization by ant colonies, The 1991 European Conference on Artificial Life, (1991), 134–142.

[12]

D. Ferone, P. Festa, F. Guerriero and D. Laganá, The constrained shortest path tour problem, Comput. Oper. Res., 74 (2016), 64-77.  doi: 10.1016/j.cor.2016.04.002.

[13]

D. Ferone, P. Festa, F. Guerriero and D. Laganá, An integer linear programming model for the constrained shortest path tour problem, Electron. Notes Discret. Math., 69 (2018), 141-148.  doi: 10.1016/j.endm.2018.07.019.

[14]

A. Gunawan, H. C. Lau and Li ndawati, Fine-tuning algorithm parameters using the design of experiments approach, Lect. Notes Comput. Sci., 6683 (2011), 278-292. 

[15]

M. Held and R. M. Karp, A dynamic programming approach to sequencing problems, J. Soc. Ind. Appl. Math., 10 (1962), 196-210. 

[16]

J. Jana and S. Kumar Roy, Solution of matrix games with generalised trapezoidal fuzzy payoffs, Fuzzy Inf. Eng., 10 (2018), 213-224. 

[17]

J. Jana and S. K. Roy, Dual hesitant fuzzy matrix games: based on new similarity measure, Soft Comput., 23 (2019), 8873-8886. 

[18]

M. Kuby, O. M. Araz, M. Palmer and I. Capar, An efficient online mapping tool for finding the shortest feasible path for alternative-fuel vehicles, Int. J. Hydrogen Energy, 39 (2014), 18433-18439. 

[19]

S. Kumar Roy, M. Pervin and G. Wilhelm Weber, Imperfection with inspection policy and variable demand under trade-credit: a deteriorating inventory model, Numer. Algebr. Control Optim., 10 (2020), 45-74. 

[20]

T. H. Lai and S. S. Wei, The edge Hamiltonian path problem is NP-complete for bipartite graphs, Inf. Process. Lett., 46 (1993), 21-26.  doi: 10.1016/0020-0190(93)90191-B.

[21]

C. P. Lam, J. Xiao and H. Li, Ant colony optimisation for generation of conformance testing sequences using characterising sequences, The 3rd IASTED International Conference on Advances in Computer Science and Technology (ACS2007), (2007), 140–146.

[22]

E. B. De Lima, G. L. Pappa, J. M. De Almeida, M. A. Goncalves and W. Meira, Tuning genetic programming parameters with factorial designs, IEEE World Congr. Comput. Intell., IEEE Congr. Evol. Comput. 2010.

[23]

Y. H. Liu, Different initial solution generators in genetic algorithms for solving the probabilistic traveling salesman problem, Appl. Math. Comput., 216 (2010), 125-137.  doi: 10.1016/j.amc.2010.01.021.

[24]

S. de Mesquita, A. R. Backes and P. Cortez, Texture analysis and classification using shortest paths in graphs, Pattern Recognit. Lett., 34 (2013), 1314-1319.  doi: 10.1109/TIP.2014.2333655.

[25]

M. Mobin, S. M. Mousavi, M. Komaki and M. Tavana, A hybrid desirability function approach for tuning parameters in evolutionary optimization algorithms, Meas. J. Int. Meas. Confed., 114 (2018), 417-427. 

[26]

D. C. Montgomery, Design And Analysis of Experiments, 5$^th$ edition, Wiley, New York, 1984.

[27]

C. M. Papadimitriou, Computational Complexity, 1$^st$ edition, Addison-Wesley, New York, 1994.

[28]

M. Pervin, S. K. Roy and G. W. Weber, A two-echelon inventory model with stock-dependent demand and variable holding cost for deteriorating items, Numer. Algebr. Control Optim., 7 (2017), 21-50.  doi: 10.3934/naco.2017002.

[29]

M. Pervin, S. K. Roy and G. W. Weber, An integrated inventory model with variable holding cost under two levels of trade-credit policy, Numer. Algebr. Control Optim., 8 (2018), 169-191.  doi: 10.3934/naco.2018010.

[30]

B. Richard, Dynamic programming treatment of the travelling salesman problem, J. Assoc. Comput. Mach., 9 (1962), 61-63.  doi: 10.1145/321105.321111.

[31]

E. Ridge and D. Kudenko, Tuning an algorithm using design of experiments, Experimental Methods for the Analysis of Optimization Algorithms, (eds. T. Bartz-Beielstein, M. Chiarandini, L. Paquete and M. Preuss), Springer, New York, (2010), 265–286. doi: 10.1007/978-3-642-02538-9.

[32]

M. Salari, M. Reihaneh and M. S. Sabbagh, Combining ant colony optimization algorithm and dynamic programming technique for solving the covering salesman problem, Comput. Ind. Eng., 83 (2015), 244-251. 

[33]

R. De Santis, R. Montanari, G. Vignali and E. Bottani, An adapted ant colony optimization algorithm for the minimization of the travel distance of pickers in manual warehouses, Eur. J. Oper. Res., 267 (2018), 120-137.  doi: 10.1016/j.ejor.2017.11.017.

[34]

V. Saw, A. Rahman and W. E. Ong, Shortest path problem on a grid network with unordered intermediate points, J. Phys. Conf. Ser., 893 (2017). doi: 10.1088/1742-6596/893/1/012066.

[35]

P. I. Stetsyuk, Problem statements for k-node shortest path and k-node shortest cycle in a complete graph, Cybern. Syst. Anal., 52 (2016), 71-75.  doi: 10.1007/s10559-016-9801-x.

[36]

D. Sudholt and C. Thyssen, Running time analysis of ant colony optimization for shortest path problems, J. Discret. Algorithms, 10 (2012), 165-180.  doi: 10.1016/j.jda.2011.06.002.

[37]

D. Sudholt and C. Thyssen, A simple ant colony optimizer for stochastic shortest path problems, Algorithmica, 64 (2012), 643-672.  doi: 10.1007/s00453-011-9606-2.

[38]

T. Vidal, M. Battarra, A. Subramanian and G. Erdogan, Hybrid metaheuristics for the clustered vehicle routing problem, Comput. Oper. Res., 58 (2015), 87-99.  doi: 10.1016/j.cor.2014.10.019.

[39]

Y. Wang, The hybrid genetic algorithm with two local optimization strategies for traveling salesman problem, Comput. Ind. Eng., 70 (2014), 124-133. 

[40]

J. Xiao, Y. Zhang, X. Jia and X. Zhou, A schedule of join operations to reduce I/O cost in spatial database systems, Data Knowl. Eng., 35 (2000), 299-317. 

[41]

J. Yang, X. Shi, M. Marchese and Y. Liang, Ant colony optimization method for generalized TSP problem, Prog. Nat. Sci., 18 (2008), 1417-1422.  doi: 10.1016/j.pnsc.2008.03.028.

Figure 1.  The horizontal and orthogonal movements in the grid networks
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Figure 2.  The shortest path tree
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Figure 3.  The ACO Algorithm for the C-SHP problem
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Figure 4.  The initial neighborhood methods
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Figure 5.  The pareto charts of the standardized effects for the responses in the screening phase for the network 200$ \times $200
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Figure 6.  The pareto charts of the standardized effects in the network 200$ \times $200 for the responses in Box–Behnken design
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Figure 7.  The optimized fitted response plots for the network 200$ \times $200
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Figure 8.  The solution diagram of ACO algorithm by optimal tuning of parameters
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Table 1.  The design factors and their levels
factors levels
-1 0 1
A $ \alpha $ 1 7 13
B $ \beta $ 0 6 13
C $ Q $ 1 2 4
D $ \tau (0) $ 0.1 0.5 0.9
E $ \rho $ 0.01 0.5 0.99
F initial solution RO NN1 NN2
G ant number 0.5 1 1.5
Blocks instance size small moderate large
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factors levels
-1 0 1
A $ \alpha $ 1 7 13
B $ \beta $ 0 6 13
C $ Q $ 1 2 4
D $ \tau (0) $ 0.1 0.5 0.9
E $ \rho $ 0.01 0.5 0.99
F initial solution RO NN1 NN2
G ant number 0.5 1 1.5
Blocks instance size small moderate large
Table 2.  The optimal coded and uncoded settings of the factors
network size factors $ \alpha $ $ \beta $ $ Q $ $ \tau (0) $ $ \rho $ initial solution ant number desirability value
100$ \times $100 optimal coded 1 -1 1 -1 -0.68 0.05 -0.98 0.9640
optimal uncoded 13 0 4 0.1 0.17 NN1 0.5
200$ \times $200 optimal coded -1 -1 -0.99 -0.70 0.68 -0.04 -1 0.9957
optimal uncoded 1 0 1 0.22 0.83 NN1 0.5
400$ \times $400 optimal coded -1 -0.54 -1 1 1 0.15 -1 0.8996
optimal uncoded 1 3 1 0.9 0.99 NN1 0.5
Table Options

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network size factors $ \alpha $ $ \beta $ $ Q $ $ \tau (0) $ $ \rho $ initial solution ant number desirability value
100$ \times $100 optimal coded 1 -1 1 -1 -0.68 0.05 -0.98 0.9640
optimal uncoded 13 0 4 0.1 0.17 NN1 0.5
200$ \times $200 optimal coded -1 -1 -0.99 -0.70 0.68 -0.04 -1 0.9957
optimal uncoded 1 0 1 0.22 0.83 NN1 0.5
400$ \times $400 optimal coded -1 -0.54 -1 1 1 0.15 -1 0.8996
optimal uncoded 1 3 1 0.9 0.99 NN1 0.5
Table 3.  The optimal prediction and the confidence intervals of the responses
Network Response Fit SE Fit 95% CI 95% PI
100$ \times $100 Improve 0.2970 0.0848 (0.1226, 0.4713) (0.0829, 0.5111)
CPU Time 18 10.1 (-2.8, 38.8) (-7.6, 43.5)
Opt. Sol. 3608 318 (2954, 4262) (2804, 4412)
200$ \times $200 Improve 0.2119 0.0534 (0.1020, 0.3217) (0.0724, 0.3513)
CPU Time 161.25 4.77 (151.44,171.05) (148.80,173.69)
Opt. Sol. 13719 654 (12375, 15063) (12013, 15425)
400$ \times $400 Improve 0.1605 0.0413 (0.0756, 0.2454) (0.0555, 0.2655)
CPU Time 1763 647 (434, 3092) (119, 3407)
Opt. Sol. 54087 1726 (50540, 57634) (49699, 58474)
Table Options

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Network Response Fit SE Fit 95% CI 95% PI
100$ \times $100 Improve 0.2970 0.0848 (0.1226, 0.4713) (0.0829, 0.5111)
CPU Time 18 10.1 (-2.8, 38.8) (-7.6, 43.5)
Opt. Sol. 3608 318 (2954, 4262) (2804, 4412)
200$ \times $200 Improve 0.2119 0.0534 (0.1020, 0.3217) (0.0724, 0.3513)
CPU Time 161.25 4.77 (151.44,171.05) (148.80,173.69)
Opt. Sol. 13719 654 (12375, 15063) (12013, 15425)
400$ \times $400 Improve 0.1605 0.0413 (0.0756, 0.2454) (0.0555, 0.2655)
CPU Time 1763 647 (434, 3092) (119, 3407)
Opt. Sol. 54087 1726 (50540, 57634) (49699, 58474)
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