# Stochastic Column-Row Method for Travelling Salesman Problem: the Dhouib-Matrix-TSP2

DOI : 10.17577/IJERTV10IS030318

Text Only Version

#### Stochastic Column-Row Method for Travelling Salesman Problem: the Dhouib-Matrix-TSP2

Stochastic Column-Row Method: Dhouib-Matrix-TSP2

Souhail Dhouib

Department of Industrial Management, Higher Institute of Industrial Management Sfax University, Tunisia

AbstractIn this paper, a stochastic method, named Dhouib- Matrix-TSP2, is designed and developed to solve the travelling salesman problem. This method based on our last column-row

n

n

xij 1,

j 1

i 1,…, n

method (Dhouib-Matrix-TSP1) is a stochastic process used as a tool to enrich the diversification phase.

n

n

i 1

xij

1,

j 1,…, n

. (2)

The result test shows that the diversification by the means of

xij 0 or 1,

i 1,…, n , j 1,…, n

the stochastic process generates the shortest total transport distance.

KeywordsCombinatory Optimization; Metaheuristic; Column-Row Method; Stochastic

1. INTRODUCTION

Recently in (Dhouib, 2021), we developed a new constructive method named Dhouib-Matrix-TSP1 in order to quickly find the optimal or near optimal solution for the Travelling Salesman Problem (TSP). This method (Dhouib- Matrix-TSP1) is a deterministic algorithm. Whereas, in this paper, we present the Dhouib-Matrix-TSP2 which is a stochastic version of Dhouib-Matrix-TSP1. This stochastic technique is used as a way to diversify the research space. Hence, this process will allow to generate different realizable solutions.

The outlines of the paper are as follow. In section 2, the travelling salesman problem is described. In section 3, the Dhouib-Matrix-TSP1 is presented. In section 4, a new stochastic column-row algorithm (Dhouib-Matrix-TSP2) to find optimal or near optimal solution for the TSP is depicted. In section 5, the application of our proposed method on a 10×10 distance matrix is provided. Finally, concluding remarks are given in section 6.

2. TRAVELLING SALESMAN PROBLEM

In the travelling salesman problem, the aim is to find the shortest roundtrip. This latter is essentially a Hamiltonian tour, where a set of cities are visited only once except the starting city which will be also the ending one.

The travelling salesman problem is NP-hard. However, it is easily formulated as:

n n

In the literature, there are several research works interested in solving the TSP. (Mehmet & Kalayci. 2021) applies the variable neighborhood search algorithm to optimize the cost- balanced TSP. (Saji & Barkatou, 2021) designs a bat metaheuristic to solve the TSP. (Krzysztof & Urszula, 2020) solves the asymmetric travelling salesman problem using the pheromone-based harmony search metaheuristic. (Akanksha & Sharma, 2019) uses the particle swarm optimization algorithm to solve the TSP. (Todosijevic et al., 2017) develops a variable neighborhood search metaheuristic to optimize the TSP.

3. DHOUIB-MATRIX-TSP1

In our previous research work (Dhouib, 2021), we have presented the deterministic Dhouib-Matrix-TSP1 method which is composed of four easy phases (see Fig 1.).

• Compute the standard deviation for each row

Phase 1

• Select the minimum of booth rows

Phase 2

• Insert the corresponding city and discard its column.

Phase 3

• Transform the generated tour to be a cycle

Phase 4

• Compute the standard deviation for each row

Phase 1

• Select the minimum of booth rows

Phase 2

• Insert the corresponding city and discard its column.

Phase 3

• Transform the generated tour to be a cycle

Phase 4

Fig. 1. Four phases for Dhouib-Matrix-TSP1

Optimize Subject to

dij xij

i 1 j 1

(1)

The four phases of Dhouib-Matrix-TSP1 are described as follow. At first, calculate the standard deviation for each row then find the minimal element existing in the position dxy in the row of the smallest deviation. For more details see (Dhouib, 2021).

Next, insert x and y cities in the list Cycle_List and discard the columns of x and y.

At second, compute the minimal element for row x and row y and select the smallest between them named z.

At third, add z in the Cycle_List and discard its column. Then, return to the last phase if there are still non discarded columns in the matrix, unless, go to the next phase.

Finally, transform the generated route in Cycle_List to be a roundtrip: translate the cities in the beginning of the roundtrip at the end until the starting city will be at the first position. In addition, insert the starting city at the last position in the roundtrip.

4. DHOUIB-MATRIX-TSP2 DESIGN

This section will describe the proposed method: the Dhouib-Matrix-TSP2. Actually, it is a stochastic version of the Dhouib-Matrix-TSP1. Whereas, the selection of the minimal element is replaced by the random selection from k minimal elements: this will enhance the diversification of the research space.

The pseudo-code of the proposed approach, Dhouib- Matrix-TSP2, is presented as follow (see Fig 2).

2

2

Fig. 2. Dhouib-Matrix-TSP2

A numerical example from (Mandziuk, 1996), 10×10 matrix (see Fig 3.), is used to prove the good convergence and the efficiency of Dhouib-Matrix-TSP2.

 0 0.629 0.629 0 0.408 0.228 0.564 0.212 0.117 0.712 0.620 0.738 0.327 0.461 0.814 0.304 0.799 0.626 0.370 0.260 0.407 0.228 0 0.265 0.500 0.672 0.327 0.480 0.668 0.064 0.564 0.212 0.264 0 0.612 0.530 0.304 0.250 0.427 0.242 0.117 0.712 0.500 0.617 0 0.569 0.340 0.865 0.789 0.454 0.620 0.738 0.672 0.530 0.569 0 0.362 0.663 0.323 0.610 0.326 0.461 0.326 0.304 0.340 0.362 0 0.539 0.474 0.262 0.814 0.304 0.480 0.250 0.865 0.663 0.539 0 0.430 0.476 0.798 0.626 0.668 0.428 0.789 0.323 0.474 0.430 0 0.622
 0 0.629 0.629 0 0.408 0.228 0.564 0.212 0.117 0.712 0.620 0.738 0.327 0.461 0.814 0.304 0.799 0.626 <>0.370 0.260 0.407 0.228 0 0.265 0.500 0.672 0.327 0.480 0.668 0.064 0.564 0.212 0.264 0 0.612 0.530 0.304 0.250 0.427 0.242 0.117 0.712 0.500 0.617 0 0.569 0.340 0.865 0.789 0.454 0.620 0.738 0.672 0.530 0.569 0 0.362 0.663 0.323 0.610 0.326 0.461 0.326 0.304 0.340 0.362 0 0.539 0.474 0.262 0.814 0.304 0.480 0.250 0.865 0.663 0.539 0 0.430 0.476 0.798 0.626 0.668 0.428 0.789 0.323 0.474 0.430 0 0.622

0.370 0.260 0.064 0.242 0.454 0.610 0.262 0.476 0.622 0

Fig. 3. Distance Matrix

Well, to quickly and effectively search the optimal path for the ten cities of the travelling salesman problem, we first apply the Dhouib-Matrix-TSP1 which needs only 10 iterations.

0.2574

0.2388

0.2175

0.1809

0.2654

0.2108

0.1399

0.2476

0.2283

0.1997

0.2574

0.2388

0.2175

0.1809

0.2654

0.2108

0.1399

0.2476

0.2283

0.1997

0 0.629 0.408 0.564 0.117 0.620 0.327 0.814 0.799 0.370

0 0.629 0.408 0.564 0.117 0.620 0.327 0.814 0.799 0.370

0.629 0 0.228 0.212 0.712 0.738 0.461 0.304 0.626 0.260

0.629 0 0.228 0.212 0.712 0.738 0.461 0.304 0.626 0.260

0.407 0.228 0 0.265 0.500 0.672 0.327 0.480 0.668 0.064

0.407 0.228 0 0.265 0.500 0.672 0.327 0.480 0.668 0.064

0.564 0.212 0.264 0 0.612 0.530 0.304 0.250 0.427 0.242

0.564 0.212 0.264 0 0.612 0.530 0.304 0.250 0.427 0.242

0.117 0.712 0.500 0.617 0 0.569 0.340 0.865 0.789 0.454

0.117 0.712 0.500 0.617 0 0.569 0.340 0.865 0.789 0.454

0.620 0.738 0.672 0.530 0.569 0 0.362 0.663 0.323 0.610

0.620 0.738 0.672 0.530 0.569 0 0.362 0.663 0.323 0.610

Step 1: Compute the standard deviation for each row. Then, select the minimal value (0.1399) which is in row 7.

0.326 0.461 0.326 0.304 0.340 0.362 0 0.539 0.474 0.262

0.326 0.461 0.326 0.304 0.340 0.362 0 0.539 0.474 0.262

0.814 0.304 0.480 0.250 0.865 0.663 0.539 0 0.430 0.476

0.814 0.304 0.480 0.250 0.865 0.663 0.539 0 0.430 0.476

0.798 0.626 0.668 0.428 0.789 0.323 0.474 0.430 0 0.622

0.798 0.626 0.668 0.428 0.789 0.323 0.474 0.430 0 0.622

0.370 0.260 0.064 0.242 0.454 0.610 0.262 0.476 0.622

0.370 0.260 0.064 0.242 0.454 0.610 0.262 0.476 0.622

0

0

Fig. 4. Compute the standard deviation for each row

0.37

0.26

0.06

0.24

0.45

0.61

0.26

0.47

0.62

0.37

0.26

0.06

0.24

0.45

0.61

0.26

0.47

0.62

0.327

0.461

0.327

0.304

0.340

0.362

0

0.539

0.474

0.327

0.461

0.327

0.304

0.340

0.362

0

0.539

0.474

0 0.629 0.408 0.564 0.117 0.620

0 0.629 0.408 0.564 0.117 0.620

0.228 0.212 0.712 0.738

0.228 0.212 0.712 0.738

0.407 0.228 0 0.265 0.500 0.672

0.564 0.212 0.264 0 0.612 0.530

0.620 0.738 0.672 0.530 0.569 0

0.407 0.228 0 0.265 0.500 0.672

0.564 0.212 0.264 0 0.612 0.530

0.620 0.738 0.672 0.530 0.569 0

0.814 0.799

0.304 0.626

0.480 0.668

0.250 0.427

0.865 0.789

0.663 0.323

0.539 0.474

0 0.430

0.814 0.799

0.304 0.626

0.480 0.668

0.250 0.427

0.865 0.789

0.663 0.323

0.539 0.474

0 0.430

0

0

0.629 0

0.629 0

0

0

4

4

2

2

0.117 0.712 0.500 0.617 0 0.569

0.117 0.712 0.500 0.617 0 0.569

4

4

0

6

0

6

Step 2: Select the minimal element in row 7 which is 0.262 in position d7,10. Thus, insert cities 7 and 10 in the list and discard their columns (See Fig 5.).

2

2

0.326 0.461 0.326 0.304 0.340 0.362

0.326 0.461 0.326 0.304 0.340 0.362

0.814 0.304 0.480 0.250 0.865 0.663

0.814 0.304 0.480 0.250 0.865 0.663

0.798 0.626 0.668 0.428 0.789 0.323

0.798 0.626 0.668 0.428 0.789 0.323

0.430 0

0.430 0

2

2

0.370 0.260 0.064 0.242 0.454 0.610 0.262 0.476 0.622

0.370 0.260 0.064 0.242 0.454 0.610 0.262 0.476 0.622

0

0

Fig. 5. Discard columns 7 and 10

 0 0.629 0.629 0 0.407 0.228 8 8 0.564 0.117 0.620 0.212 0.712 0.738 0.265 0.500 0.672 0.814 0.304 0.480 0.799 0.626 0.668 0 0 4 0.564 0.212 4 0 0.612 0.530 0.250 0.427 2 0.117 0.712 0 0.617 0 0.569 0.865 0.789 4 0.620 0.738 0.326 0.461 0.798 0.626 0.370 0.260 2 6 0 8 0.530 0.304 0.250 0.428 0.242 0.569 0.340 0.865 0.789 0.454 0 0.362 0.663 0.323 0.610 0.663 0.539 0 0.430 0.262 0.476 0.323 0.474 0.430 0 0.622 0 0 6 2
 0 0.629 0.629 0 0.407 0.228 8 8 0.564 0.117 0.620 0.212 0.712 0.738 0.265 0.500 0.672 0.814 0.304 0.480 0.799 0.626 0.668 0 0 4 0.564 0.212 4 0 0.612 0.530 0.250 0.427 2 0.117 0.712 0 0.617 0 0.569 0.865 0.789 4 0.620 0.738 0.326 0.461 0.798 0.626 0.370 0.260 2 6 0 8 0.530 0.304 0.250 0.428 0.242 0.569 0.340 0.865 0.789 0.454 0 0.362 0.663 0.323 0.610 0.663 0.539 0 0.430 0.262 0.476 0.323 0.474 0.430 0 0.622 0 0 6 2

Step 3: Select the minimal element in row 7 and row 10 and select the smallest one which is 0.064 in position d10,3. Thus, insert city 3 after city 10 and discard its column (See Fig 6.).

0.37

0.26

0.06

0.24

0.45

0.61

0.26

0.47

0.62

0.37

0.26

0.06

0.24

0.45

0.61

0.26

0.47

0.62

0.327

0.461

0.327

0.304

0.340

0.362

0

0.539

0.474

0.327

0.461

0.327

0.304

0.340

0.362

0

0.539

0.474

0.40

0.22

0

0.26

0.50

0.67

0.32

0.48

0.66

0.40

0.22

0

0.26

0.50

0.67

0.32

0.48

0.66

5. EXPERIMENTS AND RESULTS ANALYSIS

The proposed methods, namely Dhouib-Matrix-TSP1 and Dhouib-Matrix-TSP2, are developed using Python language.

0.814 0.304

0.064

0.064

0.37

0.26

0.06

0.24

0.45

0.61

0.26

0.47

0.62

0.37

0.26

0.06

0.24

0.45

0.61

0.26

0.47

0.62

0.327

0.461

0.327

0.304

0.340

0.362

0

0.539

0.474

0.327

0.461

0.327

0.304

0.340

0.362

0

0.539

0.474

0.40

0.22

0

0.26

0.50

0.67

0.32

0.48

0.66

0.40

0.22

0

0.26

0.50

0.67

0.32

0.48

0.66

0.629

0

0.228

0.212

0.712

0.738

0.461

0.304

0.626

0.629

0

0.228

0.212

0.712

0.738

0.461

0.304

0.626

0

0

0.629

0.629

0.407

0.407

8 0.564 0.117 0.620

8 0.212 0.712 0.738

0.265 0.500 0.672

8 0.564 0.117 0.620

8 0.212 0.712 0.738

0.265 0.500 0.672

0.564 4 0

0.564 4 0

0.620

0.620

0.612 0.530

0 0.617 0 0.569

0.612 0.530

0 0.617 0 0.569

0.814 0.799

0.304 0.626

0.480 0.668

0.250 0.427

0.865 0.789

0.663 0.323

0.539 0.474

0 0.430

0.814 0.799

0.304 0.626

0.480 0.668

0.250 0.427

0.865 0.789

0.663 0.323

0.539 0.474

0 0.430

0

0

0

0

4

4

2

2

0.117

0.117

4

4

2 0.530 0.569 0

2 0.530 0.569 0

0

6

0

6

Step 4: Compute the minimal element in row 7 and row 3 and select the smallest one which is 0.228 in position d3,2. Thus, insert citiy 2 after city 3 and discard its column (See Fig 7.).

2

2

0.064

0.064

0.326

0.326

0.814

0.814

0.798

0.798

0.370 0.260

0.370 0.260

6 0.304 0.340 0.362

0 0.250 0.865 0.663

8 0.428 0.789 0.323

6 0.304 0.340 0.362

0 0.250 0.865 0.663

8 0.428 0.789 0.323

0.430

0.430

0

0

2

2

0.242 0.454 0.610 0.262 0.476 0.622

0.242 0.454 0.610 0.262 0.476 0.622

0

0

0.37

0.26

0.06

0.24

0.45

0.61

0.26

0.47

0.62

0.37

0.26

0.06

0.24

0.45

0.61

0.26

0.47

0.62

0.327

0.461

0.327

0.304

0.340

0.362

0

0.539

0.474

0.327

0.461

0.327

0.304

0.340

0.362

0

0.539

0.474

0.40

0.22

0

0.26

0.50

0.67

0.32

0.48

0.66

0.40

0.22

0

0.26

0.50

0.67

0.32

0.48

0.66

0.629

0

0.228

0.212

0.712

0.738

0.461

0.304

0.626

0.629

0

0.228

0.212

0.712

0.738

0.461

0.304

0.626

.564

.212

.265

0

.617

.530

.304

.250

.428

.242

.564

.212

.265

0

.617

.530

.304

.250

.428

.242

0

0

0

0

0.629

0.629

0.407

0.564

0.407

0.564

8 0

8

0

4

0 0

2 0

6 0

0 0

8 0

0

8 0

8

0

4

0 0

2 0

6 0

0 0

8 0

0

0.117 0.620

0.712 0.738

0.500 0.672

0.612 0.530

0 0.569

0.117 0.620

0.712 0.738

0.500 0.672

0.612 0.530

0 0.569

0.814 0.799

0.304 0.626

0.480 0.668

0.250 0.427

0.865 0.789

0.663 0.323

0.539 0.474

0 0.430

0.814 0.799

0.304 0.626

0.480 0.668

0.250 0.427

0.865 0.789

0.663 0.323

0.539 0.474

0 0.430

0

0

4

2

4

2

0

0

0.117

0.117

0.620

0.620

4

4

0

6

0

6

Step 5: Select the minimal element in row 7 and row 2 and select the smallest one which is 0.212 in position d2,4. Thus, insert city 4 after city 2 and discard its column (See Fig 8.).

2

2

0.064

0.064

0

0

0.326

0.326

0.814

0.814

0.569

0.569

0.798

0.798

0.370 0.260

0.370 0.260

0.340 0.362

0.865 0.663

0.789 0.323

0.340 0.362

0.865 0.663

0.789 0.323

0.430 0

0.430 0

2

2

0.454 0.610 0.262 0.476 0.622

0.454 0.610 0.262 0.476 0.622

0

0

2

2

Step 6: Select the minimal element in row 7 and row 4 and

0.37

0.26

0.06

0.24

0.45

0.61

0.26

0.47

0.62

0.37

0.26

0.06

0.24

0.45

0.61

0.26

0.47

0.62

0.327

0.461

0.327

0.304

0.340

0.362

0

0.539

0.474

0.327

0.461

0.327

0.304

0.340

0.362

0

0.539

0.474

0.40

0.22

0

0.26

0.50

0.7

0.32

0.48

0.66

0.40

0.22

0

0.26

0.50

0.67

0.32

0.48

0.66

0.629

0

0.228

0.212

0.712

0.738

0.461

0.304

0.626

0.629

0

0.228

0.212

0.712

0.738

0.461

0.304

0.626

.564

.212

.265

0

.617

.530

.304

.250

.428

.242

.564

.212

.265

0

.617

.530

.304

.250

.428

.242

0

0

0.814

0.304

0.480

0.250

0.865

0.663

0.539

0

0.430

0.814

0.304

0.480

0.250

0.865

0.663

0.539

0

0.430

0

0.629

0.407

0.564

0.117

0.620

0.326

0.814

0.798

0

0.629

0.407

0.564

0.117

0.620

0.326

0.814

0.798

0.117

0.712

0.500

0.612

0

0.569

0.340

0.865

0.789

0.117

0.712

0.500

0.612

0

0.569

0.340

0.865

0.789

0.799

0.626

0.668

0.427

0.789

0.323

0.474

0.430

0

0.799

0.626

0.668

0.427

0.789

0.323

0.474

0.430

0

0.37

0.26

0.06

0.24

0.45

0.61

0.26

0.47

0.62

0.37

0.26

0.06

0.24

0.45

0.61

0.26

0.47

0.62

2

2

0.327

0.461

0.327

0.304

0.340

0.362

0

0.539

0.474

0.327

0.461

0.327

0.304

0.340

0.362

0

0.539

0.474

0.40

0.22

0

0.26

0.50

0.67

0.32

0.48

0.66

0.40

0.22

0

0.26

0.50

0.67

0.32

0.48

0.66

0.064

0.064

0.629

0

0.228

0.212

0.712

0.738

0.461

0.304

0.626

0.629

0

0.228

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0.712

0.738

0.461

0.304

0.626

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.212

.265

0

.617

.530

.304

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.428

.242

.564

.212

.265

0

.617

.530

.304

.250

.428

.242

0

0

0.814

0.304

0.480

0.250

0.865

0.663

0.539

0

0.430

0.814

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0.480

0.250

0.865

0.663

0.539

0

0.430

0

0.629

0.407

0.564

0.117

0.620

0.326

0.814

0.798

0

0.629

0.407

0.564

0.117

0.620

0.326

0.814

0.798

0.117

0.712

0.500

0.612

0

0.569

0.340

0.865

0.789

0.117

0.712

0.500

0.612

0

0.569

0.340

0.865

0.789

0.370 0.260

0.370 0.260

8 0

8

0

4

0 0

2 0

6 0

0 0

8 0

0

8 0

8

0

4

0 0

2 0

6 0

0 0

8 0

0

0.620

0.738

0.672

0.530

0.569

0

0.362

0.663

0.323

0.620

0.738

0.672

0.530

0.569

0

0.362

0.663

0.323

0.799

0.626

0.668

0.427

0.789

0.323

0.474

0.430

0

0.799

0.626

0.668

0.427

0.789

0.323

0.474

0.430

0

0

0

0

0

4

4

2

2

4

4

0

6

2

0

6

2

0.454 0.610 0.262 0.476 0.622

0.454 0.610 0.262 0.476 0.622

0

0

0.370 0.260

0.370 0.260

8 0

8

0

4

0 0

2 0

6 0

0 0

8 0

0

8 0

8

0

4

0 0

2 0

6 0

0 0

8 0

0

0.620

0.738

0.672

0.530

0.569

0

0.362

0.663

0.323

0.454 0.610 0.262 0.476 0.622

0.620

0.738

0.672

0.530

0.569

0

0.362

0.663

0.323

0.454 0.610 0.262 0.476 0.622

0

0

0

0

4

4

2

2

4

4

0

6

0

6

Step 9: Select the minimal element in rows 5 and 8. Thus, insert city 9 after city 8 and discard its column (See Fig 12.).

2

2

0.064

0.064

0

0

2

2

Step 10: Select the minimal element in rows 5 and 9. Thus, insert city 6 after city 9 and discard its column (See Fig 13.).

0

0

0

0

select the smallest one which is 0.250 in position d

4,8

. Thus,

0.37

0.26

0.06

0.24

0.45

0.61

0.26

0.47

0.62

0.37

0.26

0.06

0.24

0.45

0.61

0.26

0.47

0.62

0.327

0.461

0.327

0.304

0.340

0.362

0

0.539

0.474

0.327

0.461

0.327

0.304

0.340

0.362

0

0.539

0.474

0.40

0.22

0

0.26

0.50

0.67

0.32

0.48

0.66

0.40

0.22

0

0.26

0.50

0.67

0.32

0.48

0.66

0.629

0

0.228

0.212

0.712

0.738

0.461

0.304

0.626

0.629

0

0.228

0.212

0.712

0.738

0.461

0.304

0.626

.564

.212

.265

0

.617

.530

.304

.250

.428

.242

.564

.212

.265

0

.617

.530

.304

.250

.428

.242

0

0

0.814

0.304

0.480

0.250

0.865

0.663

0.539

0

0.430

0.814

0.304

0.480

0.250

0.865

0.663

0.539

0

0.430

2

2

0

0

4

4

4

4

0

0

0.629

0.629

0.407

0.407

8 0

8

0

4

0 0

2 0

6 0

0 0

8 0

0

8 0

8

0

4

0 0

2 0

6 0

0 0

8 0

0

0.117 0.620

0.712 0.738

0.500 0.672

0.612 0.530

0 0.569

0.117 0.620

0.712 0.738

0.500 0.672

0.612 0.530

0 0.569

0.799

0.626

0.668

0.427

0.789

0.323

0.474

0.430

0

0.799

0.626

0.668

0.427

0.789

0.323

0.474

0.430

0

0

0

0

0

4

4

0.564

0.564

2

2

0.117

0.17

0.620

0.620

4

4

0

6

0

6

insert city 8 after city 4 and discard its column (See Fig 9.).

2

2

0.064

0.064

0.569

0.569

0

0

0.326

0.326

0.814

0.814

0.798

0.798

0.370 0.260

0.370 0.260

0.340 0.362

0.865 0.663

0.789 0.323

0.340 0.362

0.865 0.663

0.789 0.323

2

2

0.454 0.610 0.262 0.476 0.622

0.454 0.610 0.262 0.476 0.622

0

0

0.37

0.26

0.06

0.24

0.45

0.61

0.26

0.47

0.62

0.37

0.26

0.06

0.24

0.45

0.61

0.26

0.47

0.62

0.327

0.461

0.327

0.304

0.340

0.362

0

0.539

0.474

0.327

0.461

0.327

0.304

0.340

0.362

0

0.539

0.474

0.40

0.22

0

0.26

0.50

0.67

0.32

0.48

0.66

0.40

0.22

0

0.26

0.50

0.67

0.32

0.48

0.66

0.629

0

0.228

0.212

0.712

0.738

0.461

0.304

0.626

0.629

0

0.228

0.212

0.712

0.738

0.461

0.304

0.626

.564

.212

.265

0

.617

.530

.304

.250

.428

.242

.564

.212

.265

0

.617

.530

.304

.250

.428

.242

0

0

0.814

0.304

0.480

0.250

0.865

0.663

0.539

0

0.430

0.814

0.304

0.480

0.250

0.865

0.663

0.539

0

0.430

0

0.629

0.407

0.564

0.117

0.620

0.326

0.814

0.798

0

0.629

0.407

0.564

0.117

0.620

0.326

0.814

0.798

0.370 0.260

0.370 0.260

8 0

8

0

4

0 0

2 0

6 0

0 0

8 0

0

8 0

8

0

4

0 0

2 0

6 0

0 0

8 0

0

0.117 0.620

0.712 0.738

0.500 0.672

0.612 0.530

0 0.569

0.117 0.620

0.712 0.738

0.500 0.672

0.612 0.530

0 0.569

0.799

0.626

0.668

0.427

0.789

0.323

0.474

0.430

0

0.799

0.626

0.668

0.427

0.789

0.323

0.474

0.430

0

0

0

0

0

4

4

2

4

2

4

0.569 0 0.340 0.362 0.865 0.663

0.789 0.323

0.569 0 0.340 0.362 0.865 0.663

0.789 0.323

0

6

0

6

Step 7: Select the minimal element in row 7 and row 8 and select the smallest one which is 0.326 in position d1,7. Thus, insert city 1 before city 7 and discard its column (See Fig 10.).

2

2

0.064

0.064

0.454 0.610 0.262 0.476 0.622

0.454 0.610 0.262 0.476 0.622

0

0

2

2

Step 8: Select the minimal element in row 1 and row 8 and select the smallest one (d1,5). Thus, insert city 5 before city 1 and discard its column (See Fig 11.).

0

6

2

0

6

2

0.454 0.610 0.262 0.476 0.622

0.454 0.610 0.262 0.476 0.622

0.37

0.26

0.06

0.24

0.45

0.61

0.26

0.47

0.62

0.37

0.26

0.06

0.24

0.45

0.61

0.26

0.47

0.62

0.327

0.461

0.327

0.304

0.340

0.362

0

0.539

0.474

0.327

0.461

0.327

0.304

0.340

0.362

0

0.539

0.474

0.40

0.22

0

0.26

0.50

0.67

0.32

0.48

0.66

0.40

0.22

0

0.26

0.50

0.67

0.32

0.48

0.66

0.064

0.064

0.629

0

0.228

0.212

0.712

0.738

0.461

0.304

0.626

0.629

0

0.228

0.212

0.712

0.738

0.461

0.304

0.626

.564

.212

.265

0

.617

.530

.304

.250

.428

.242

.564

.212

.265

0

.617

.530

.304

.250

.428

.242

0.814

0.304

0.480

0.250

0.865

0.663

0.539

0

0.430

0.814

0.304

0.480

0.250

0.865

0.663

0.539

0

0.430

0

0.629

0.407

0.564

0.117

0.620

0.326

0.814

0.798

0

0.629

0.407

0.564

0.117

0.620

0.326

0.814

0.798

0.117

0.712

0.500

0.612

0

0.569

0.340

0.865

0.789

0.117

0.712

0.500

0.612

0

0.569

0.340

0.865

0.789

0.799

0.626

0.668

0.427

0.789

0.323

0.474

0.430

0

0.799

0.626

0.668

0.427

0.789

0.323

0.474

0.430

0

0.620

0.738

0.672

0.530

0.569

0

0.362

0.663

0.323

0.620

0.738

0.672

0.530

0.569

0

0.362

0.663

0.323

0.370 0.260

0.370 0.260

8 0

8

0

4

0 0

2 0

6 0

0 0

8 0

0

8 0

8

0

4

0 0

2 0

6 0

0 0

8 0

0

0

0

Finally, we need to transform the generated tour {5-1-7- 10-3-2-4-8-9-6} to a roundtrip by translating the cities in the first position to the end until the starting city will be at the first position and add the starting city at the last position {1- 7-10-3-2-4-8-9-6-5-1}.

So, using the Dhouib-Matrix-TSP1, we found 2.78211 (deviation of 3.17%) in only 10 iterations using the standard deviation metric (see Fig 14.).

Fig. 14. The solution found by Dhouib-Matrix-TSP1

When we insert the Dhouib-Matrix-TSP2 in an iterative structure, it can easily find the optimal solution (2.69646). In Fig 15., we can see the step-by-step generation of the optimal solution using Dhouib-Matrix-TSP2.

Fig. 15. The optimal solution found by Dhouib-Matrix-TP2

6. CONCLUSION

This paper aims to insert a stochastic process in a deterministic optimization method (Dhouib-Matrix-TSP1), in order to generate the new method: Dhouib-Matrix-TSP2. The distinguishing feature of this new technique resides in the random selection of the n smallest elements which forced the fetch of new solutions, not simply sought the smallest value as in Dhouib-Matrix-TSP1.

This new stochastic method is developed using Python language and the final test result poves its effectiveness and good convergence. In our future research works, larger scale TSP will be explored through our new method Dhouib- Matrix-TSP2 to search the optimal path.

REFERENCES

1. Dhouib S., "A New Column-Row Method for Traveling Salesman Problem: The Dhouib-Matrix-TSP1," International Journal of Recent Engineering Science, vol. 8, Issue 1, pp.6-10, 2021.

2. Mandziuk J., Solving the Travelling Salesman Problem with a Hopfield – type neural network, Demonstratio Mathematica, vol. 29, issue 1,Â·1996.

3. Glover F., Multi-wave algorithms for metaheuristic optimization, Journal of Heuristics, vol. 22, pp. 331-358, 2016.

4. Saji Y. and Barkatou M., A discrete bat algorithm based on LÃ©vy flights for Euclidean traveling salesman problem, Expert Systems with Applications, vol.172(15), pp.114639, 2021.

5. Todosijevic R., Mjirda A., Mladenovic M., Hanafi S., and Gendron, B., A general variable neighborhood search variants for the travelling salesman problem with draft limits, Optimization Letters, vol.11(6), pp.1047-1056, 2017.

6. Krzysztof S. and Urszula B., The Pheromone-Based Harmony Search Algorithm for the Asymmetric Traveling Salesman Problem, Applied. Sciences, vol.10(18), pp. 6422, 2020.

7. Akanksha S. & Sharma R. S., Design of Semi-chaotic Integration- Based Particle Swarm Optimization Algorithm and Also Solving Travelling Salesman Problem Using It, Soft Computing for Problem Solving, pp. 695-709, 2019.

8. Mehmet A. A. and Kalayci C. B., A Variable Neighborhood Search Algorithm for Cost-Balanced Travelling Salesman Problem, Metaheuristics for Combinatorial Optimization, pp. 23-36, 2021.