Optimization of Multiple Traveling Salesman Problem Based on Simulated Annealing Genetic Algorithm

Xu, M.; Sheng, Li; Guo, Jian

doi:10.1051/matecconf/201710002025

Cited by 6 publications

(7 citation statements)

References 20 publications

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“…The input latitude and longitude data of the robot car ( , ) and the target point ( , ), which were received from the GPS, were used in equation 12 to get ∅ (the angle of the target point against the exact north). Then we deducted H, the heading direction (see equation 13), to get the direction error , which was the angle between the current direction and the target point. We then used the proportional control to multiply (the gain used for servo proportional control; p = 0.161) after adding c (the center angle of the servo motor for the steering) to obtain θ (that is, the command sent to the servo motor for moving).…”

Section: Experimental Vehiclementioning

confidence: 99%

A Hybrid Tabu Search and 2-opt Path Programming for Mission Route Planning of Multiple Robots under Range Limitations

2020

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The application of an unmanned vehicle system allows for accelerating the performance of various tasks. Due to limited capacities, such as battery power, it is almost impossible for a single unmanned vehicle to complete a large-scale mission area. An unmanned vehicle swarm has the potential to distribute tasks and coordinate the operations of many robots/drones with very little operator intervention. Therefore, multiple unmanned vehicles are required to execute a set of well-planned mission routes, in order to minimize time and energy consumption. A two-phase heuristic algorithm was used to pursue this goal. In the first phase, a tabu search and the 2-opt node exchange method were used to generate a single optimal path for all target nodes; the solution was then split into multiple clusters according to vehicle numbers as an initial solution for each. In the second phase, a tabu algorithm combined with a 2-opt path exchange was used to further improve the in-route and cross-route solutions for each route. This diversification strategy allowed for approaching the global optimal solution, rather than a regional one with less CPU time. After these algorithms were coded, a group of three robot cars was used to validate this hybrid path programming algorithm.

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Section: Experimental Vehiclementioning

confidence: 99%

A Hybrid Tabu Search and 2-opt Path Programming for Mission Route Planning of Multiple Robots under Range Limitations

2020

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“…The maximum number L and minimum number of cities that each salesman should visit K were defined and limited to solve MTSP instances using the TSPLIB. Furthermore, Mingji Xu et al [11] used a hybrid genetic algorithm (GA) and simulated annealing (SA) to solve MTSP, enhancing the local search ability to achieve a better global optimal solution than could be obtained using a general GA; however, the solution was proven not to be the best in later research. Using ACO and GA to solve MTSP problems may not always yield consistent solutions even under exactly the same initial test setting, and each case must be tested continuously to converge the best solution.…”

Section: Related Workmentioning

confidence: 99%

“…Per (8,9), all the targets are entered and exited by the car, and per (10), each car must visit at least one target. Finally, (11) prevents the problem of a sub-path that does not include the original point. This is an NP-Hard problem, and heuristic algorithms are always adopted to solve this type of complicated optimization problem.…”

Section: Design and Principle Of Path Programmingmentioning

confidence: 99%

“…Therefore, the innovative algorithm developed for this research was used to solve the MTSP problem for comparisons. Mingji Xu et al [11] set 50 target points to test the modified simulated annealing genetic algorithm (MSAGA). For this research, a new algorithm was tested with the same instance as in Xu's research and was compared to the result of Xu's algorithm.…”

Section: Benchmark Test and Comparisonmentioning

confidence: 99%

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Failure-Robot Path Complementation for Robot Swarm Mission Planning

Lee

Chen

2019

Applied Sciences

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Currently, unmanned vehicles are widely used in different fields of exploration. Due to limited capacities, such as limited power supply, it is almost impossible for one unmanned vehicle to visit multiple wide areas. Multiple unmanned vehicles with well-planned routes are required to minimize an unnecessary consumption of time, distance, and energy waste. The aim of the present study was to develop a multiple-vehicle system that can automatically compile a set of optimum vehicle paths, complement failed assignments, and avoid passing through no-travel zones. A heuristic algorithm was used to obtain an approximate solution within a reasonable timeline. The A* Search algorithm was adopted to determine an alternative path that does not cross the no-travel zone when the distance array was set, and an improved two-phased Tabu search was applied to converge any initial solutions into a feasible solution. A diversification strategy helped identify a global optimal solution rather than a regional one. The final experiments successfully demonstrated a group of three robot cars that were simultaneously dispatched to each of their planned routes; when any car failed during the test, its path was immediately reprogrammed by the monitoring station and passed to the other cars to continue the task until each target point had been visited.

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“…Various modifications were done to improve the performance of the SA algorithm. Several studies about the modification of the SA algorithm can be found in some references [4][5][6][7][8][9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Simulated Annealing – 2 Opt Algorithm for Solving Traveling Salesman Problem

Gunawan¹,

Iryanto²

2023

IJC

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The purpose of this article is to elaborate performance of the hybrid model of Simulated Annealing (SA) and 2 Opt algorithm for solving the traveling salesman problem (TSP). The SA algorithm used in this article is based on the outer and inner loop SA algorithm. The hybrid algorithm has promising results in solving small and medium-scale symmetric traveling salesman problem benchmark tests taken from the TSPLIB reference. Results of the optimal solution and standard deviation indicate that the hybrid algorithm shows good performance in terms of reliability and stability in finding the optimal solution from the TSP benchmark case. Values of average error and standard deviation for all simulations in the medium scale are 0.0267 and 644.12, respectively. Moreover, in some cases namely KroB100, Pr107, and Pr144, the hybrid algorithm finds a better solution compared with the best-known solution mentioned in the reference. Further, the hybrid algorithm is 1.207 – 5.692 times faster than the pure outer and inner loop-based SA algorithm. Additionally, the results show that the hybrid algorithm outperforms other hybrid algorithms such as SA – nearest neighbor (NN) and NN – 2 Opt.

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Optimization of Multiple Traveling Salesman Problem Based on Simulated Annealing Genetic Algorithm

Cited by 6 publications

References 20 publications

A Hybrid Tabu Search and 2-opt Path Programming for Mission Route Planning of Multiple Robots under Range Limitations

A Hybrid Tabu Search and 2-opt Path Programming for Mission Route Planning of Multiple Robots under Range Limitations

Failure-Robot Path Complementation for Robot Swarm Mission Planning

Simulated Annealing – 2 Opt Algorithm for Solving Traveling Salesman Problem

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