Travelling salesman problem for UAV path planning with two parallel optimization algorithms

2017 ◽  
Author(s):  
Jie Chen ◽  
Fang Ye ◽  
Yibing Li
Keyword(s):  
Path Planning ◽  
Travelling Salesman Problem ◽  
Optimization Algorithms ◽  
Parallel Optimization ◽  
Travelling Salesman

scholarly journals A Novel Cooperative Path Planning for Multi-robot Persistent Coverage with Obstacles and Coverage Period Constraints

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1994 ◽  
Author(s):  
Guibin Sun ◽  
Rui Zhou ◽  
Bin Di ◽  
Zhuoning Dong ◽  
Yingxun Wang
Keyword(s):  
Path Planning ◽  
Numerical Simulations ◽  
Obstacle Avoidance ◽  
Travelling Salesman Problem ◽  
Region Of Interest ◽  
Minimum Cost ◽  
Closed Path ◽  
Combinatorial Approach ◽  
Travelling Salesman ◽  
Multi Robot

In this paper, a multi-robot persistent coverage of the region of interest is considered, where persistent coverage and cooperative coverage are addressed simultaneously. Previous works have mainly concentrated on the paths that allow for repeated coverage, but ignored the coverage period requirements of each sub-region. In contrast, this paper presents a combinatorial approach for path planning, which aims to cover mission domains with different task periods while guaranteeing both obstacle avoidance and minimizing the number of robots used. The algorithm first deploys the sensors in the region to satisfy coverage requirements with minimum cost. Then it solves the travelling salesman problem to obtain the frame of the closed path. Finally, the approach partitions the closed path into the fewest segments under the coverage period constraints, and it generates the closed route for each robot on the basis of portioned segments of the closed path. Therefore, each robot can circumnavigate one closed route to cover the different task areas completely and persistently. The numerical simulations show that the proposed approach is feasible to implement the cooperative coverage in consideration of obstacles and coverage period constraints, and the number of robots used is also minimized.

scholarly journals Power Efficient Hierarchical Scheduling for DSP Transformations

VLSI Design ◽  
2002 ◽  
Vol 14 (2) ◽  
pp. 203-217
Author(s):  
P. K. Merakos ◽  
K. Masselos ◽  
C. E. Goutis
Keyword(s):  
Travelling Salesman Problem ◽  
Optimization Algorithms ◽  
Digital Signal ◽  
Scheduling Problem ◽  
Switching Activity ◽  
Travelling Salesman ◽  
Hierarchical Scheduling ◽  
Power Efficient ◽  
Target Architecture ◽  
Selection For

In this paper, the problem of scheduling the computation of partial products in transformational Digital Signal Processing (DSP) algorithms, aiming at the minimization of the switching activity in data and address buses, is addressed. The problem is stated as a hierarchical scheduling problem. Two different optimization algorithms, which are based on the Travelling Salesman Problem (TSP), are defined. The proposed optimization algorithms are independent on the target architecture and can be adapted to take into account it. Experimental results obtained from the application of the proposed algorithms in various widely used DSP transformations, like Discrete Cosine Transform (DCT) and Discrete Fourier Transform (DFT), show that significant switching activity savings in data and address buses can be achieved, resulting in corresponding power savings. In addition, the differences between the two proposed methods are underlined, providing envisage for their suitable selection for implementation, in particular transformational algorithms and architectures.

scholarly journals A Two-Way Parallel Slime Mold Algorithm by Flow and Distance for the Travelling Salesman Problem

2020 ◽  
Vol 10 (18) ◽  
pp. 6180
Author(s):  
Meijiao Liu ◽  
Yanhui Li ◽  
Qi Huo ◽  
Ang Li ◽  
Mingchao Zhu ◽  
...  
Keyword(s):  
Travelling Salesman Problem ◽  
Optimization Method ◽  
Slime Mold ◽  
Parallel Optimization ◽  
Flow Index ◽  
Travelling Salesman ◽  
Random Strategy ◽  
Optimization Principle ◽  
Culture Model ◽  
Better Than

In order to solve the problem of poor local optimization of the Slime Mold Algorithm (SMA) in the Travelling Salesman Problem (TSP), a Two-way Parallel Slime Mold Algorithm by Flow and Distance (TPSMA) is proposed in this paper. Firstly, the flow between each path point is calculated by the “critical pipeline and critical culture” model of SMA; then, according to the two indexes of flow and distance, the set of path points to be selected is obtained; finally, the optimization principle with a flow index is improved with two indexes of flow and distance and added random strategy. Hence, a two-way parallel optimization method is realized and the local optimal problem is solved effectively. Through the simulation of Traveling Salesman Problem Library (TSPLIB) on ulysses16, city31, eil51, gr96, and bier127, the results of TPSMA were improved by 24.56, 36.10, 41.88, 49.83, and 52.93%, respectively, compared to SMA. Furthermore, the number of path points is more and the optimization ability of TPSMA is better. At the same time, TPSMA is closer to the current optimal result than other algorithms by multiple sets of tests, and its time complexity is obviously better than others. Therefore, the superiority of TPSMA is adequately proven.

scholarly journals Comparative Study on Travelling Salesman Problem

2021 ◽  
Vol 23 (07) ◽  
pp. 853-857
Author(s):  
Yatharth Srivastav ◽  
◽  
J.K. Saini ◽  
Keyword(s):  
Comparative Study ◽  
Travelling Salesman Problem ◽  
Optimization Algorithms ◽  
Minimum Cost ◽  
Optimization Techniques ◽  
Travelling Salesman

Travelling Salesman Problem (TSP) is a kind of LPP to find a minimum cost sequence in order to travel in each set of cities in a way that starting as well as ending should be on the same city and each city is visited exactly one time. In this paper, we will compare different optimization algorithms working principles, and we will also discuss the advantages and limitations of all the optimization techniques.

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