scholarly journals Heterogeneous Task Allocation and Sequencing via Decentralized Large Neighborhood Search

2017 ◽  
Vol 05 (02) ◽  
pp. 79-95 ◽  
Author(s):  
Armin Sadeghi ◽  
Stephen L. Smith
Keyword(s):  
Traveling Salesman Problem ◽  
Task Allocation ◽  
Branch And Cut ◽  
Traveling Salesman ◽  
Neighborhood Search ◽  
Large Neighborhood Search ◽  
Solution Quality ◽  
Solution Approach ◽  
Large Neighborhood ◽  
Generalized Traveling Salesman Problem

This paper focuses on decentralized task allocation and sequencing for multiple heterogeneous robots. Each task is defined as visiting a point in a subset of the robot configuration space — this definition captures a variety of tasks including inspection and servicing. The robots are heterogeneous in that they may be subject to different differential motion constraints. Our approach is to transform the problem into a multi-vehicle generalized traveling salesman problem (GTSP). To solve the GTSP, we propose a novel decentralized implementation of large-neighborhood search (LNS). Our solution approach leverages the GTSP insertion methods proposed in Fischetti et al. [A branch-and-cut algorithm for the symmetric generalized traveling salesman problem, Oper. Res. 45(3) (1997) 378–394]. to repeatedly remove and reinsert tasks from each robot path. Decentralization is achieved using combinatorial-auctions between the robots on tasks removed from robot’s path. We provide bounds on the length of the dynamically feasible robot paths produced by the insertion methods. We also show that the number of bids in each combinatorial auction, a crucial factor in the runtime, scales linearly with the number of tasks. Finally, we present extensive benchmarking results to characterize both solution quality and runtime, which show improvements over existing decentralized task allocation methods.

Solving the Laser Cutting Path Problem Using Population-Based Simulated Annealing with Adaptive Large Neighborhood Search

2020 ◽  
Vol 833 ◽  
pp. 29-34 ◽  
Author(s):  
Makbul Hajad ◽  
Viboon Tangwarodomnukun ◽  
Chaiya Dumkum ◽  
Chorkaew Jaturanonda
Keyword(s):  
Simulated Annealing ◽  
Laser Cutting ◽  
Computing Time ◽  
Population Based ◽  
Neighborhood Search ◽  
Large Neighborhood Search ◽  
Adaptive Large Neighborhood Search ◽  
Large Neighborhood ◽  
Generalized Traveling Salesman Problem ◽  
Cutting Path

This paper presents an alternative algorithm for solving the laser cutting path problem which was modeled as Generalized Traveling Salesman Problem (GTSP). The objective is to minimize the traveling distance of laser cutting of all profiles in a given layout, where a laser beam makes a single visit and then does the complete cut of individual profile in an optimum sequence. This study proposed a hybrid method combining population-based simulated annealing (SA) with an adaptive large neighborhood search (ALNS) algorithm to solve the cutting path problem. Recombination procedures were executed alternately using swap, reversion, insertion and removal-insertion through a fitness proportionate selection mechanism. In order to reduce the computing time and maintain the solution quality, the 35% proportion of population were executed in each iteration using the cultural algorithm selection method. The results revealed that the algorithm can solve several ranges of problem size with an acceptable percentage of error compared to the best known solution.

scholarly journals An Enhanced Genetic Algorithm for the Generalized Traveling Salesman Problem

2017 ◽  
Vol 7 (6) ◽  
pp. 2260-2265 ◽  
Author(s):  
H. Jafarzadeh ◽  
N. Moradinasab ◽  
M. Elyasi
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Nearest Neighbor Search ◽  
Traveling Salesman ◽  
Computational Time ◽  
Test Problems ◽  
Solution Quality ◽  
Generalized Traveling Salesman Problem

The generalized traveling salesman problem (GTSP) deals with finding the minimum-cost tour in a clustered set of cities. In this problem, the traveler is interested in finding the best path that goes through all clusters. As this problem is NP-hard, implementing a metaheuristic algorithm to solve the large scale problems is inevitable. The performance of these algorithms can be intensively promoted by other heuristic algorithms. In this study, a search method is developed that improves the quality of the solutions and competition time considerably in comparison with Genetic Algorithm. In the proposed algorithm, the genetic algorithms with the Nearest Neighbor Search (NNS) are combined and a heuristic mutation operator is applied. According to the experimental results on a set of standard test problems with symmetric distances, the proposed algorithm finds the best solutions in most cases with the least computational time. The proposed algorithm is highly competitive with the published until now algorithms in both solution quality and running time.

A Branch-and-Cut Algorithm for the Symmetric Generalized Traveling Salesman Problem

1997 ◽  
Vol 45 (3) ◽  
pp. 378-394 ◽  
Author(s):  
Matteo Fischetti ◽  
Juan José Salazar González ◽  
Paolo Toth
Keyword(s):  
Traveling Salesman Problem ◽  
Branch And Cut ◽  
Traveling Salesman ◽  
Generalized Traveling Salesman Problem

scholarly journals Anytime Multi-Agent Path Finding via Large Neighborhood Search

2021 ◽  
Author(s):  
Jiaoyang Li ◽  
Zhe Chen ◽  
Daniel Harabor ◽  
Peter J. Stuckey ◽  
Sven Koenig
Keyword(s):  
Initial Solution ◽  
Neighborhood Search ◽  
Path Finding ◽  
Large Neighborhood Search ◽  
Challenging Problem ◽  
Anytime Algorithms ◽  
Solution Quality ◽  
Large Neighborhood ◽  
Multi Agent ◽  
Suboptimal Algorithms

Multi-Agent Path Finding (MAPF) is the challenging problem of computing collision-free paths for multiple agents. Algorithms for solving MAPF can be categorized on a spectrum. At one end are (bounded-sub)optimal algorithms that can find high-quality solutions for small problems. At the other end are unbounded-suboptimal algorithms that can solve large problems but usually find low-quality solutions. In this paper, we consider a third approach that combines the best of both worlds: anytime algorithms that quickly find an initial solution using efficient MAPF algorithms from the literature, even for large problems, and that subsequently improve the solution quality to near-optimal as time progresses by replanning subgroups of agents using Large Neighborhood Search. We compare our algorithm MAPF-LNS against a range of existing work and report significant gains in scalability, runtime to the initial solution, and speed of improving the solution.

scholarly journals An Improved Whale Optimization Algorithm for the Traveling Salesman Problem

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 48
Author(s):  
Jin Zhang ◽  
Li Hong ◽  
Qing Liu
Keyword(s):  
Traveling Salesman Problem ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Relevant Literature ◽  
Population Diversity ◽  
Traveling Salesman ◽  
Whale Optimization Algorithm ◽  
Neighborhood Search ◽  
Whale Optimization ◽  
The Traveling Salesman Problem

The whale optimization algorithm is a new type of swarm intelligence bionic optimization algorithm, which has achieved good optimization results in solving continuous optimization problems. However, it has less application in discrete optimization problems. A variable neighborhood discrete whale optimization algorithm for the traveling salesman problem (TSP) is studied in this paper. The discrete code is designed first, and then the adaptive weight, Gaussian disturbance, and variable neighborhood search strategy are introduced, so that the population diversity and the global search ability of the algorithm are improved. The proposed algorithm is tested by 12 classic problems of the Traveling Salesman Problem Library (TSPLIB). Experiment results show that the proposed algorithm has better optimization performance and higher efficiency compared with other popular algorithms and relevant literature.

Sign in / Sign up

Export Citation Format

Share Document