Hybrid search with neighborhood reduction for the multiple traveling salesman problem

He, Pengfei; Hao, Jin‐Kao

doi:10.1016/j.cor.2022.105726

Cited by 23 publications

(7 citation statements)

References 42 publications

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“…It adopts Metropolis acceptance criteria and uses a group of parameters called cooling schedule to control the algorithm process so that the algorithm can give an approximate optimal solution in polynomial time. Its main feature is that it can jump out of the local pole region, so it can find the global optimum or approximate global optimum regardless of the selection of the initial point [9].…”

Section: Optimal Design Of Multi-wire Mesh Channel Network Based On R...mentioning

confidence: 99%

Optimization of holes routing in hydraulic manifold blocks based on simulated annealing algorithm

Jia

2023

J. Phys.: Conf. Ser.

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The design of the hydraulic manifold block is time-consuming, laborious, and prone to make mistakes due to the vertical intersection and dense complexity of its internal oil passage network. The mathematical optimization model of this problem is given based on the comprehensive analysis of the structural characteristics and design rules of the internal orifices of the hydraulic manifold block. Using the integrated circuit layout algorithm for reference, an optimization design method of a multi-wire mesh channel network based on routing sequence optimization is proposed and verified by an example. The research results show that the optimization of the connection order between multiple wire networks at both ends and the routing order between multiple wire networks in a single-wire network can not only achieve the optimal design of the connectivity of the single-wire network but also ensure the optimal connectivity result of the entire hole network. At the same time, as the bottom core algorithm of the integrated block layout optimization design, it can provide a powerful technical guarantee for improving the integrated block design level, quality, and automation.

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Section: Optimal Design Of Multi-wire Mesh Channel Network Based On R...mentioning

confidence: 99%

Optimization of holes routing in hydraulic manifold blocks based on simulated annealing algorithm

Jia

2023

J. Phys.: Conf. Ser.

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“…The trip planning problem (TPP) in this paper is the problem that finds the optimal route to visit a series of point-ofinterests (POIs) and hotels over multiple days [1]- [4]. For example, Sylejmani et al [1] presented a method that solves the trip planning problem using a heuristic algorithm based on tabu search; Saeki et al [2] presented a method for planning a multi-objective trip using antcolony optimization; Fournier et al [3] showed a method that solves the bus passenger trip planning problem using an A*-guided and Pareto dominance-based heuristic; Garcia et al [4] presented two different methods to solving the time-dependent team orienteering problem with time windows; Shuai et al [5] presented a method that solves multiple traveling salesman problems by applying an NSGA-II framework; He et al [6] presented a hybrid method based on tabu search and intratour optimization to solve the multiple traveling salesman problems.…”

Section: A Trip Planning Problemmentioning

confidence: 99%

Trip Planning Based on subQUBO Annealing

Noguchi,

Fukada,

Bao

et al. 2023

IEEE Access

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The trip planning problem (TPP) can be formulated as a combinatorial optimization problem that searches for the best route to visit a series of landmarks and hotels. Meanwhile, Ising machines have attracted attention due to their efficiency in solving combinatorial optimization problems. The Ising machines solve the combinatorial optimization problems by transforming the problems into quadratic unconstrained binary optimization (QUBO) models. However, the possible input QUBO size of current Ising machines is quite limited. Thus, it is hard to directly embed a large-scale TPP onto the current Ising machines. In this paper, we propose a novel subQUBO annealing method based on the combined variable selection method to solve the TPP. The proposed method finds a quasi-optimal solution to a large problem by repeatedly partitioning the original QUBO model into small subQUBOs that can be embedded onto the Ising machine. Specifically, to construct a subQUBO, we select variables from the original QUBO model, which have small deviation values. Further, we select variables randomly from the original QUBO model, so as not to fall into the local optimum. We have conducted an evaluation experiment using Ising machines on TPP and confirmed that the proposed method outperforms the state-of-the-art methods in terms of POI satisfaction and POI cost.

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“…For example, mTSP with motion constraints are addressed in [ 24 , 44 ]; fuel constraints are addressed in [ 45 ]; capacity and other resource constraints are addressed in [ 46 , 47 ]. Many recent methods have focused on the application of metaheuristics like genetic algorithms (GA) [ 48 , 49 ], simulated annealing (SA) [ 50 ], memetic search [ 51 ], tabu search [ 52 ], swarm optimization [ 53 ], and other methods [ 54 , 55 ] to solve the mTSP problem for the single-depot and multi-depot case.…”

Section: Literature Reviewmentioning

confidence: 99%

Heuristics and Learning Models for Dubins MinMax Traveling Salesman Problem

Nayak

Rathinam

2023

Sensors

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This paper addresses a MinMax variant of the Dubins multiple traveling salesman problem (mTSP). This routing problem arises naturally in mission planning applications involving fixed-wing unmanned vehicles and ground robots. We first formulate the routing problem, referred to as the one-in-a-set Dubins mTSP problem (MD-GmTSP), as a mixed-integer linear program (MILP). We then develop heuristic-based search methods for the MD-GmTSP using tour construction algorithms to generate initial feasible solutions relatively fast and then improve on these solutions using variants of the variable neighborhood search (VNS) metaheuristic. Finally, we also explore a graph neural network to implicitly learn policies for the MD-GmTSP using a learning-based approach; specifically, we employ an S-sample batch reinforcement learning method on a shared graph neural network architecture and distributed policy networks to solve the MD-GMTSP. All the proposed algorithms are implemented on modified TSPLIB instances, and the performance of all the proposed algorithms is corroborated. The results show that learning based approaches work well for smaller sized instances, while the VNS based heuristics find the best solutions for larger instances.

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Hybrid search with neighborhood reduction for the multiple traveling salesman problem

Cited by 23 publications

References 42 publications

Optimization of holes routing in hydraulic manifold blocks based on simulated annealing algorithm

Optimization of holes routing in hydraulic manifold blocks based on simulated annealing algorithm

Trip Planning Based on subQUBO Annealing

Heuristics and Learning Models for Dubins MinMax Traveling Salesman Problem

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