SOM-Guided Evolutionary Search for Solving MinMax Multiple-TSP

Lupoaie, Vlad-Ioan; Chili, Ivona-Alexandra; Breaban, Mihaela; Raschip, Madalina

doi:10.1109/cec.2019.8790276

Cited by 10 publications

(4 citation statements)

References 18 publications

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“…The baseline algorithms are: (1) exact solver CPLEX, (2) LKH3 whose stopping criteria set to be the known best solutions, (3) OR-Tools, (4) population-based meta-heuristics Self-organization map (SOM), Ant-colony optimization (ACO), Evolutionary algorithm (EA) [25], and (5) the two-phase heuristics (NI and RI). From Table 1, we can observe that, at small-to-medium scale, OR-Tools produces near-optimal solutions, followed by ScheduleNet and ACO.…”

Section: Methodsmentioning

confidence: 99%

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ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning

Park,

Bakhtiyar,

Park

2021

Preprint

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We propose ScheduleNet, a RL-based real-time scheduler, that can solve various types of multi-agent scheduling problems. We formulate these problems as a semi-MDP with episodic reward (makespan) and learn ScheduleNet, a decentralized decision-making policy that can effectively coordinate multiple agents to complete tasks. The decision making procedure of ScheduleNet includes: (1) representing the state of a scheduling problem with the agent-task graph, (2) extracting node embeddings for agent and tasks nodes, the important relational information among agents and tasks, by employing the type-aware graph attention (TGA), and (3) computing the assignment probability with the computed node embeddings. We validate the effectiveness of ScheduleNet as a general learning-based scheduler for solving various types of multi-agent scheduling tasks, including multiple salesman traveling problem (mTSP) and job shop scheduling problem (JSP).Preprint. Under review.

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Section: Methodsmentioning

confidence: 99%

“…Otherwise, the known-best upper bound of CPLEX results are reported[27]. Population based metaheuristics results are reproduced from Lupoaie et al[25].…”

mentioning

confidence: 99%

ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning

Park,

Bakhtiyar,

Park

2021

Preprint

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“…However, the authors in [25] figure out there is a limitation on the diversity of the genetic operation of the two-part chromosome, and they proposed a new crossover approach named TCX to overcome this advantage. In addition, multi-chromosome is another alternative representation technique, as proposed and used in [26,27]. Both the techniques suffer from complex genetic operations because the chromosome must be split first so that the operations can be executed.…”

Section: Related Workmentioning

confidence: 99%

Research on Multi-Objective Multi-Robot Task Allocation by Lin–Kernighan–Helsgaun Guided Evolutionary Algorithms

Zhang

Jiang

2022

Mathematics

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Multi-robot task allocation (MRTA) and route planning are crucial for a large-scale multi-robot system. In this paper, the problem is formulated to minimize the total energy consumption and overall task completion time simultaneously, with some constraints taken into consideration. To represent a solution, a novel one-chromosome representation technique is proposed, which eases the consequent genetic operations and the construction of the cost matrix. Lin–Kernighan–Helsgaun (LKH), a highly efficient sub-tour planner, is employed to generate prophet generation beforehand as well as guide the evolutionary direction during the proceeding of multi-objective evolutionary algorithms, aiming to promote convergence of the Pareto front. Numerical experiments on the benchmark show the LKH guidance mechanism is effective for two famous multi-objective evolutionary algorithms, namely multi-objective evolutionary algorithm based on decomposition (MOEA/D) and non-dominated sorting genetic algorithm (NSGA), of which LKH-guided NSGA exhibits the best performance on three predefined indicators, namely C-metric, HV, and Spacing, respectively. The generalization experiment on a multiple depots MRTA problem with constraints further demonstrates the effectiveness of the proposed approach for practical decision making.

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“…OR-Tools (Google 2012) tends to seek approximated optimal solutions on large-scale mTSP with relatively fast speed. (Lupoaie et al 2019) proposed an algorithm SOM based on metaheuristic algorithms and is combined with self-organizing map. Most of these conventional methods either compute expensively or perform terribly when solving MinMax mTSP, especially when the scale of problems is enormous.…”

Section: Introductionmentioning

confidence: 99%

SplitNet: A Reinforcement Learning Based Sequence Splitting Method for the MinMax Multiple Travelling Salesman Problem

Liang

Cao

et al. 2023

AAAI

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MinMax Multiple Travelling Salesman Problem (mTSP) is an important class of combinatorial optimization problems with many practical applications, of which the goal is to minimize the longest tour of all vehicles. Due to its high computational complexity, existing methods for solving this problem cannot obtain a solution of satisfactory quality with fast speed, especially when the scale of the problem is large. In this paper, we propose a learning-based method named SplitNet to transform the single TSP solutions into the MinMax mTSP solutions of the same instances. Specifically, we generate single TSP solution sequences and split them into mTSP subsequences using an attention-based model trained by reinforcement learning. We also design a decision region for the splitting policy, which significantly reduces the policy action space on instances of various scales and thus improves the generalization ability of SplitNet. The experimental results show that SplitNet generalizes well and outperforms existing learning-based baselines and Google OR-Tools on widely-used random datasets of different scales and public datasets with fast solving speed.

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SOM-Guided Evolutionary Search for Solving MinMax Multiple-TSP

Cited by 10 publications

References 18 publications

ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning

ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning

Research on Multi-Objective Multi-Robot Task Allocation by Lin–Kernighan–Helsgaun Guided Evolutionary Algorithms

SplitNet: A Reinforcement Learning Based Sequence Splitting Method for the MinMax Multiple Travelling Salesman Problem

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