Solving the Min-Max Clustered Traveling Salesmen Problem Based on Genetic Algorithm

Bao, Xiaoguang; Wang, Guojun; Xu, Lei; Wang, Zhaocai

doi:10.3390/biomimetics8020238

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…To mimic the genetic processes observed in nature, an adaptive and globally oriented optimization search algorithm, known as the Genetic Algorithm, has been introduced [26]. This algorithm possessed robust global search capabilities, coupled with the ability to conduct localized searches.…”

Section: Three-way K-means Algorithm Based On Genetic Algorithm 261 G...mentioning

confidence: 99%

Three-Way k-Means Model: Dynamic Optimal Sensor Placement for Efficient Environment Monitoring in Pig House

Li,

et al. 2024

Animals

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Sensors were of paramount importance in the context of poultry and livestock farming, serving as essential tools for monitoring a variety of production management parameters. The effective surveillance and optimal control of the swine facility environment critically depend on the implementation of a robust strategy for situating the optimal number of sensors in precisely the right locations. This study presents a dynamic sensor placement approach for pigsties using the three-way k-means algorithm. The method involves determining candidate sensor combinations through the application of the k-means algorithm and a re-clustering strategy. The optimal sensor locations were then identified using the Joint Entropy-Based Method (JEBM). This approach adjusts sensor positions based on different seasons (summer and winter) to effectively monitor the overall environment of the pigsty. We employ two clustering models, one based on particle swarm optimization and the other on genetic algorithms, along with a re-clustering strategy to identify candidate sensor combinations. The joint entropy-based method (JEBM) helps select the optimal sensor placement. Fused data from the optimal sensor layout undergo a fuzzy fusion process, reducing errors compared to direct averaging. The results show varying sensor needs across seasons, and dynamic placement enhances pigsty environment monitoring. Our approach reduced the number of sensors from 30 to 5 (in summer) and 6 (in winter). The optimal sensor positions for both seasons were integrated. Comparing the selected sensor layout to the average of all sensor readings representing the overall pigsty environment, the RMSE were 0.227–0.294 and the MAPE were 0.172–0.228, respectively, demonstrating the effectiveness of the sensor layout.

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Section: Three-way K-means Algorithm Based On Genetic Algorithm 261 G...mentioning

confidence: 99%

Three-Way k-Means Model: Dynamic Optimal Sensor Placement for Efficient Environment Monitoring in Pig House

Li,

et al. 2024

Animals

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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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