Fuzzy Clustering Based Genetic Algorithm for the Multi-Depot Polygon Visiting Dubins Multiple Traveling Salesman Problem

Ernest, Nicholas; Cohen, Kelly

doi:10.2514/6.2012-2562

Cited by 11 publications

(11 citation statements)

References 3 publications

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“…However, these routes are not optimized for the PVDTSP. Slightly modified from [5], rather than brute force search along the boundary points of each polygon, a GA determines the optimal contact point for the rout on each polygon by analyzing three polygons at a time. This iterates around the route twice, the first time these values are calculated utilizing the initial solution, and thus the centroids of the polygons, as shown in Figure 5.…”

Section: Development Of the Genetic Fuzzy Methodsmentioning

confidence: 99%

“…The genetic method utilized for the comparison aims to solve the Polygon Visiting Dubins TSP (PVDTSP) [5]. In this study, these polygons have been set to nearly circular.…”

Section: Development Of the Genetic Fuzzy Methodsmentioning

confidence: 99%

“…However, research could be found that applied neural networks, genetic algorithms or other means to TSPs to come up with a useful solution. In research by Snyder [5], a random key genetic algorithm is used to solve for a TSP with defined targets. This research was interesting as it produced solutions within 1% of optimal for "all but the largest problems", and computed quickly.…”

Section: Literature Reviewmentioning

confidence: 99%

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Comparison of Fuzzy Optimization and Genetic Fuzzy Methods in Solving a Modified Traveling Salesman Problem

Mitchell

Ernest

Cohen

2013

AIAA Infotech@Aerospace (I@A) Conference

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There are a growing number of applications demonstrating the effectiveness of emulating human decision making using fuzzy logic. Main research challenges include situational awareness and decision making in an uncertain spatio-temporal environment. In this effort, a MATLAB simulation of a surveillance environment was created that placed targets in random areas on a map, with each target having a circular area imposed around it. In the simulation, a fuzzy robot was to find the shortest path around the environment, where it touched each target area at least once (meaning the areas can be passed through) before returning to its starting position. Through fuzzy optimization of a path produced through a genetic algorithm, this task was completed and it was shown that a shorter path could be found through the fuzzy optimization. The project could then be further optimized through created straight line optimization, and was made more realistic with Dubins paths. Finally, this optimization was compared to a created genetic fuzzy algorithm to determine differences in accuracy and precision between the two solutions.

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Section: Development Of the Genetic Fuzzy Methodsmentioning

confidence: 99%

“…The genetic method utilized for the comparison aims to solve the Polygon Visiting Dubins TSP (PVDTSP) [5]. In this study, these polygons have been set to nearly circular.…”

Section: Development Of the Genetic Fuzzy Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Comparison of Fuzzy Optimization and Genetic Fuzzy Methods in Solving a Modified Traveling Salesman Problem

Mitchell

Ernest

Cohen

2013

AIAA Infotech@Aerospace (I@A) Conference

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“…As mentioned prior, a similar GFLS operates on the UAV clustering level for each depot. Rather than a nearest neighbor algorithm, here a polar clustering FLS [2] provides the initial groupings.…”

Section: B Algorithmsmentioning

confidence: 99%

“…Here the values the GA uses to create its FLS are changed as this clustering algorithm operates in polar coordinates. Additionally, rather than a nearest neighbor algorithm developing an initial solution, a separate, quick FLS [2] groups the targets based on their Θ values around the depot.…”

Section: A Processmentioning

confidence: 99%

UAV Swarm Routing Through Genetic Fuzzy Learning Methods

Ernest

Cohen

Schumacher

2013

AIAA Infotech@Aerospace (I@A) Conference

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Utilizing a combination of heuristic and stochastic techniques, this work approximates solutions for complex variants of the Traveling Salesman Problem. Within this study, we seek to develop a Pareto front of time optimal solutions for a large scale, multi-objective, multi-agent, multi-depot, traveling salesman problem with turning constraints that emulates a UAV operator monitoring a swarm of autonomous vehicles. This research is a culmination of years of investigation and past publications [1, 2, 3] which sought to solve portions of this problem individually. Solving the entire problem, more precisely named the Multi-Objective Min-Max Multi-Depot Polygon Visiting Dubins Multiple Traveling Salesman Problem, follows similar processes of simpler variants. With a focus on military applications, run-time is very critical to the success of this software. Through the cooperation of these algorithms, accurate solutions for large scale problems can be produced within a quick timeframe.

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Area Allocation Algorithm for Multiple UAVs Area Coverage Based on Clustering and Graph Method

2015

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Fuzzy Clustering Based Genetic Algorithm for the Multi-Depot Polygon Visiting Dubins Multiple Traveling Salesman Problem

Cited by 11 publications

References 3 publications

Comparison of Fuzzy Optimization and Genetic Fuzzy Methods in Solving a Modified Traveling Salesman Problem

Comparison of Fuzzy Optimization and Genetic Fuzzy Methods in Solving a Modified Traveling Salesman Problem

UAV Swarm Routing Through Genetic Fuzzy Learning Methods

Area Allocation Algorithm for Multiple UAVs Area Coverage Based on Clustering and Graph Method

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