Divide and conquer evolutionary TSP solution for vehicle path planning

Meuth, Ryan; Wunsch, Donald C.

doi:10.1109/cec.2008.4630868

Cited by 10 publications

(12 citation statements)

References 9 publications

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“…Here the schema takes the form of the ordering of points in a tour. The addition of a clustering method to divide and conquer the TSP has been shown to greatly accelerate the solution of the TSP [40]. With this addition, the overall schema for the optimizer consists of the combination of cluster templates, tour point ordering, and the locations of points.…”

Section: Traveling Salesman Problemmentioning

confidence: 99%

“…(ix) Large-scale quadratic assignment problem [63] (x) Evolutionary Lin-Kernighan for traveling salesman problem [40] (xi) Dynamic optimization problem [68] and many others Second Generation Global search with multiple local optimizers. Memetic information (Choice of optimizer) Passed to offspring (Lamarckian evolution) (i) Nurse rostering problem [9] (ii) Hyper-heuristic MA [15,22] (iii) Structure prediction and structure comparison of proteins [29] (iv) Meta-Lamarckian MA [54] (v) Multimeme MA [31] (vi) Adaptive multi-meme MA [53] (vii) Multimeme algorithm for designing HIV multidrug therapies [10,45] (viii) Agent-based memetic algorithm [17,67] (ix) Diffusion memetic algorithm [48] and several others Third Generation Global search with multiple local optimizers.…”

Section: Brain Inspired Memetic Computingmentioning

confidence: 99%

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A proposition on memes and meta-memes in computing for higher-order learning

et al. 2009

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In computational intelligence, the term 'memetic algorithm' has come to be associated with the algorithmic pairing of a global search method with a local search method. In a sociological context, a 'meme' has been loosely defined as a unit of cultural information, the social analog of genes for individuals. Both of these definitions are inadequate, as 'memetic algorithm' is too specific, and ultimately a misnomer, as much as a 'meme' is defined too generally to be of scientific use. In this paper, we extend the notion of memes from a computational viewpoint and explore the purpose, definitions, design guidelines and architecture for effective memetic computing. Utilizing two conceptual case studies, we illustrate the power of high-order meme-based learning. With applications ranging from cognitive science to machine learning, memetic computing has the potential to provide much-needed stimulation to the field of computational intelligence by providing a framework for higher order learning.

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Section: Traveling Salesman Problemmentioning

confidence: 99%

Section: Brain Inspired Memetic Computingmentioning

confidence: 99%

A proposition on memes and meta-memes in computing for higher-order learning

et al. 2009

Self Cite

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“…Optimal methods mainly include graph theoretical and other mathematical methods [10], while nonoptimal methods include intelligent search methods [3] and heuristic search methods [6]. Several graph theoretical methods, such as branch and bound graph search (BBGS), solution methods based on the minimum weight Hamiltonian path problem (MWHPP), terrain coverage (TC), and the traveling salesman problem (TSP), have been modified and applied to sequence planning [9,12]. In TSP, the robot is allowed to visit each point once and only once, whereas in multiple target search, each observation point must be visited at least once.…”

Section: Introductionmentioning

confidence: 99%

An optimal path planning strategy for multiple target search by a mobile robot

Zhang¹,

Liu²,

Lü³

et al. 2013

IEEJ Transactions Elec Engng

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Although there is an obvious relationship between the Chinese Postman Problem (CPP) and the path‐planning problem for multiple target‐search tasks by a robot, no attempts have been made so far to connect the two. A novel map transformation framework (MTF) is proposed in this paper. With MTF, a feature map or a topological map can be converted into a standard topological map on which many graph‐search algorithms suitable for CPP can be employed to carry out path planning for multiple target search. Theoretical analysis proves that the route generated by this approach is optimal. Both simulation results and experiments indicate that the MTF‐based algorithm is better than several other target‐search algorithms. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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“…Finally, Meuth and Wunsch (2001) presented an approach that combines cellular decomposition, a solution to the traveling salesman problem, and genetic algorithms. It used the divide and conquer methods from cellular decomposition first to subdivide the field.…”

Section: Prior Researchmentioning

confidence: 99%

“…(1cos(θ)). Meuth and Wunsch (2001) defined the vehicle agility as its ability to execute turns without incurring extra time. So the above formula basically calculates the time to make a straight pass plus the time in the turns.…”

Section: Cost Functions In Prior Workmentioning

confidence: 99%

Complete coverage path planning in an agricultural environment

Driscoll

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Divide and conquer evolutionary TSP solution for vehicle path planning

Cited by 10 publications

References 9 publications

A proposition on memes and meta-memes in computing for higher-order learning

A proposition on memes and meta-memes in computing for higher-order learning

An optimal path planning strategy for multiple target search by a mobile robot

Complete coverage path planning in an agricultural environment

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