Genetic local search for the TSP: new results

Merz, Peter; Freisleben, Bernd

doi:10.1109/icec.1997.592288

Cited by 167 publications

(84 citation statements)

References 19 publications

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“…The presented results concern genetic local search (Merz and Freisleben, 1997). There are more recent results (Choi et al, 2003), however, the results are not very clear and do not show any special improvement, therefore they are not considered.…”

Section: Traveling Salesman Problemmentioning

confidence: 99%

A Logistic Process Scheduling Problem: Genetic Algorithms or Ant Colony Optimization?

Silva

Sousa

Runkler

et al. 2005

IFAC Proceedings Volumes

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This paper compares the optimization of a logistic scheduling problem using two different optimization techniques; the genetic algorithms and the ant colony optimization. The comparison is preceded by a literature review that summarizes the available comparison results for different benchmark problems and tries to generalize the differences between the techniques. The simulation results for the logistic problem confirm the conclusions of the literature survey: both methods perform equally well, but in general the genetic algorithms are faster. However, the ant colonies give more information about the solution, which is advantage in some applications.

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Section: Traveling Salesman Problemmentioning

confidence: 99%

A Logistic Process Scheduling Problem: Genetic Algorithms or Ant Colony Optimization?

Silva

Sousa

Runkler

et al. 2005

IFAC Proceedings Volumes

View full text Add to dashboard Cite

show abstract

“…Considering of the constraints, formula (5) guarantees that the number of parcels of post vehicle must not exceed its maximum capacity; formula (6) presents the change in the number of parcels when the post vehicle visited a branch; formula (7) guarantees that all post vehicles return to the post office X1 within the time window.…”

Section: The Balanced Msp Modelmentioning

confidence: 99%

“…These algorithms can be divided into two categories [1], One category has local heuristic search algorithms, such as k-opt algorithm, LK algorithm, and LKH algorithm. The other category has artificial intelligence algorithms, such as ant colony algorithms [2,3], genetic algorithms [4][5][6][7], neural network algorithms [8].…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization Algorithm for Multisalesman Problem with Time and Capacity Constraints

Pang¹,

Tan²,

Dai³

et al. 2013

Appl. Math. Inf. Sci.

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Classic multiple traveling salesman problem (MTSP) requires to find the k closed circular paths which minimize the sum of the path lengths, and each vertex is visited only once by a salesman. This paper presents an optimized model for the balanced Multiple-salesman Problem with time and capacity constraints, it requires that a salesman visits each vertex at least once and returns to the starting vertex within given time. The balanced MSP is more widely used than MTSP. We describe a particle swarm optimization algorithm for balanced MSP and explain its simple application in the Post Office Scheduling Problem. We demonstrate the effectiveness of the model and algorithm through a case study.

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“…Recombination operator In order to design an appropriate recombination operator we use an adaptation pattern based on previous works of Merz and Freisleben [5], [6], Jaszkiewicz and Kominek [3], and Kubiak [4]:…”

Section: 3mentioning

confidence: 99%

Distance Preserving Recombination Operator for Earth Observation Satellites Operations Scheduling

Jaszkiewicz

2007

J Math Model Algor

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Genetic local search for the TSP: new results

Cited by 167 publications

References 19 publications

A Logistic Process Scheduling Problem: Genetic Algorithms or Ant Colony Optimization?

A Logistic Process Scheduling Problem: Genetic Algorithms or Ant Colony Optimization?

Particle Swarm Optimization Algorithm for Multisalesman Problem with Time and Capacity Constraints

Distance Preserving Recombination Operator for Earth Observation Satellites Operations Scheduling

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