A New Approach to Solving Dynamic Traveling Salesman Problems

Li, Changhe; Yang, Ming; Kang, Lishan

doi:10.1007/11903697_31

Cited by 52 publications

(21 citation statements)

References 4 publications

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“…Since then, several variations of DTSPs were introduced, where the set of nodes N [1,9,10,12,13,25] and/or the cost from the set of arcs A [6,17,19,21,25] cause the weight matrix W(t) to change during the optimization process. However, there is still no any unified benchmark problem for DTSPs, which makes the comparison with algorithms from the literature a very challenging task.…”

Section: Dtsp Benchmark Generatorsmentioning

confidence: 99%

Pre-scheduled Colony Size Variation in Dynamic Environments

Mavrovouniotis

Ιωάννου

Yang

2017

Applications of Evolutionary Computation

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Abstract. The performance of the MAX -MIN ant system (MMAS) in dynamic optimization problems (DOPs) is sensitive to the colony size. In particular, a large colony size may waste computational resources whereas a small colony size may restrict the searching capabilities of the algorithm. There is a trade off in the behaviour of the algorithm between the early and later stages of the optimization process. A smaller colony size leads to better performance on shorter runs whereas a larger colony size leads to better performance on longer runs. In this paper, pre-scheduling of varying the colony size of MMAS is investigated in dynamic environments.

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Section: Dtsp Benchmark Generatorsmentioning

confidence: 99%

Pre-scheduled Colony Size Variation in Dynamic Environments

Mavrovouniotis

Ιωάννου

Yang

2017

Applications of Evolutionary Computation

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“…The list of the state capitals is given in Table 2 with the abbreviation. In the case study, unique identification numbers (ranging from one to seventeen) are used to represent each state capital in alphabetical order as follows: (1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17), these numbers are arranged in different order randomly to generate several tours. The tours that were generated are then put together and termed the "initial population."…”

Section: Proposed Hybrid Modelmentioning

confidence: 99%

“…The authors consider path representation method which simply lists the label of nodes. For example, let "1, 2, 3, 4, 5, 6, 7" be the labels of nodes in a seven node instance, then a tour "1→ 4→ 3→ 5→ 7→ 6→ 2→1" may be represented as (1,4,3,5,7,6,2). …”

Section: Genetic Representation and Codingmentioning

confidence: 99%

“…TSP research finds its application in different areas such as transportation problem, logistic distribution problem, delivery order problem, minimum spanning tree (MST) for communication network, electricity network or water pipelining, and machine flow shop scheduling [1]. Another variant of TSP is the Dynamic traveling salesman problem (DTSP), which is a modified version of the travelling salesman [2,3]. Here, a salesman embarks on a trip starting from a city and after a complete round comes back to his starting point whilst passing each city once.…”

Section: Introductionmentioning

confidence: 99%

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Towards Solving Travelling Salesperson Problem using Hybrid of Genetic Algorithm and Lin-Kernighan Algorithm: A Comparative Evaluation with Neural Network Model

Oluwadare¹,

Ogunsanmi²,

Nwaiwu³

2017

IJCA

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Travelling salesperson problem involves the sales person who intends to find the minimum or shortest round trip that passes through a finite set of cities, exactly once at minimum cost. This problem belongs to the class of optimization problems which is described as non-deterministic polynomial hard, that is, it cannot be solved in exact polynomial time. Several approaches have been employed in solving the problem, but empirical results has shown that these approaches needs more optimization in terms of run time and quality of getting the optimum solution. Genetic algorithm combined with another local search algorithm shows more efficient result could be obtained. In this paper, hybridization of genetic algorithm with a local search algorithm called Lin-Kernighan algorithm is employed to provide efficient solution. A case study of finding the optimal solution for a tour of state capitals in Southern Nigeria is carried out. The model is implemented on Intel Celeron 2GHz, 1GB RAM machine with JAVA programming language and Wamp sever. The performance of the proposed hybrid genetic algorithm-based model is compared with Artificial Neural Network. The results showed that the proposed model performs better than neural network in terms of run-time and minimal tour distance.

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“…Zhou [22] presented three types of operators based on some prior knowledge of the current environment to enhance the search ability of EAs for DTSPs. Li et al [13] further improved the inver-over operator based on a gene pool, which stores a set of most promising gene segments by applying some heuristic rules. These approaches indicate again that adopting effective diversity maintaining or enhancing schemes and memory-based methods may provide EAs with a competitive performance to solve DTSPs.…”

Section: Relevant Work On the Dynamic Tspmentioning

confidence: 99%

An Immune System Based Genetic Algorithm Using Permutation-Based Dualism for Dynamic Traveling Salesman Problems

Liu

Wang

Yang

2009

Lecture Notes in Computer Science

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Abstract. In recent years, optimization in dynamic environments has attracted a growing interest from the genetic algorithm community due to the importance and practicability in real world applications. This paper proposes a new genetic algorithm, based on the inspiration from biological immune systems, to address dynamic traveling salesman problems. Within the proposed algorithm, a permutation-based dualism is introduced in the course of clone process to promote the population diversity. In addition, a memory-based vaccination scheme is presented to further improve its tracking ability in dynamic environments. The experimental results show that the proposed diversification and memory enhancement methods can greatly improve the adaptability of genetic algorithms for dynamic traveling salesman problems.

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A New Approach to Solving Dynamic Traveling Salesman Problems

Cited by 52 publications

References 4 publications

Pre-scheduled Colony Size Variation in Dynamic Environments

Pre-scheduled Colony Size Variation in Dynamic Environments

Towards Solving Travelling Salesperson Problem using Hybrid of Genetic Algorithm and Lin-Kernighan Algorithm: A Comparative Evaluation with Neural Network Model

An Immune System Based Genetic Algorithm Using Permutation-Based Dualism for Dynamic Traveling Salesman Problems

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