Solving TSP with Shuffled Frog-Leaping Algorithm

Luo, Xuehui; Yang, Yanchao; Li, Xia

doi:10.1109/isda.2008.346

Cited by 51 publications

(23 citation statements)

References 7 publications

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“…Although in the literature [25], the shuffled frog-leaping algorithm with local search can guarantee the feasibility of the updated solution, the step size is still selected randomly. Therefore, on the basis of the literature [25], this paper designed the local search strategy by introducing the adjustment factor and adjustment order.…”

Section: Improvement Strategiesmentioning

confidence: 99%

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An Improved Shuffled Frog-Leaping Algorithm for Flexible Job Shop Scheduling Problem

Kong

Wang

et al. 2015

Algorithms

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Abstract:The flexible job shop scheduling problem is a well-known combinatorial optimization problem. This paper proposes an improved shuffled frog-leaping algorithm to solve the flexible job shop scheduling problem. The algorithm possesses an adjustment sequence to design the strategy of local searching and an extremal optimization in information exchange. The computational result shows that the proposed algorithm has a powerful search capability in solving the flexible job shop scheduling problem compared with other heuristic algorithms, such as the genetic algorithm, tabu search and ant colony optimization. Moreover, the results also show that the improved strategies could improve the performance of the algorithm effectively.

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Section: Improvement Strategiesmentioning

confidence: 99%

“…Therefore, on the basis of the literature [25], this paper designed the local search strategy by introducing the adjustment factor and adjustment order.…”

Section: Improvement Strategiesmentioning

confidence: 99%

An Improved Shuffled Frog-Leaping Algorithm for Flexible Job Shop Scheduling Problem

Kong

Wang

et al. 2015

Algorithms

View full text Add to dashboard Cite

show abstract

“…The SCP has been solved using complete techniques and different metaheuristics [20,7,6]. SFLA has been applied to multi-mode resource-constrained project scheduling problem [21], bridge deck repairs [9], water resource distribution [11], unit commitment problem [8], traveling salesman problem (TSP) [17] and job-shop scheduling arrangement [19].…”

Section: Introductionmentioning

confidence: 99%

Binarization Methods for Shuffled Frog Leaping Algorithms That Solve Set Covering Problems

Crawford

Soto

Peña

et al. 2015

Software Engineering in Intelligent Systems

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“…Task scheduling researches how to map these tasks to appropriate virtual machines. Currently, most task scheduling strategies are solved using intelligent algorithm [6,7,8,9] such as, genetic algorithm, ant colony algorithm and so on. However, these methods have some problems, such as lower ability to search the global optimal solution, poorer convergence and so on.…”

Section: Introductionmentioning

confidence: 99%

A Task Scheduling Algorithm Based on Potential Games in Cloud Computing Environment

Zheng¹,

Xiao²

2015

IJFGCN

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Efficient task scheduling mechanism in cloud computing can improve the resource utilization and enhance the overall performance of the cloud computing environment. However, the existing strategies based on static task requirements are difficult to guarantee the stability of the system, while dynamic mechanisms have higher complexity. In this paper, a new task scheduling algorithm based on potential game is proposed. We prove that the potential game will reach Nash equilibrium quickly. Also, the system load balancing level is adaptive with the number of users' task changing. The experimental results show the priority of the proposed algorithm.

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Solving TSP with Shuffled Frog-Leaping Algorithm

Cited by 51 publications

References 7 publications

An Improved Shuffled Frog-Leaping Algorithm for Flexible Job Shop Scheduling Problem

An Improved Shuffled Frog-Leaping Algorithm for Flexible Job Shop Scheduling Problem

Binarization Methods for Shuffled Frog Leaping Algorithms That Solve Set Covering Problems

A Task Scheduling Algorithm Based on Potential Games in Cloud Computing Environment

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