Random-key cuckoo search for the travelling salesman problem

Ouaarab, Aziz; Ahiod, Belaïd; Yang, Xin-She

doi:10.1007/s00500-014-1322-9

Cited by 63 publications

(33 citation statements)

References 27 publications

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“…However, the salesman using this approach may be stuck at local minima because this approach does not effectively address the diversity of feasible solutions. Recently, two new approaches are proposed by Ouaarab et al (2015), and Yong (2015). Ouaarab et al (2015) used a simplified random-key encoding scheme to pass from a continuous space (real numbers) to a combinatorial space.…”

Section: Literature Review 21 Traveling Salesman Problemmentioning

confidence: 99%

“…Recently, two new approaches are proposed by Ouaarab et al (2015), and Yong (2015). Ouaarab et al (2015) used a simplified random-key encoding scheme to pass from a continuous space (real numbers) to a combinatorial space. They also considered the displacement of a solution in both spaces using Lévy flights.…”

Section: Literature Review 21 Traveling Salesman Problemmentioning

confidence: 99%

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Imperial competitive algorithm with policy learning for the traveling salesman problem

Chen

Chang

2015

Soft Comput

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The traveling salesman problem (TSP) is one of the most studied combinatorial optimization problems. In this paper, we present the new idea of combining the imperial competitive algorithm with a policy-learning function for solving the TSP problems. All offspring of each country are defined as representing feasible solutions for the TSP. All countries can grow increasingly strong by learning the effective policies of strong countries. Weak countries will generate increasingly excellent offspring by learning the policies of strong countries while retaining the characteristics of their own country. Imitating these policies will enable the weak countries to produce improved offspring; the solutions generated will, therefore, acquire a favorable scheme while maintaining diversity. Finally, experimental results for TSP instances from the TSP library have shown that our proposed algorithm can determine the salesman's tour with more effective performance levels than other known methods.

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Section: Literature Review 21 Traveling Salesman Problemmentioning

confidence: 99%

Section: Literature Review 21 Traveling Salesman Problemmentioning

confidence: 99%

Imperial competitive algorithm with policy learning for the traveling salesman problem

Chen

Chang

2015

Soft Comput

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“…In fact, it is observed in the simulations that CS is indeed scale-free and have a fractal-like search structure. Consequently, CS can be very effective for nonlinear optimization problems and multiobjective optimization [17,36,30,58,64].…”

Section: Algorithms Based On Swarm Intelligencementioning

confidence: 99%

Swarm intelligence: past, present and future

Yang¹,

et al. 2017

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Many optimization problems in science and engineering are challenging to solve, and the current trend is to use swarm intelligence (SI) and SI-based algorithms to tackle such challenging problems. Some significant developments have been made in recent years, though there are still many open problems in this area. This paper provides a short but timely analysis about SI-based algorithms and their links with self-organization. Different characteristics and properties are analyzed here from both mathematical and qualitative perspectives. Future research directions are outlined and open questions are also highlighted.

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“…For VMS and VMP, a new algorithm called VM selection and placement using Random Key Cuckoo search (VMSPRKCS), was proposed to handle both VMS and VMP steps. Random Key Cuckoo Search (RKCS) [10] is a novel approach using Cuckoo Search (CS) [11], based on random keys encoding schema (RKES) [12]. CloudSim [13], with a real data set presented from PlanetLab [14], has been used to evaluate the proposed algorithm, and the experimental results showed that the proposed algorithm significantly reduces the consumed energy as well as SLAV.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Algorithm Naive Bayesian Classifier and Random Key Cuckoo Search for Virtual Machine Consolidation Problem

Moaly¹,

A.Youssef²

2020

IJACSA

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The trade-off between Energy consumption and SLA violation presents a serious challenge in cloud computing environments. A non-aggressive virtual machine consolidation algorithm is a good approach to reduce the consumed energy as well as SLA violation. A well-known strategy to deal with the virtual machine consolidation problem consists of four steps: host overloading detection, host under-loading detection, virtual machine selection and virtual machine placement. In this paper, the previous strategy is modified by merging the last two steps virtual machine selection and virtual machine placement, to avoid any poor solutions caused by solving both steps separately. In the host overloading/under-loading detection steps, we classified host status into five classes: Over-Utilized, Nearly Over-Utilized, Normal Utilized, Under-Utilized and Switched Off, then an algorithm, based on the Naive Bayesian Classifier, was introduced in order to detect the future host state for minimizing the number of virtual machine migrations; as a result, the energy consumption and performance degradation due to migrations will be minimized. In the virtual machine selection and placement steps, we introduced an algorithm based on the Random Key Cuckoo Search to reduce the energy consumption and enhance the SLA violation. To assess the algorithm, real data traces for 10 days, were used to verify the proposed algorithms. The experimental results proved that the proposed algorithms can significantly reduce the consumed energy as well as the SLA violation in data centers.

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Random-key cuckoo search for the travelling salesman problem

Cited by 63 publications

References 27 publications

Imperial competitive algorithm with policy learning for the traveling salesman problem

Imperial competitive algorithm with policy learning for the traveling salesman problem

Swarm intelligence: past, present and future

Hybrid Algorithm Naive Bayesian Classifier and Random Key Cuckoo Search for Virtual Machine Consolidation Problem

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