PSO and ACO Algorithms Applied to Optimization Resource Allocation to Support QoS Requirements in NGN

Le, Dac‐Nhuong

doi:10.11591/ijins.v2i3.2072

Cited by 9 publications

(3 citation statements)

References 26 publications

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“…PSO is a stochastic optimization technique inspired by bird flocking or fish schooling. Compared with other evolutionary computation algorithms, such as genetic algorithms [33]- [36], the PSO has no evolution operators [37]. Detailed description of PSO can be found in the Appendix B.…”

Section: ) the Specific Identification Process Of Psomentioning

confidence: 99%

Ant Colony Optimization PID Control of Hypnosis With Propofol Using Renyi Permutation Entropy as Controlled Variable

Liang

Ling-yun

et al. 2019

IEEE Access

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General anesthesia is a critical procedure in clinical surgery. To offer a closed control of depth of hypnosis, we establish a closed-loop anesthetic delivery system for propofol anesthesia. The system consists of three components: 1) three-compartment pharmacokinetic model; 2) a pharmacodynamics model obtained by identifying the relationship of effect-site concentration and Renyi permutation entropy via particle swarm optimization; and 3) ant colony optimization proportion integration differentiation controller. The performance of Renyi permutation entropy and bispectral index in tracking the effect-site concentration is evaluated by the prediction probability. The assessments of the performance of the controller are using: 1) the rising time, percent overshoot, and settling time and 2) median performance error, median absolute performance error, wobble, divergence, and integral absolute error. The results show that the prediction probability of Renyi permutation entropy (0.79±0.13 and mean±standard deviation) is higher than the bispectral index (0.74±0.15). The ant colony optimization proportion integration differentiation controller is quick to respond to sudden changes and maintains the stabilty at the desired depth of hypnosis (rise time and overshoot are 4.53±1.96 min and 3.48±1.49 (%), respectively). In conclusion, the proposed closed-loop anesthetic delivery system has potential value for accurate anesthetic administration.INDEX TERMS General anesthesia, closed-loop anesthetic delivery system, Renyi permutation entropy, ant colony optimization proportional integral derivative, particle swarm optimization.

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Section: ) the Specific Identification Process Of Psomentioning

confidence: 99%

Ant Colony Optimization PID Control of Hypnosis With Propofol Using Renyi Permutation Entropy as Controlled Variable

Liang

Ling-yun

et al. 2019

IEEE Access

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“…Quality of Service (QoS) is a management concept that aims to optimize network resources or process and ensure good performance of an Open Distributed Processing (ODP) system, this concept is fundamental in many fields such as transmission protocols [11], routing algorithms [12], resources allocation algorithms [13] and web service [14]. In our negotiation process, we will base our study on the trader concept [1].…”

Section: Negotiation Approachmentioning

confidence: 99%

Formal Specification of QoS Negotiation in ODP System

Jarrar

Balouki

Gadi

2017

IJECE

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The future of Open Distributed Processing systems (ODP) will see an increasing of components number, these components are sharing resources. In general, these resources are offering some kind of services. Due to the huge number of components, it is very difficult to offer the optimum Quality of service (QoS). This encourages us to develop a model for QoS negotiation process to optimize the QoS in an ODP system. In such system, there is a High risk of software or hardware failure. To ensure good performance of a system based on our model, we develop it using a formal method. In our case, we will use Event-B to get in the end of our development a system correct by construction. Keyword: INTRODUCTIONThe evolution of telecommunications technology and the structure of organizations have led to the emergence of complex distributed systems. These systems are distributed structures whose components, both hardware and software, are of different types. In some systems, these components are developed by different actors acting independently of each other. The assembly of such components gives rise to highly heterogeneous systems. The applications that support these systems are themselves composed of distributed components. The interaction between these application components is one of the aspects of the distributed treatment. Specifically, the distributed processing correspond to different aspects of information processing in which specific components can be located in different places, during this process the communication between components may be delays or failures. This means that this kind of systems needs to be developed carefully due to its complexity [1]. This motivates us to model it using a formal method to ensure the correctness of our system and obtain a very strong assurance of bug's absence. Formal methods are a particular kind of mathematically based techniques for the specification [2], [3], development and verification of software and hardware systems [4]. There are a variety of formal methods such as language Z, a specification in Z is a predicate, the specification of invariants and the specification of operations have the form of a predicate. There is also B-method which is a method of software development based on B, a toolsupported formal method based on an abstract machine notation, used in the development of computer software. It was originally developed by Jean-Raymond Abrial [5].In this paper, we will use Event-B [6] since it allows us to prove that our system is correct by construction basing on proofs obligations. These proofs are done automatically by a tool called Rodin [7]. Event-B is also based on refinement which means creating an abstract model and enriching it in a multiple steps by adding more details to get a more concrete model [8]. In every refinement, we prove that the system is correct and it does not contradict with the previous one, whereby the resulting system is correct by

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“…Several approaches have been proposed to adapt the parameters on Ant Colony algorithms [4][5][6][7][8]. Espacially the adaptation using Fuzzy Logic, in this part the most important are presented.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Updating the Local Pheromone on ACS Performance using Fuzzy Logic

Afia

Bouzbita

Faizi

2017

IJECE

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Fuzzy Logic Controller (FLC) has become one of the most frequently utilised algorithms to adapt the metaheuristics parameters as an artificial intelligence technique. In this paper, the parameter of Ant Colony System (ACS) algorithm is adapted by the use of FLC, and its behaviour is studied during this adaptation. The proposed approach is compared with the standard ACS algorithm. Computational results are done based on a library of sample instances for the Traveling Salesman Problem (TSPLIB).

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PSO and ACO Algorithms Applied to Optimization Resource Allocation to Support QoS Requirements in NGN

Cited by 9 publications

References 26 publications

Ant Colony Optimization PID Control of Hypnosis With Propofol Using Renyi Permutation Entropy as Controlled Variable

Ant Colony Optimization PID Control of Hypnosis With Propofol Using Renyi Permutation Entropy as Controlled Variable

Formal Specification of QoS Negotiation in ODP System

The Effect of Updating the Local Pheromone on ACS Performance using Fuzzy Logic

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