Routing in networks using genetic algorithm

Moza, Meenakshi; Kumar, Suresh

doi:10.1504/ijcnds.2018.091052

Cited by 4 publications

(2 citation statements)

References 1 publication

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“…One of the greatest advantages of GAs is the simplicity in the optimization problem formulation [32]. Usually, fixed-length bit strings are used as input for the algorithm, which perfectly adapts to problems involving graphs [33][34][35]. Another advantage is the fast convergence time, in comparison to polynomial algorithms, for problems that involve a large number of variables [36].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Evolutionary Strategy for Practical Design of Passive Optical Networks

et al. 2022

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Passive optical networks (PONs) are an important and interesting technology for broadband access as a result of the growing demand for bandwidth over the past 10 years. An arduous and complex step in the design of such networks involves determining the placement of equipment, optical fiber cables and several other parameters relevant to the proper functioning of the network. In this paper, we propose an evolutionary strategy to optimize the infrastructure design of PONs by using genetic algorithm technique. This meta-heuristic is capable of elaborating fast, automatic and efficient solutions for the design and planning of PONs. Our proposal has been developed using real maps, aiming to minimize deployment costs and time spent to carry out PON projects, achieving pre-defined quality criteria. We considered, in our simulations, two scenarios (non-dense and dense), four possible topologies and two regions of interest. The non-dense consists of a scenario in which subscribers are distributed in a dispersed manner in the region of interest. The dense has a considerably higher number of subscribers distributed in a very close way to each other. Based on the obtained results, the potential of our proposal is quite clear, as well as its relevance from a technical, economic, and commercial point of view.

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Section: Genetic Algorithmmentioning

confidence: 99%

Evolutionary Strategy for Practical Design of Passive Optical Networks

et al. 2022

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“…From the previous researches, most researchers used genetic algorithm to optimise the system parameter such as routing in networks, job shop scheduling problem and isothermal liquid phase kinetic sequence [7]- [9]. Moreover, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is proving to be a robust optimization algorithm for a wide range of multi-objective problems.…”

Section: Introductionmentioning

confidence: 99%

Newly Developed Nonlinear Vehicle Model for an Active Anti-roll Bar System

Zulkarnain¹,

Zamzuri²,

Saruchi³

et al. 2018

Bulletin EEI

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This paper presents the development of a newly developed nonlinear vehicle model is used in the validation process of the vehicle model. The parameters chosen in a newly developed vehicle model is developed based on CARSIM vehicle model by using non-dominated sorting genetic algorithm version II (NSGA-II) optimization method. The ride comfort and handling performances have been one of the main objective to fulfil the expectation of customers in the vehicle development. Full nonlinear vehicle model which consists of ride, handling and Magic tyre subsystems has been derived and developed in MATLAB/Simulink. Then, optimum values of the full nonlinear vehicle parameters are investigated by using NSGA-II. The two objective functions are established based on RMS error between simulation and benchmark system. A stiffer suspension provides good stability and handling during manoeuvres while softer suspension gives better ride quality. The final results indicated that the newly developed nonlinear vehicle model is behaving accurately with input ride and manoeuvre. The outputs trend are successfully replicated.

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Secure edge computing vulnerabilities in smart cities sustainability using petri net and genetic algorithm-based reinforcement learning

Ajao¹,

Apeh²

2023

Intelligent Systems with Applications

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Routing in networks using genetic algorithm

Cited by 4 publications

References 1 publication

Evolutionary Strategy for Practical Design of Passive Optical Networks

Evolutionary Strategy for Practical Design of Passive Optical Networks

Newly Developed Nonlinear Vehicle Model for an Active Anti-roll Bar System

Secure edge computing vulnerabilities in smart cities sustainability using petri net and genetic algorithm-based reinforcement learning

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