Proposing bat inspired heuristic algorithm for the optimization of GMPLS networks

Masood, Mohsin; Fouad, Mohamed Mostafa; Glesk, Ivan

doi:10.1109/telfor.2017.8249295

Cited by 5 publications

(6 citation statements)

References 10 publications

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“…The first evaluation appears in the outcomes of solving the MCOP optimization problem in MPLS/ GMPLS networks by using the Pareto Front graphs. The second investigation is a comparative study analysis; where the offered algorithm performance is compared against numerous metaheuristic algorithms such as particle swarm optimization (PSO), adaptive PSO [33], [34], Bat algorithm [35] and Dolphin algorithm [36]. Both researches focus on an algorithm convergence ratio (particularly the trapping in local/global optima) to test how successfully the proposed algorithm fixed the exploration problem when compared to the other optimization algorithms.…”

Section: Empirical Assessmentsmentioning

confidence: 99%

An Improved Particle Swarm Algorithm for Multi-Objectives Based Optimization in MPLS/GMPLS Networks

et al. 2019

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Particle swarm optimization (PSO) is a swarm-based optimization technique capable of solving different categories of optimization problems. Nevertheless, PSO has a serious exploration issue that makes it a difficult choice for multi-objectives constrained optimization problems (MCOP). At the same time, Multi-Protocol Label Switched (MPLS) and its extended version Generalized MPLS, has become an emerging network technology for modern and diverse applications. Therefore, as per MPLS and Generalized MPLS MCOP needs, it is important to find the Pareto based optimal solutions that guarantee the optimal resource utilization without compromising the quality of services (QoS) within the networks. The paper proposes a novel version of PSO, which includes a modified version of the Elitist learning Strategy (ELS) in PSO that not only solves the existing exploration problem in PSO, but also produces optimal solutions with efficient convergence rates for different MPLS/ GMPLS network scales. The proposed approach has also been applied with two objective functions; the resource provisioning and the traffic load balancing costs. Our simulations and comparative study showed improved results of the proposed algorithm over the well-known optimization algorithms such as standard PSO, Adaptive PSO, Bat and Dolphin algorithm.

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Section: Empirical Assessmentsmentioning

confidence: 99%

An Improved Particle Swarm Algorithm for Multi-Objectives Based Optimization in MPLS/GMPLS Networks

et al. 2019

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“…The path cost is denoted as and thus equal to the sum of its link costs. is an associated bandwidth which is routed on the path [5]. The total routing cost mathematically can be expressed as…”

Section: (B) Routing Costmentioning

confidence: 99%

Analysis of Artificial Intelligence-Based Metaheuristic Algorithm for MPLS Network Optimization

Masood

Fouad

2018

2018 20th International Conference on Transparent Optical Networks (ICTON)

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Multiprotocol label switched (MPLS) networks were introduced to enhance the network`s service provisioning and optimize its performance using multiple protocols along with label switched based networking technique. With the addition of traffic engineering entity in MPLS domain, there is a massive increase in the networks resource management capability with better quality of services (QoS) provisioning for end users. Routing protocols play an important role in MPLS networks for network traffic management, which uses exact and approximate algorithms. There are number of artificial intelligence-based optimization algorithms which can be used for the optimization of traffic engineering in MPLS networks. The paper presents an optimization model for MPLS networks and proposed dolphin-echolocation algorithm (DEA) for optimal path computation. For Network with different nodes, both algorithms performance has been investigated to study their convergence towards the production of optimal solutions. Furthermore, the DEA algorithm will be compared with the bat algorithm to examine their performance in MPLS network optimization. Various parameters such as mean, minimum /optimal fitness function values and standard deviation.

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“…Population-based techniques, such as simulated annealing, random generation, and metaheuristic algorithms, are examples of randomised algorithms. [5][6][7][8][9] Machine learning methods on the other hand are seen to solve the feature selection problem but with a great cost in computation, high complexities and premature convergence. In order to contain these drawbacks, metaheuristic algorithms were proposed, as they are good at dealing with these type of conditions.…”

Section: Introductionmentioning

confidence: 99%

A Modified Whale Optimization Algorithm for Enhancing the Features Selection Process in Machine Learning

Syed¹,

Masood

Fouad

et al. 2021

2021 29th Telecommunications Forum (TELFOR)

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In recent years, when there is an abundance of large datasets in various fields, the importance of feature selection problem has become critical for researchers. The real world applications rely on large datasets, which implies that datasets have hundreds of instances and attributes. Finding a better way of optimum feature selection could significantly improve the machine learning predictions. Recently, metaheuristics have gained momentous popularity for solving feature selection problem. Whale Optimization Algorithm has gained significant attention by the researcher community searching to solve the feature selection problem. However, the exploration problem in whale optimization algorithm still exists and remains to be researched as various parameters within the whale algorithm have been ignored. This paper proposes a new and improved version of the whale algorithm entitled Modified Whale Optimization Algorithm (MWOA) that hybrid with the machine learning models such as logistic regression, decision tree, random forest, K-nearest neighbor, support vector machine, naïve Bayes model. To test this new approach and the performance, the breast cancer dataset was used for MWOA evaluation. The test results revealed the superiority of this model when compared to the results obtained by machine learning models.

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Proposing bat inspired heuristic algorithm for the optimization of GMPLS networks

Cited by 5 publications

References 10 publications

An Improved Particle Swarm Algorithm for Multi-Objectives Based Optimization in MPLS/GMPLS Networks

An Improved Particle Swarm Algorithm for Multi-Objectives Based Optimization in MPLS/GMPLS Networks

Analysis of Artificial Intelligence-Based Metaheuristic Algorithm for MPLS Network Optimization

A Modified Whale Optimization Algorithm for Enhancing the Features Selection Process in Machine Learning

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