scholarly journals Performance-aware placement and chaining scheme for virtualized network functions: a particle swarm optimization approach

2021 ◽  
Author(s):  
Samane Asgari ◽  
Shahram Jamali ◽  
Reza Fotohi ◽  
Mahdi Nooshyar
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Network Functions

Power-efficient routing for SDN with discrete link rates and size-limited flow tables: A tree-based particle swarm optimization approach

2017 ◽  
Vol 27 (5) ◽  
pp. e1972 ◽  
Author(s):  
Mohamad Khattar Awad ◽  
Mohammed El-Shafei ◽  
Tassos Dimitriou ◽  
Yousef Rafique ◽  
Mohammed Baidas ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Power Efficient

scholarly journals A Distributed Multifactorial Particle Swarm Optimization Approach

2021 ◽  
Author(s):  
Ahlem Aboud ◽  
Nizar Rokbani ◽  
Seyedali Mirjalili ◽  
Abdulrahman M. Qahtani ◽  
Omar Almutiry ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Beta Function ◽  
Particle Swarm ◽  
Search Space ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
New System

<p>Multifactorial Optimization (MFO) and Evolutionary Transfer Optimization (ETO) are new optimization challenging paradigms for which the multi-Objective Particle Swarm Optimization system (MOPSO) may be interesting despite limitations. MOPSO has been widely used in static/dynamic multi-objective optimization problems, while its potentials for multi-task optimization are not completely unveiled. This paper proposes a new Distributed Multifactorial Particle Swarm Optimization algorithm (DMFPSO) for multi-task optimization. This new system has a distributed architecture on a set of sub-swarms that are dynamically constructed based on the number of optimization tasks affected by each particle skill factor. DMFPSO is designed to deal with the issues of handling convergence and diversity concepts separately. DMFPSO uses Beta function to provide two optimized profiles with a dynamic switching behaviour. The first profile, Beta-1, is used for the exploration which aims to explore the search space toward potential solutions, while the second Beta-2 function is used for convergence enhancement. This new system is tested on 36 benchmarks provided by the CEC’2021 Evolutionary Transfer Multi-Objective Optimization Competition. Comparatives with the state-of-the-art methods are done using the Inverted General Distance (IGD) and Mean Inverted General Distance (MIGD) metrics. Based on the MSS metric, this proposal has the best results on most tested problems.</p>

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