Constriction Factor Particle Swarm Optimization based load balancing and cell association for 5G heterogeneous networks

2021 ◽  
Author(s):  
Mohammad Kamrul Hasan ◽  
Teong Chee Chuah ◽  
Ayman A. El-Saleh ◽  
Muhammad Shafiq ◽  
Shoaib Ahmed Shaikh ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Load Balancing ◽  
Heterogeneous Networks ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Cell Association ◽  
Constriction Factor

scholarly journals Improved Load Balancing for LTE-A Heterogeneous Networks using Particle Swarm Optimization

2019 ◽  
Vol 10 (7) ◽  
pp. 1407
Author(s):  
MHD Amen Summakieh ◽  
Chee Keong Tan ◽  
Ayman, A. El-Saleh ◽  
Teong Chee Chuah
Keyword(s):  
Particle Swarm Optimization ◽  
Load Balancing ◽  
Heterogeneous Networks ◽  
Particle Swarm ◽  
Swarm Optimization

scholarly journals Directionally-Enhanced Binary Multi-Objective Particle Swarm Optimisation for Load Balancing in Software Defined Networks

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3356
Author(s):  
Mustafa Hasan Albowarab ◽  
Nurul Azma Zakaria ◽  
Zaheera Zainal Abidin
Keyword(s):  
Particle Swarm Optimization ◽  
Load Balancing ◽  
Mathematical Formulation ◽  
Particle Swarm ◽  
Solution Space ◽  
Particle Swarm Optimisation ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Crowding Distance ◽  
Standard Models

Various aspects of task execution load balancing of Internet of Things (IoTs) networks can be optimised using intelligent algorithms provided by software-defined networking (SDN). These load balancing aspects include makespan, energy consumption, and execution cost. While past studies have evaluated load balancing from one or two aspects, none has explored the possibility of simultaneously optimising all aspects, namely, reliability, energy, cost, and execution time. For the purposes of load balancing, implementing multi-objective optimisation (MOO) based on meta-heuristic searching algorithms requires assurances that the solution space will be thoroughly explored. Optimising load balancing provides not only decision makers with optimised solutions but a rich set of candidate solutions to choose from. Therefore, the purposes of this study were (1) to propose a joint mathematical formulation to solve load balancing challenges in cloud computing and (2) to propose two multi-objective particle swarm optimisation (MP) models; distance angle multi-objective particle swarm optimization (DAMP) and angle multi-objective particle swarm optimization (AMP). Unlike existing models that only use crowding distance as a criterion for solution selection, our MP models probabilistically combine both crowding distance and crowding angle. More specifically, we only selected solutions that had more than a 0.5 probability of higher crowding distance and higher angular distribution. In addition, binary variants of the approaches were generated based on transfer function, and they were denoted by binary DAMP (BDAMP) and binary AMP (BAMP). After using MOO mathematical functions to compare our models, BDAMP and BAMP, with state of the standard models, BMP, BDMP and BPSO, they were tested using the proposed load balancing model. Both tests proved that our DAMP and AMP models were far superior to the state of the art standard models, MP, crowding distance multi-objective particle swarm optimisation (DMP), and PSO. Therefore, this study enables the incorporation of meta-heuristic in the management layer of cloud networks.

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