Multi-objective optimization of satellite image registration using Discrete Particle Swarm Optimisation

2011 ◽  
Author(s):  
J. Senthilnath ◽  
S. N. Omkar ◽  
V. Mani ◽  
T. Karthikeyan
Keyword(s):  
Image Registration ◽  
Satellite Image ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Multi Objective Optimization ◽  
Discrete Particle ◽  
Multi Objective

scholarly journals Multi-objective discrete particle swarm optimisation algorithm for integrated assembly sequence planning and assembly line balancing

2016 ◽  
Vol 232 (8) ◽  
pp. 1444-1459 ◽  
Author(s):  
Mohd Fadzil Faisae Ab Rashid ◽  
Windo Hutabarat ◽  
Ashutosh Tiwari
Keyword(s):  
Assembly Line ◽  
Particle Swarm ◽  
Assembly Line Balancing ◽  
Particle Swarm Optimisation ◽  
Assembly Sequence Planning ◽  
Line Balancing ◽  
Assembly Sequence ◽  
Discrete Particle ◽  
Sequence Planning ◽  
Multi Objective

In assembly optimisation, assembly sequence planning and assembly line balancing have been extensively studied because both activities are directly linked with assembly efficiency that influences the final assembly costs. Both activities are categorised as NP-hard and usually performed separately. Assembly sequence planning and assembly line balancing optimisation presents a good opportunity to be integrated, considering the benefits such as larger search space that leads to better solution quality, reduces error rate in planning and speeds up time-to-market for a product. In order to optimise an integrated assembly sequence planning and assembly line balancing, this work proposes a multi-objective discrete particle swarm optimisation algorithm that used discrete procedures to update its position and velocity in finding Pareto optimal solution. A computational experiment with 51 test problems at different difficulty levels was used to test the multi-objective discrete particle swarm optimisation performance compared with the existing algorithms. A statistical test of the algorithm performance indicates that the proposed multi-objective discrete particle swarm optimisation algorithm presents significant improvement in terms of the quality of the solution set towards the Pareto optimal set.

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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