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.

Power Transmission and Transformation Project Establishing and Decision-Making Based on ASU-MOPSO Algorithm

2014 ◽  
Vol 521 ◽  
pp. 521-529 ◽  
Author(s):  
Ke Sun ◽  
Kai Xu ◽  
Zhao Ming Zheng ◽  
Xiao Yu Ding ◽  
Ke Sun ◽  
...  
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Euclidean Distance ◽  
Power Transmission ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Computing Complexity ◽  
Crowding Distance ◽  
Decision Making Problem

This paper demonstrates an asynchronous-stepwise updated strategy multi-objective particle swarm optimization (ASU-MOPSO) algorithm to improve the convergence and diversity of the multi-objective particle swarm optimization. In the process of the elite reduction, we utilize the asynchronous grid strategy to filter particles since this strategy has lower computing complexity. Meanwhile, the stepwised Euclidean crowding distance strategy is presented to filter particles within every grid, which uses the sum of the Euclidean distance with the two nearest particles to replace of the traditional crowding distance. This strategy can avoid the destruction of distribution diversity. Finally, our algorithm is successfully applied for the actual power transmission and transformation project establishing and decision-making problem. Comparing with traditional MOPSO based on crowding distance strategy and grid strategy, our algorithm can obtain the better solution.

scholarly journals Multi-objective clustering algorithm using particle swarm optimization with crowding distance (MCPSO-CD)

2020 ◽  
Vol 6 (1) ◽  
pp. 72
Author(s):  
Alwatben Batoul Rashed ◽  
Hazlina Hamdan ◽  
Nurfadhlina Mohd Sharef ◽  
Md Nasir Sulaiman ◽  
Razali Yaakob ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Clustering Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Solution Technique ◽  
Pareto Optimal ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Crowding Distance

Clustering, an unsupervised method of grouping sets of data, is used as a solution technique in various fields to divide and restructure data to become more significant and transform them into more useful information. Generally, clustering is difficult and complex phenomenon, where the appropriate numbers of clusters are always unknown, comes with a large number of potential solutions, and as well the datasets are unsupervised. These problems can be addressed by the Multi-Objective Particle Swarm Optimization (MOPSO) approach, which is commonly used in addressing optimization problems. However, MOPSO algorithm produces a group of non-dominated solutions which make the selection of an “appropriate” Pareto optimal or non-dominated solution more difficult. According to the literature, crowding distance is one of the most efficient algorithms that was developed based on density measures to treat the problem of selection mechanism for archive updates. In an attempt to address this problem, the clustering-based method that utilizes crowding distance (CD) technique to balance the optimality of the objectives in Pareto optimal solution search is proposed. The approach is based on the dominance concept and crowding distances mechanism to guarantee survival of the best solution. Furthermore, we used the Pareto dominance concept after calculating the value of crowding degree for each solution. The proposed method was evaluated against five clustering approaches that have succeeded in optimization that comprises of K-means Clustering, MCPSO, IMCPSO, Spectral clustering, Birch, and average-link algorithms. The results of the evaluation show that the proposed approach exemplified the state-of-the-art method with significant differences in most of the datasets tested.

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