Particle Swarm Optimization Based Multi-Objective User Association for LTE-A Heterogeneous Networks

2021 ◽  
Author(s):  
Mhd Amen Summakieh ◽  
Chee Keong Tan ◽  
Yin Hoe Ng ◽  
Ayman A. El-Saleh
Keyword(s):  
Particle Swarm Optimization ◽  
Heterogeneous Networks ◽  
Particle Swarm ◽  
Conventional Technique ◽  
Base Station ◽  
Throughput Maximization ◽  
User Association ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Single Objective

Abstract Heterogeneous networks (HetNets) are a promising communication paradigm to satisfy the diverse requirements of Long Term Evolution-Advanced (LTE-A). Associating users with different base station tiers using the conventional technique based on the highest received SINR is not viable in HetNets due to its rigid association, which only aims at throughput maximization. Many e orts have been made to tackle the optimization problem of user association with a single objective such as throughput, fairness or energy efficiency. In this paper, we propose a novel multi-objective user association technique using particle swarm optimization (PSO) with the aim of jointly maximizing the throughput and the network balance index (NBI). By incorporating weight factors into the proposed scheme, the system operator has the flexibility to configure the priority levels of throughput and NBI. Numerical results demonstrate that our proposed multi-objective user association technique achieves better performance in terms of fitness values compared to the single-objective user association schemes.

scholarly journals Multi-Objective Particle Swarm Optimization of Sensor Distribution Scheme with Consideration of the Accuracy and the Robustness for Deformation Reconstruction

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1306 ◽  
Author(s):  
Feifei Zhao ◽  
Hong Bao ◽  
Song Xue ◽  
Qian Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Model ◽  
Particle Swarm ◽  
Strain Sensor ◽  
Reconstruction Accuracy ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Promising Tool ◽  
Circular Beam ◽  
Single Objective

For the inverse finite element method (iFEM), an inappropriate scheme of strain senor distribution would cause severe degradation of the deformation reconstruction accuracy. The robustness of the strain–displacement transfer relationship and the accuracy of reconstruction displacement are the two key factors of reconstruction accuracy. Previous research studies have been focused on single-objective optimization for the robustness of the strain–displacement transfer relationship. However, researchers found that it was difficult to reach a mutual balance between robustness and accuracy using single-objective optimization. In order to solve this problem, a bi-objective optimal model for the scheme of sensor distribution was proposed for this paper, where multi-objective particle swarm optimization (MOPSO) was employed to optimize the robustness and the accuracy. Initially, a hollow circular beam subjected to various loads was used as a case to perform the static analysis. Next, the optimization model was established and two different schemes of strain sensor were obtained correspondingly. Finally, the proposed schemes were successfully implemented in both the simulation calculation and the experiment test. It was found that the results from the proposed optimization model in this paper proved to be a promising tool for the selection of the scheme of strain sensor distribution.

Particle Swarm Optimization as Applied to Electromagnetic Design Problems

2018 ◽  
Vol 9 (2) ◽  
pp. 47-82 ◽  
Author(s):  
Sotirios K. Goudos ◽  
Zaharias D. Zaharis ◽  
Konstantinos B. Baltzis
Keyword(s):  
Particle Swarm Optimization ◽  
Mobile Communications ◽  
Transfer Functions ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Base Station ◽  
Dual Band ◽  
Design Problems ◽  
Swarm Optimization ◽  
Multi Objective

Particle swarm optimization (PSO) is a swarm intelligence algorithm inspired by the social behavior of birds flocking and fish schooling. Numerous PSO variants have been proposed in the literature for addressing different problem types. In this article, the authors apply different PSO variants to common design problems in electromagnetics. They apply the Inertia Weight PSO (IWPSO), the Constriction Factor PSO (CFPSO), and the Comprehensive Learning Particle Swarm Optimization (CLPSO) algorithms to real-valued optimization problems, i.e. microwave absorber design, and linear array synthesis. Moreover, the authors use discrete PSO optimizers such as the binary PSO (binPSO) and the Boolean PSO with a velocity mutation (BPSO-vm) in order to solve discrete-valued optimization problems, i.e. patch antenna design. Additionally, the authors apply and compare binPSO with different transfer functions to thinning array design problems. In the case of a multi-objective optimization problem, they apply two multi-objective PSO variants to dual-band base station antenna optimization for mobile communications. Namely, these are the Multi-Objective PSO (MOPSO) and the Multi-Objective PSO with Fitness Sharing (MOPSO-fs) algorithms. Finally, the authors conclude the paper by providing a discussion on future trends and the conclusion.

Particle Swarm Optimization for Ship Degaussing Coils Calibration

2012 ◽  
Vol 182-183 ◽  
pp. 1446-1451
Author(s):  
Ming Ming Yang ◽  
Da Ming Liu ◽  
Li Ting Lian
Keyword(s):  
Magnetic Field ◽  
Particle Swarm Optimization ◽  
Objective Function ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Scale Factors ◽  
Single Objective

In this paper, we deal with the problem of the ship degaussing coils optimal calibration by a linearly decreasing weight particle swarm optimization (LDW-PSO). Taking the ship’s magnetic field and its gradient reduction into account, the problem is treated as a multi-objective optimization problem. First a set of scale factors are calculated by LDW-PSO to scale the two kinds of objective function, then the multi-objective optimization problem is transformed to a single objective optimization problem via a set of proper weights, and the problem is solved by LDW-PSO finally. A typical ship degaussing system is applied to test the method’s validity, and the results are good.

Multi-Objective Particles Swarm Optimization Approaches

2011 ◽  
pp. 20-42 ◽  
Author(s):  
Konstantinos E. Parsopoulos ◽  
Michael N. Vrahatis
Keyword(s):  
Particle Swarm Optimization ◽  
Real World ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Future Research ◽  
Swarm Optimization ◽  
Computational Burden ◽  
Multi Objective ◽  
Active Research ◽  
Single Objective

The multiple criteria nature of most real world problems has boosted research on multi-objective algorithms that can tackle such problems effectively, with the smallest possible computational burden. Particle Swarm Optimization has attracted the interest of researchers due to its simplicity, effectiveness and efficiency in solving numerous single-objective optimization problems. Up-to-date, there are a significant number of multi-objective Particle Swarm Optimization approaches and applications reported in the literature. This chapter aims at providing a review and discussion of the most established results on this field, as well as exposing the most active research topics that can give initiative for future research.

A New Multi-Objective Mixed-Discrete Particle Swarm Optimization Algorithm

2014 ◽  
Author(s):  
Weiyang Tong ◽  
Souma Chowdhury ◽  
Achille Messac
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Mixed Integer ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Multi Objective ◽  
Diversity Preservation ◽  
Preservation Technique ◽  
Single Objective

Complex system design problems tend to be high dimensional and nonlinear, and also often involve multiple objectives and mixed-integer variables. Heuristic optimization algorithms have the potential to address the typical (if not most) characteristics of such complex problems. Among them, the Particle Swarm Optimization (PSO) algorithm has gained significant popularity due to its maturity and fast convergence abilities. This paper seeks to translate the unique benefits of PSO from solving typical continuous single-objective optimization problems to solving multi-objective mixed-discrete problems, which is a relatively new ground for PSO application. The previously developed Mixed-Discrete Particle Swarm Optimization (MDPSO) algorithm, which includes an exclusive diversity preservation technique to prevent premature particle clustering, has been shown to be a powerful single-objective solver for highly constrained MINLP problems. In this paper, we make fundamental advancements to the MDPSO algorithm, enabling it to solve challenging multi-objective problems with mixed-discrete design variables. In the velocity update equation, the explorative term is modified to point towards the non-dominated solution that is the closest to the corresponding particle (at any iteration). The fractional domain in the diversity preservation technique, which was previously defined in terms of a single global leader, is now applied to multiple global leaders in the intermediate Pareto front. The multi-objective MDPSO (MO-MDPSO) algorithm is tested using a suite of diverse benchmark problems and a disc-brake design problem. To illustrate the advantages of the new MO-MDPSO algorithm, the results are compared with those given by the popular Elitist Non-dominated Sorting Genetic Algorithm-II (NSGA-II).

A Study of Single-Objective Particle Swarm Optimization and Multi-Objective Particle Swarm Optimization

2014 ◽  
Vol 543-547 ◽  
pp. 1635-1638 ◽  
Author(s):  
Ming Li Song
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Convergence Speed ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Simple Realization ◽  
Single Objective ◽  
Selection Of

The complexity of optimization problems encountered in various modeling algorithms makes a selection of a proper optimization vehicle crucial. Developments in particle swarm algorithm since its origin along with its benefits and drawbacks are mainly discussed as particle swarm optimization provides a simple realization mechanism and high convergence speed. We discuss several developments for single-objective case problem and multi-objective case problem.

Pareto Optimal Design of Dual-Band Base Station Antenna Arrays Using Multi-Objective Particle Swarm Optimization With Fitness Sharing

2009 ◽  
Vol 45 (3) ◽  
pp. 1522-1525 ◽  
Author(s):  
Sotirios K. Goudos ◽  
Zaharias D. Zaharis ◽  
Dimitra G. Kampitaki ◽  
Ioannis T. Rekanos ◽  
Costas S. Hilas
Keyword(s):  
Particle Swarm Optimization ◽  
Optimal Design ◽  
Antenna Arrays ◽  
Particle Swarm ◽  
Base Station ◽  
Dual Band ◽  
Pareto Optimal ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Fitness Sharing
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