scholarly journals On the Efficacy of Particle Swarm Optimization for Gateway Placement in LoRaWAN Networks

2021 ◽  
Author(s):  
Clement N. Nyirenda
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Flight Time ◽  
Geographical Information ◽  
Optimization Process ◽  
Deterministic Approach ◽  
Swarm Optimization ◽  
Gateway Placement ◽  
Future Work ◽  
Modified Pso

The efficacy of the Particle Swarm Optimization (PSO) in determining the optimal locations for gateways in LoRaWAN networks is investigated. A modified PSO approach, which introduces gateway distancing measures during the initialization phase and flight time, is proposed. For the ease of comparisons and the understanding of the behavior of the algorithms under study, a square LoRaWAN area is used for simulations. Optimization results on a LoRaWAN script, implemented in NS-3, show that the modified PSO converges faster and achieves better results than the traditional PSO, as the number of gateways increases. Results further show that the modified PSO approach achieves similar performance to a deterministic approach, in which gateways are uniformly distributed in the network. This shows that for swarm intelligence techniques such as PSO to be used for gateway placement in LoRaWAN networks, gateway distancing mechanisms must be incorporated in the optimization process. These results further show that these techniques can be easily deployed in geometrically more complex LoRaWAN figures such as rectangular, triangular, circular and trapezoidal shapes. It is generally difficult to figure out a deterministic gateway placement mechanism for such shapes. As part of future work, more realistic LoRaWAN networks will be developed by using real geographical information of an area.

High Precision Time Synchronization Algorithm for Smart Grid Based on Adaptive Filtering with Modified Particle Swarm Optimization

2013 ◽  
Vol 860-863 ◽  
pp. 2501-2506
Author(s):  
Wen Hua Han ◽  
Xiao Hui Shen
Keyword(s):  
Particle Swarm Optimization ◽  
Smart Grid ◽  
High Precision ◽  
Adaptive Filtering ◽  
Time Synchronization ◽  
Particle Swarm ◽  
Electric Power System ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Modified Pso

The time synchronization network provides time benchmark tasks for various services in electric power system. With the development of the power grid, the applications require more and more accurate time synchronization precision. In this paper, a method of time synchronization based on adaptive filtering with a modified particle swarm optimization (MPSO-AF) was presented to satisfy the high precision and high security requirements of the time synchronization for smart grid. The modified PSO was introduced for tuning the weight coefficients of the adaptive filter to improve the filtering property. The proposed MPSO-AF hybrid algorithm can combine the advantageous properties of the modified PSO and the adaptive filtering algorithm to enhance the performance of the time synchronization. A comparison of simulation results shows the optimization efficacy of the algorithm.

A dynamic particle swarm optimization method applied to global optimizations of engineering inverse problem

2018 ◽  
Vol 37 (1) ◽  
pp. 98-117 ◽  
Author(s):  
Shafiullah Khan ◽  
Shiyou Yang ◽  
Obaid Ur Rehman
Keyword(s):  
Inverse Problem ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Content Type ◽  
Novel Strategy ◽  
Modified Pso

Purpose The aim of this paper is to explore the potential of particle swarm optimization (PSO) algorithm to solve an electromagnetic inverse problem. Design/methodology/approach A modified PSO algorithm is designed. Findings The modified PSO algorithm is a more stable, robust and efficient global optimizer for solving the well-known benchmark optimization problems. The new mutation approach preserves the diversity of the population, whereas the proposed dynamic and adaptive parameters maintain a good balance between the exploration and exploitation searches. The numerically experimental results of two case studies demonstrate the merits of the proposed algorithm. Originality/value Some improvements, such as the design of a new global mutation mechanism and introducing a novel strategy for learning and control parameters, are proposed.

A Particle Swarm Optimization with Variety Factor

2012 ◽  
Vol 239-240 ◽  
pp. 1291-1297 ◽  
Author(s):  
Hai Sheng Qin ◽  
Deng Yue Wei ◽  
Jun Hui Li ◽  
Lei Zhang ◽  
Yan Qiang Feng
Keyword(s):  
Particle Swarm Optimization ◽  
Convergence Property ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Premature Convergence ◽  
Simulation Experiments ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Previous Algorithm ◽  
Modified Pso

A new particle swarm optimization (PSO) algorithm (a PSO with Variety Factor, VFPSO) based on the PSO was proposed. Compared with the previous algorithm, the proposed algorithm is to update the Variety Factor and to improve the inertia weight of the PSO. The target of the improvement is that the new algorithm could go on enhancing the robustness as before and should reduce the risk of premature convergence. The simulation experiments show that it has great advantages of convergence property over some other modified PSO algorithms, and also avoids algorithm being trapped in local minimum effectively. So it can avoid the phenomenon of premature convergence.

Circuit Design Based on Particle Swarm Optimization Algorithms

2011 ◽  
Vol 474-476 ◽  
pp. 1093-1098 ◽  
Author(s):  
Xue Song Yan ◽  
Qing Hua Wu ◽  
Cheng Yu Hu ◽  
Qing Zhong Liang
Keyword(s):  
Particle Swarm Optimization ◽  
Circuit Design ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Random Solution ◽  
Swarm Optimization ◽  
Fish Schools ◽  
Optimum Solution ◽  
Evolutionary Electronics ◽  
Modified Pso

This work investigates the application of Particle Swarm Optimization (PSO) algorithms in the field of evolutionary electronics. PSO was developed under the inspiration of behavior laws of bird flocks, fish schools and human communities. PSO achieves its optimum solution by starting from a group of random solution and then searching repeatedly. We propose the new means for designing electronic circuits and introduce the modified PSO algorithm. For the case studies this means has proved to be efficient, experiments show that we have better results.

scholarly journals A MODIFIED PARTICLE SWARM OPTIMIZATION WITH RANDOM ACTIVATION FOR INCREASING EXPLORATION

Kursor ◽  
2016 ◽  
Vol 8 (1) ◽  
pp. 33
Author(s):  
Alrijadjis Alrijadjis
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Inertia Weight ◽  
Modified Pso

Particle Swarm Optimization (PSO) is a popular optimization technique which is inspired by the social behavior of birds flocking or fishes schooling for finding food. It is a new metaheuristic search algorithm developed by Eberhart and Kennedy in 1995. However, the standard PSO has a shortcoming, i.e., premature convergence and easy to get stack or fall into local optimum. Inertia weight is an important parameter in PSO, which significantly affect the performance of PSO. There are many variations of inertia weight strategies have been proposed in order to overcome the shortcoming. In this paper, a new modified PSO with random activation to increase exploration ability, help trapped particles for jumping-out from local optimum and avoid premature convergence is proposed. In the proposed method, an inertia weight is decreased linearly until half of iteration, and then a random number for an inertia weight is applied until the end of iteration. To emphasis the role of this new inertia weight adjustment, the modified PSO paradigm is named Modified PSO with random activation (MPSO-RA). The experiments with three famous benchmark functions show that the accuracy and success rate of the proposed MPSO-RA increase of 43.23% and 32.95% compared with the standard PSO.

scholarly journals The State of Art in Particle Swarm Optimization Based Unit Commitment: A Review

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 733 ◽  
Author(s):  
Gad Shaari ◽  
Neyre Tekbiyik-Ersoy ◽  
Mustafa Dagbasi
Keyword(s):  
Particle Swarm Optimization ◽  
Unit Commitment ◽  
State Of The Art ◽  
Cost Minimization ◽  
Particle Swarm ◽  
Environmental Constraints ◽  
Thermal Unit ◽  
Spinning Reserve ◽  
Swarm Optimization ◽  
Modified Pso

Unit Commitment (UC) requires the optimization of the operation of generation units with varying loads, at every hour, under different technical and environmental constraints. Many solution techniques were developed for the UC problem, and the researchers are still working on improving the efficiency of these techniques. Particle swarm optimization (PSO) is an effective and efficient technique used for solving the UC problems, and it has gotten a considerable amount of attention in recent years. This study provides a state-of-the-art literature review on UC studies utilizing PSO or PSO-variant algorithms, by focusing on research articles published in the last decade. In this study, these algorithms/methods, objectives, constraints are reviewed, with focus on the UC problems that include at least one of the wind and solar technologies, along with thermal unit(s). Although, conventional PSO is one of the most effective techniques used in solving UC problem, other methods were also developed in literature to improve the convergence. In this study, these methods are grouped as extended PSO, modified PSO, and PSO with other techniques. This study shows that PSO with other techniques are utilized more than any other methods. In terms of constraints, it was observed that there are only few studies that considered Transmission Line (TL), Fuel (F), Emission (E), Storage (St) and Crew (Cr) constraints, while Power Balance (PB), Generation limit (GL), Unit minimum Up or Down Time (U/DT), Ramp Up or Ramp Down Time (R-U/DT) and system Spinning Reserve (SR) were the most utilized constraints in UC problems considering wind/solar as a renewable source. In addition, most of the studies are based on a single objective function (cost minimization) and, few of them are multi-objective (cost and emission minimization) based studies.

scholarly journals Multi-Leader Comprehensive Learning Particle Swarm Optimization with Adaptive Mutation for Economic Load Dispatch Problems

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 116 ◽  
Author(s):  
Anping Lin ◽  
Wei Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Real Life ◽  
Particle Swarm ◽  
Optimization Process ◽  
Adaptive Mutation ◽  
Economic Load Dispatch ◽  
Test Results ◽  
Swarm Optimization ◽  
Better Than

Particle swarm optimization (PSO) is one of the most popular, nature inspired optimization algorithms. The canonical PSO is easy to implement and converges fast, however, it suffers from premature convergence. The comprehensive learning particle swarm optimization (CLPSO) can achieve high exploration while it converges relatively slowly on unimodal problems. To enhance the exploitation of CLPSO without significantly impairing its exploration, a multi-leader (ML) strategy is combined with CLPSO. In ML strategy, a group of top ranked particles act as the leaders to guide the motion of the whole swarm. Each particle is randomly assigned with an individual leader and the leader is refreshed dynamically during the optimization process. To activate the stagnated particles, an adaptive mutation (AM) strategy is introduced. Combining the ML and the AM strategies with CLPSO simultaneously, the resultant algorithm is referred to as multi-leader comprehensive learning particle swarm optimization with adaptive mutation (ML-CLPSO-AM). To evaluate the performance of ML-CLPSO-AM, the CEC2017 test suite was employed. The test results indicate that ML-CLPSO-AM performs better than ten popular PSO variants and six other types of representative evolutionary algorithms and meta-heuristics. To validate the effectiveness of ML-CLPSO-AM in real-life applications, ML-CLPSO-AM was applied to economic load dispatch (ELD) problems.

Infrared Image Contrast Enhancement Based on Modified Particle Swarm Optimization

2013 ◽  
Vol 760-762 ◽  
pp. 1389-1393
Author(s):  
Ren Tao Zhao ◽  
You Yu Wang ◽  
Hua De Li ◽  
Jun Tie
Keyword(s):  
Particle Swarm Optimization ◽  
Contrast Enhancement ◽  
Particle Swarm ◽  
Infrared Image ◽  
Optimal Solution ◽  
Image Contrast ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Image Contrast Enhancement ◽  
Modified Pso

Adaptive infrared image contrast enhancement is presented based on modified particle swarm optimization (PSO) and incomplete Beta Function. On the basis of traditional PSO, modified PSO integrates into the theory of Multi-Particle Swarm and evolution theory algorithm. By using separate search space optimal solution of multiple particles, the global search ability is improved. And in the iteration procedures, timely adjustment of acceleration coefficients is convenient for PSO to find the global optimal solution in the later iteration. Through infrared image simulation, experimental results show that the modified PSO is better than the standard PSO in computing speed and convergence.

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