scholarly journals A comparative study of PSO, GSA and SCA in parameters optimization of surface grinding process

2019 ◽  
Vol 8 (3) ◽  
pp. 1117-1127
Author(s):  
Teh Muy Shin ◽  
Asrul Adam ◽  
Amar Faiz Zainal Abidin
Keyword(s):  
Optimization Methods ◽  
Pso Algorithm ◽  
Parameters Optimization ◽  
Surface Grinding ◽  
Grinding Process ◽  
Swarm Optimization ◽  
Optimum Parameters ◽  
Maximum Production Rate ◽  
The Cost ◽  
Selection Of

The selection of parameters in grinding process remains as a crucial role to guarantee that the machined product quality is at the minimum production cost and maximum production rate. Therefore, it is required to utilize more advance and effective optimization methods to obtain the optimum parameters and resulting an improvement on the grinding performance. In this paper, three optimization algorithms which are particle swarm optimization (PSO), gravitational search, and Sine Cosine algorithms are employed to optimize the grinding process parameters that may either reduce the cost, increase the productivity or obtain the finest surface finish and resulting a higher grinding process performance. The efficiency of the three algorithms are evaluated and comparedwith previous results obtained by other optimization methods on similar studies.The experimental results showed that PSO algorithm achieves better optimization performance in the aspect of convergence rate and accuracy of best solution.Whereas in the comparison of results of previous researchers, the obtained result of PSO proves that it is efficient in solving the complicated mathematical model of surface grinding process with different conditions.

Coverage Optimization Methods in Wireless Homo-Sensor Network Based on Guided Swarms

2011 ◽  
Vol 148-149 ◽  
pp. 868-874
Author(s):  
Huan Yang Zheng
Keyword(s):  
Sensor Network ◽  
Optimization Methods ◽  
Pso Algorithm ◽  
Search Process ◽  
Migration Process ◽  
Swarm Optimization ◽  
Network Position ◽  
Coverage Optimization ◽  
Position Problem ◽  
Grid Based

An improved particle swarm optimization (PSO) algorithm is designed for the grid based wireless homo-sensor network position problem. The proposed method, called guided method, introduces the simulation of migration process to PSO and its mutation algorithm, using a previous designed sparse position plan to guide the swarm to the optimization solution, and accelerates the search process. Experiments show not only the feasibility and validity of the proposed method but also a marked improvement in performance over traditional PSO.

scholarly journals Adaptive Phase-Shifted PWM based on Particle Swarm Optimization for Cascaded H-Bridge Inverter with Unequal Dc-link Voltages

2018 ◽  
Vol 7 (4.15) ◽  
pp. 469
Author(s):  
Pakedam Lare ◽  
Byamakesh Nayak ◽  
Srikanta Dash ◽  
Jiban Ballav Sahu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Industrial Applications ◽  
High Order ◽  
Control Technique ◽  
Voltage Source ◽  
Switching Frequency ◽  
Swarm Optimization ◽  
The Cost

The cascaded H-Bridge Multilevel Inverter has been found a promising technology in industrial applications because of its higher voltage with less distortion production. Various PWMs techniques have been proposed to push the harmonics frequencies higher than the switching frequency and thus reduces the THD as compared to non-carrier control technique based upon grid frequency. The Phase-Shifted PWM technique has an advantage over others PWM techniques because its harmonics orders are multiples of switching frequency and also depend on the number of levels of the inverter. The phase shifting angle is uniform when the equal voltage sources are adopted. However, in applications where sets of different voltage source levels feed the H-Bridge cells, the Phase Shifted PWM suffers its high order harmonics elimination capability. As a solution to alleviate this problem, an adaptive variable angle approach is proposed in this paper using Particle Swarm Optimization (PSO) algorithm to eliminate desired higher order harmonics. The algorithm is used to minimize the cost function based on high order sideband harmonics elimination equations. The results through MATLAB/Simulink environment shown in this paper confirm the reduction of sideband harmonics of higher orders, and the overall THD.  

A Glowworm Swarm Optimization Algorithm for Uninhabited Combat Air Vehicle Path Planning

2015 ◽  
Vol 24 (1) ◽  
pp. 69-83 ◽  
Author(s):  
Zhonghua Tang ◽  
Yongquan Zhou
Keyword(s):  
Path Planning ◽  
Heuristic Algorithms ◽  
Optimization Methods ◽  
Pso Algorithm ◽  
Population Based ◽  
Computational Accuracy ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Air Vehicle ◽  
The Individual

AbstractUninhabited combat air vehicle (UCAV) path planning is a complicated, high-dimension optimization problem. To solve this problem, we present in this article an improved glowworm swarm optimization (GSO) algorithm based on the particle swarm optimization (PSO) algorithm, which we call the PGSO algorithm. In PGSO, the mechanism of a glowworm individual was modified via the individual generation mechanism of PSO. Meanwhile, to improve the presented algorithm’s convergence rate and computational accuracy, we reference the idea of parallel hybrid mutation and local search near the global optimal location. To prove the performance of the proposed algorithm, PGSO was compared with 10 other population-based optimization methods. The experiment results show that the proposed approach is more effective in UCAV path planning than most of the other meta-heuristic algorithms.

scholarly journals Resolving the optimal selection of a natural reserve using the particle swarm optimisation by applying transfer functions

2018 ◽  
Author(s):  
Boris Almonacid
Keyword(s):  
Particle Swarm Optimization ◽  
Transfer Functions ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimal Selection ◽  
Metaheuristic Algorithm ◽  
Swarm Optimization ◽  
Natural Reserve ◽  
Transfer Method ◽  
Selection Of

The optimal selection of a natural reserve (OSRN) is an optimisation problem with a binary domain. To solve this problem the metaheuristic algorithm Particle Swarm Optimization (PSO) has been chosen. The PSO algorithm has been designed to solve problems in real domains. Therefore, a transfer method has been applied that converts the equations with real domains of the PSO algorithm into binary results that are compatible with the OSRN problem. Four transfer functions have been tested in four case studies to solve the OSRN problem. According to the tests carried out, it is concluded that two of the four transfer functions are apt to solve the problem of optimal selection of a natural reserve.

scholarly journals Resolving the optimal selection of a natural reserve using the particle swarm optimisation by applying transfer functions

2018 ◽  
Author(s):  
Boris L Almonacid
Keyword(s):  
Particle Swarm Optimization ◽  
Transfer Functions ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimal Selection ◽  
Metaheuristic Algorithm ◽  
Swarm Optimization ◽  
Natural Reserve ◽  
Transfer Method ◽  
Selection Of

The optimal selection of a natural reserve (OSRN) is an optimisation problem with a binary domain. To solve this problem the metaheuristic algorithm Particle Swarm Optimization (PSO) has been chosen. The PSO algorithm has been designed to solve problems in real domains. Therefore, a transfer method has been applied that converts the equations with real domains of the PSO algorithm into binary results that are compatible with the OSRN problem. Four transfer functions have been tested in four case studies to solve the OSRN problem. According to the tests carried out, it is concluded that two of the four transfer functions are apt to solve the problem of optimal selection of a natural reserve.

scholarly journals Robust Performance of PID Controller by using Artificial Intelligence

2018 ◽  
Author(s):  
Shachi Tiwary ◽  
Ashraf Jafri ◽  
Kushal Tiwari ◽  
Richa Tiwari ◽  
Chaman Yadav
Keyword(s):  
Pid Controller ◽  
Dynamic Behaviour ◽  
Design Method ◽  
Pso Algorithm ◽  
Proportional Integral Derivative ◽  
Robust Performance ◽  
Bacterial Foraging Optimization ◽  
Swarm Optimization ◽  
Modern Methods ◽  
Selection Of

This paper is meant to design method for determining the optimal proportional-integral-derivative (PID) controller parameters of plant system using the particle swarm optimization (PSO) algorithm and bacterial Foraging Optimization (BFO). There are several methods which are used to tune the controller parameters. They are categorized into two types known as classical methods and modern methods. In this paper the use of PSO method tuned the PID parameter to make them more general and to achieve the steady state error limit, also to improve the dynamic behaviour of the system. The performance and design criteria of automatic selection of controller constants are discussed below.

scholarly journals Experimental dataset to develop a parametric model based of DC geared motor in feeder machine

2019 ◽  
Vol 9 (3) ◽  
pp. 1576
Author(s):  
Azlan. W. M. ◽  
Salleh S. M. ◽  
Mahzan S ◽  
Sadikin A ◽  
Ahmad S
Keyword(s):  
Particle Swarm Optimization ◽  
System Identification ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Input Voltage ◽  
Parametric Model ◽  
Actual Data ◽  
Swarm Optimization ◽  
Experimental Dataset ◽  
Optimum Parameters

This paper presents the application of a System Identification based on Particle Swarm Optimization (PSO) technique to develop parametric model of experimental dataset of DC Geared motor in feeder machine. The experimental was conducted to measure the input (voltage) and output (speed) data. The actual data is used to be optimized using PSO algorithm. The parameter emphasized is Time, Man Square Error (MSE) and Average Time. One of the best model has been chosen based on the optimum parameters.

scholarly journals PENINGKATAN KINERJA ALGORITMA K MEANS DENGAN MENGGUNAKAN PARTICLE SWARM OPTIMIZATION DALAM PENGELOMPOKAN DATA PENYEDIAAN AKSES

2020 ◽  
Vol 6 (2) ◽  
pp. 213-227
Author(s):  
ARI YUNUS HENDRAWAN
Keyword(s):  
Water Quality ◽  
Pso Algorithm ◽  
Selection Method ◽  
Test Results ◽  
Community Based ◽  
Swarm Optimization ◽  
Number Of Clusters ◽  
The Third ◽  
A Value ◽  
Selection Of

           Water is one of the things that plays a very important role in human survival, because the Indonesian government has a community-based water supply and sanitation (PAMSIMAS) program, so that all the programs run well need a regional status grouping technique in this thesis. with the K-means algorithm. K-means is a partition algorithm that aims to divide the data into the specified number of clusters, the results of the K means algorithm depend on the selection of the initial klater center but problems that often occur when selecting the initial centroid are randomly drawn from the solution. from the grouping is not quite right. To overcome this problem the author wants to use the PSO algorithm in the initial centroid selector for the K-means algorithm, in this study also compared the selection of the first 3 centroids according to random, second according to government standards the value of high, medium and low drinking water quality then the third method proposed by the PSO algorithm was then tested with Davies Bouldin Index. From the test results, the K-means method with the selection of random initial centroid with a value of 0.208856082, the K-means method with the selection of centroids in accordance with government standards about SAM conditions of 0.280077 and the best selection method is K-means PSO 0, 08383. So testing the PAMSIMAS data using K-means PSO found that the method was more optimal.  

An improved PSO algorithm for solving nonlinear programing problems with constrained conditions

2020 ◽  
pp. 2150001
Author(s):  
Wei-Der Chang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Pso Algorithm ◽  
Engineering Optimization ◽  
Swarm Optimization ◽  
Design Variables ◽  
Constrained Conditions ◽  
Engineering Optimization Problems

Engineering optimization problems can be always classified into two main categories including the linear programming (LP) and nonlinear programming (NLP) problems. Each programming problem further involves the unconstrained conditions and constrained conditions for design variables of the optimized system. This paper will focus on the issue about the design problem of NLP with the constrained conditions. The employed method for such NLP problems is a variant of particle swarm optimization (PSO), named improved particle swarm optimization (IPSO). The developed IPSO is to modify the velocity updating formula of the algorithm to enhance the search ability for given optimization problems. In this work, many different kinds of physical engineering optimization problems are examined and solved via the proposed IPSO algorithm. Simulation results compared with various optimization methods reported in the literature will show the effectiveness and feasibility for solving NLP problems with the constrained conditions.

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