scholarly journals Particle swarm optimization to produce optimal solution

2018 ◽  
Vol 7 (1.7) ◽  
pp. 210 ◽  
Author(s):  
C Saranya Jothi ◽  
V Usha ◽  
R Nithya
Keyword(s):  
Particle Swarm Optimization ◽  
Test Data ◽  
Fitness Function ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Optimal Test ◽  
Swarm Optimization ◽  
Test Information ◽  
Fitness Value

Search-Based Software Testing is the utilization of a meta-heuristic improving scan procedure for the programmed age of test information. Particle Swarm Optimization (PSO) is one of those technique. It can be used in testing to generate optimal test data solution based on an objective function that utilises branch coverage as criteria. Software under test is given as input to the algorithm. The problem becomes a minimization problem where our aim is to obtain test data with minimum fitness value. This is called the ideal test information for the given programming under test. PSO algorithm is found to outperform most of the optimization techniques by finding least value for fitness function. The algorithm is applied to various software under tests and checked whether it can produce optimal test data. Parameters are tuned so as to obtain better results.

scholarly journals Design of Gas Cyclone Using Hybrid Particle Swarm Optimization Algorithm

2021 ◽  
Vol 11 (20) ◽  
pp. 9772
Author(s):  
Xueli Shen ◽  
Daniel C. Ihenacho
Keyword(s):  
Particle Swarm Optimization ◽  
Research Effort ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Design Parameters ◽  
Initial Population ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization ◽  
Fitness Value ◽  
Sensitivity Approach

The method of searching for an optimal solution inspired by nature is referred to as particle swarm optimization. Differential evolution is a simple but effective EA for global optimization since it has demonstrated strong convergence qualities and is relatively straightforward to comprehend. The primary concerns of design engineers are that the traditional technique used in the design process of a gas cyclone utilizes complex mathematical formulas and a sensitivity approach to obtain relevant optimal design parameters. The motivation of this research effort is based on the desire to simplify complex mathematical models and the sensitivity approach for gas cyclone design with the use of an objective function, which is of the minimization type. The process makes use of the initial population generated by the DE algorithm, and the stopping criterion of DE is set as the fitness value. When the fitness value is not less than the current global best, the DE population is taken over by PSO. For each iteration, the new velocity and position are updated in every generation until the optimal solution is achieved. When using PSO independently, the adoption of a hybridised particle swarm optimization method for the design of an optimum gas cyclone produced better results, with an overall efficiency of 0.70, and with a low cost at the rate of 230 cost/second.

scholarly journals Research on Timetabling Problems Based on Particle Swarm Optimization Algorithm

2012 ◽  
Vol 6-7 ◽  
pp. 736-741
Author(s):  
Xin Min Ma ◽  
Lin Li Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Educational System ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Course Timetabling ◽  
Fitness Value ◽  
Course Timetabling Problem

A new algorithm for timetabling based on particle swarm optimization algorithm was proposed, and the key problems such as particle coding, fitness function fabricating, particle swarm initialization and crossover operation were settled. The fitness value declines when the evolution generation increases. The results showed that it was a good solution for course timetabling problem in the educational system.

scholarly journals Optimization Algorithm for Multiple Phases Sectionalized Modulation Jamming Based on Particle Swarm Optimization

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 160 ◽  
Author(s):  
Jiawei Jiang ◽  
Yanhong Wu ◽  
Hongyan Wang ◽  
Yakun Lv ◽  
Lei Qiu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Random Parameters ◽  
Simulation Experiments ◽  
Swarm Optimization ◽  
Fitness Value ◽  
The Relationship ◽  
Jamming Effect

Due to the difficulty in deducing the corresponding relationship between results and parameter settings of multiple phases sectionalized modulation (MPSM) jamming, a problem occurs when obtaining the optimal local suppression jamming effect, which limits the practical application of MPSM jamming. The traditional method struggles to meet the requirements by setting fixed parameters or random parameters. Therefore, an optimization algorithm for MPSM jamming based on particle swarm optimization (PSO) is proposed in this study to produce the optimal local suppression jamming effect and determine its corresponding parameter settings. First, we analyzed the relationship between the degree of mismatch and local suppression jamming effect. Then, we set appropriate fitness function and fitness value. Finally, we used PSO to calculate parameter settings of a section situation and phase situation, which minimizes the fitness function and fitness value. The optimization algorithm avoids the tremendous computation of traversing all parameter settings, is stable, the results are repeatable, and the algorithm provides the optimal local suppression jamming effect under different conditions. The simulation experiments demonstrate the feasibility and effectiveness of the optimization algorithm.

scholarly journals Optimal Load Distribution of Thermal Generating Units using Particle Swarm Optimization (PSO)

2019 ◽  
Vol 8 (2S7) ◽  
pp. 440-444
Keyword(s):  
Particle Swarm Optimization ◽  
Population Size ◽  
Conventional Method ◽  
Fitness Function ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Swarm Optimization ◽  
Load Planning ◽  
Optimal Load ◽  
Reliable Solution

This paper shows load planning of two thermal generating units feeding a load of 200 MW using Particle swarm optimization (PSO). The PSO involves selection of population size or number of particle, fitness function. The advantages of PSO over conventional method are better and reliable solution, better convergence rate. Initially fitness function, population size corresponding to each variable are decided, thereafter PSO is used for finding most optimal solution of the generating units under different set of iteration. The performance of system using PSO is compared with conventional method in terms of the tolerance band.

scholarly journals An optimal solution for software testing case generation based on particle swarm optimization

Open Physics ◽  
2018 ◽  
Vol 16 (1) ◽  
pp. 355-363 ◽  
Author(s):  
Shi Jianqi ◽  
Huang Yanhong ◽  
Li Ang ◽  
Cai Fangda
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Software Testing ◽  
Fitness Function ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Population Diversity ◽  
Generation Efficiency ◽  
Swarm Optimization ◽  
Testing Case

AbstractSearching based testing case generation technology converts the problem of testing case generation to function optimizations, through a fitness function, which is usually optimized using heuristic search algorithms. The particle swarm optimization (PSO) optimized testing case generation algorithm tends to lose population diversity of locally optimal solutions with low accuracy of local search. To overcome the above defects, a self-adaptive PSO based software testing case optimization algorithm is proposed. It adjusts the inertia weight dynamically according to the current iteration and average relative speed, to improve the performance of standard PSO. An improved alternating variable method is put forward to accelerate local search speed, which can coordinate both global and local search ability thereby improving the overall generation efficiency of testing cases. The experimental results demonstrate that the approach outlined here keeps higher testing case generation efficiency, and it shows certain advantages in coverage, evolution generation amount and running time when compared to standard PSO and GA-PSO.

A Hybrid Meta-Heuristic Approach for QoS-Aware Cloud Service Composition

2018 ◽  
Vol 15 (2) ◽  
pp. 1-20 ◽  
Author(s):  
S. Bharath Bhushan ◽  
Pradeep C. H. Reddy
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fruit Fly ◽  
Convergence Speed ◽  
Optimization Techniques ◽  
Cloud Services ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization ◽  
Fitness Value

Cloud is evolving as an outstanding platform to deliver cloud services on a pay-as-you-go basis. The selection and composition of cloud services based on QoS criteria is formulated as NP hard optimization problem. Traditionally, many optimization techniques are applied to solve it, but it suffers from slow convergence speed, large number of calculations, and falling into local optimum. This article proposes a hybrid particle swarm optimization (HPSO) technique that combines particle swarm optimization (PSO) and fruit fly (FOA) to perform the evolutionary search process. The following determines a pareto optimal service set which is non-dominated solution set as input to the proposed HPSO. In the proposed HPSO, the parameters such as position and velocity are redefined, and while updating, the smell operator of fruit fly is used to overcome the prematurity of PSO. The FOA enhances the convergence speed with good fitness value. The experimental results show that the proposed HPSO outperforms the simple particle swarm optimization in terms of fitness value, execution time, and error rate.

Study of Simulation Test Generation Method Based on Improved Binary Particle Swarm Optimization Algorithm

2013 ◽  
Vol 823 ◽  
pp. 661-664
Author(s):  
Guang Yao Lian ◽  
Peng Cheng Yan ◽  
Jiang Sheng Sun ◽  
Kao Li Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Test Generation ◽  
Particle Swarm Optimization Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Binary Particle Swarm Optimization ◽  
Simulation Test ◽  
Swarm Optimization

To solve the backdating problem of traditional test generation methods, it puts forward a new test generation method based on improved binary particle swarm optimization algorithm in the paper. It estates the fitness function of test vector and faults in the circuits, and the optimal solution is the maximal value of the function. The experimentations prove that the method can reduce the compute quantity of test generation.

CHARACTERIZATION OF RECYCLED RUBBER USING PARTICLE SWARM OPTIMIZATION TECHNIQUES

2015 ◽  
Vol 88 (3) ◽  
pp. 343-358 ◽  
Author(s):  
I. Uriarte ◽  
E. Zulueta ◽  
T. Guraya ◽  
M. Arsuaga ◽  
I. Garitaonandia ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Viscoelastic Model ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Maxwell Model ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Compression Tests ◽  
Swarm Optimization ◽  
Recycled Rubber

ABSTRACT A material based on recycled rubber has been developed to use as a protective coating on road barriers with the aim of improving motorcyclists' security against crash impacts. This material is based on grounded rubber from used tires added by extrusion using low-density polyethylene as adhesive. Compression tests have been performed for different densities of the recycled material to fully describe the mechanical characteristics under high strain rates (in the rank 0.057–5.7 s−1), and a constitutive model composed of a hyperelastic Mooney Rivlin part and a viscoelastic part based on the generalized Maxwell model has been taken to characterize this behavior. Hyperelastic parameters have been obtained by means of the least-squares fitting technique, and particle swarm optimization (PSO) has been used to obtain viscoelastic parameters. The PSO algorithm is shown to be a good optimization method, simple, versatile, and consisting of few parameters that accelerate to the optimal solution. Therefore, this article presents a new and efficient approach to obtaining the parameters for the viscoelastic model. The behavior of the experimental material confirms the theoretically obtained results, so the procedure presented in the article is validated successfully.

A Comparative Study of Genetic and Particle Swarm Optimization Algorithms and Their Hybrid Method in Water Flooding Optimization

2018 ◽  
Vol 140 (10) ◽  
Author(s):  
Majid Siavashi ◽  
Mohsen Yazdani
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Newton Method ◽  
Oil Recovery ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Water Flooding ◽  
Swarm Optimization

Optimization of oil production from petroleum reservoirs is an interesting and complex problem which can be done by optimal control of well parameters such as their flow rates and pressure. Different optimization techniques have been developed yet, and metaheuristic algorithms are commonly employed to enhance oil recovery projects. Among different metaheuristic techniques, the genetic algorithm (GA) and the particle swarm optimization (PSO) have received more attention in engineering problems. These methods require a population and many objective function calls to approach more the global optimal solution. However, for a water flooding project in a reservoir, each function call requires a long time reservoir simulation. Hence, it is necessary to reduce the number of required function evaluations to increase the rate of convergence of optimization techniques. In this study, performance of GA and PSO are compared with each other in an enhanced oil recovery (EOR) project, and Newton method is linked with PSO to improve its convergence speed. Furthermore, hybrid genetic algorithm-particle swarm optimization (GA-PSO) as the third optimization technique is introduced and all of these techniques are implemented to EOR in a water injection project with 13 decision variables. Results indicate that PSO with Newton method (NPSO) is remarkably faster than the standard PSO (SPSO). Also, the hybrid GA-PSO method is more capable of finding the optimal solution with respect to GA and PSO. In addition, GA-PSO, NPSO, and GA-NPSO methods are compared and, respectively, GA-NPSO and NPSO showed excellence over GA-PSO.

Prediction for Gas Emission Quantity of the Working Face Based on LSSVM Optimized by Improved Particle Swarm Optimization

2014 ◽  
Vol 1051 ◽  
pp. 1028-1031
Author(s):  
Yu Xi Feng ◽  
Kai Zhi Zhang ◽  
Xi Zhan Yu ◽  
Qing Zhi Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Time Series Prediction ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Least Square ◽  
Support Vector ◽  
Gas Emission ◽  
Swarm Optimization ◽  
Working Face

Gas emission quantity may forecast the quantity of gas inside the coal, which has important significance for predicting the outburst of gas, but the problem always has not been well solved. Traditional Particle swarm optimization (PSO) algorithm lacks the ability to track the optimal solution while the fitness function changes. An improved algorithm named Time Variant PSO (TVPSO) was proposed to track the optimal solution online. Then it was used to choose the parameters of Least Square Support Vector Machine (LSSVM), which could avoid the man-made blindness and enhance the efficiency of online forecasting. The TVPSO-LSSVM method is based on the minimum structure risk of SVM and the globally optimizing ability of TVPSO to forecast continuously the gas emission quantity of the working face. The method was applied to solve the problem of nonlinear chaos time series prediction. Result shows that the method satisfies the need of online forecasting.

Sign in / Sign up

Export Citation Format

Share Document