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.

Weight Coefficient Setting of Current Predictive Control for Permanent Magnet Synchronous Machine using HPSO

2021 ◽  
Vol 69 (4) ◽  
pp. 17-25
Author(s):  
Yinhang Luo ◽  
◽  
Fengyang Gao ◽  
Kaiwen Yang ◽  
◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Permanent Magnet ◽  
Predictive Control ◽  
Particle Swarm ◽  
Weight Coefficient ◽  
Switching Frequency ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization ◽  
Fitness Value

Aiming at the problem of multi-objective weight coefficient setting of model predictive control (MPC) for permanent magnet synchronous motor (PMSM), a hybrid particle swarm optimization (HPSO) algorithm with low computational complexity of fitness value is proposed to realize the self-setting of weight coefficient of cost function. In the proposed strategy, good particles update velocity and position through particle swarm optimization (PSO) algorithm, while bad particles not only do the same but generate the offspring by cross and mutation, and then the worse offspring will be replaced by their extremum individuals. It is faster that the adaptive cross and mutation rate makes the offspring get closer to the good particles, and it increases the diversity of particles without destroying the good particles. Experimental results show that compared with other optimization algorithms, the proposed algorithm. Firstly, is more inclined to escape from the local optimum. Secondly, it has higher search accuracy and faster convergence speed. Moreover, with setting weight coefficient, the system speed regulation time is shortened, the current total harmonic distortion (THD) is reduced significantly, and the switching frequency is effectively reduced without affecting the output power quality.

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.

An Improved Particle Swarm Optimization Algorithm with Adaptive Inertia Weights

2019 ◽  
Vol 18 (03) ◽  
pp. 833-866 ◽  
Author(s):  
Mi Li ◽  
Huan Chen ◽  
Xiaodong Wang ◽  
Ning Zhong ◽  
Shengfu Lu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Random Mutation ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Fitness Value ◽  
The One ◽  
Individual Particles

The particle swarm optimization (PSO) algorithm is simple to implement and converges quickly, but it easily falls into a local optimum; on the one hand, it lacks the ability to balance global exploration and local exploitation of the population, and on the other hand, the population lacks diversity. To solve these problems, this paper proposes an improved adaptive inertia weight particle swarm optimization (AIWPSO) algorithm. The AIWPSO algorithm includes two strategies: (1) An inertia weight adjustment method based on the optimal fitness value of individual particles is proposed, so that different particles have different inertia weights. This method increases the diversity of inertia weights and is conducive to balancing the capabilities of global exploration and local exploitation. (2) A mutation threshold is used to determine which particles need to be mutated. This method compensates for the inaccuracy of random mutation, effectively increasing the diversity of the population. To evaluate the performance of the proposed AIWPSO algorithm, benchmark functions are used for testing. The results show that AIWPSO achieves satisfactory results compared with those of other PSO algorithms. This outcome shows that the AIWPSO algorithm is conducive to balancing the abilities of the global exploration and local exploitation of the population, while increasing the diversity of the population, thereby significantly improving the optimization ability of the PSO algorithm.

Automatically Terminated Particle Swarm Optimization with Principal Component Analysis

2015 ◽  
Vol 14 (01) ◽  
pp. 171-194 ◽  
Author(s):  
Bun Theang Ong ◽  
Masao Fukushima
Keyword(s):  
Principal Component Analysis ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Principal Component ◽  
Component Analysis ◽  
Convergence Speed ◽  
Swarm Optimization ◽  
Termination Criteria ◽  
Hybrid Particle Swarm Optimization ◽  
Speed Accuracy

A hybrid Particle Swarm Optimization (PSO) that features an automatic termination and better search efficiency than classical PSO is presented. The proposed method is combined with the so-called "Gene Matrix" to provide the search with a self-check in order to determine a proper termination instant. Its convergence speed and reliability are also increased by the implementation of the Principal Component Analysis (PCA) technique and the hybridization with a local search method. The proposed algorithm is denominated as "Automatically Terminated Particle Swarm Optimization with Principal Component Analysis" (AT-PSO-PCA). The computational experiments demonstrate the effectiveness of the automatic termination criteria and show that AT-PSO-PCA enhances the convergence speed, accuracy and reliability of the PSO paradigm. Furthermore, comparisons with state-of-the-art evolutionary algorithms (EA) yield competitive results even under the automatically detected termination instant.

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 Application mapping and NoC configuration using hybrid particle swarm optimization

2021 ◽  
Author(s):  
Muhammad Obaidullah
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Search Space ◽  
Optimization Techniques ◽  
Movement Direction ◽  
Swarm Optimization ◽  
Application Mapping ◽  
Hybrid Particle Swarm Optimization ◽  
On Chip ◽  
Mapping Solution

Network-on-Chip (NoC) has been proposed as an interconnection framework for connecting large number of cores for a System-on-Chip (SoC). Assuming a mesh-based NoC, we investigate application mapping and NoC configuration optimization using a hybrid optimization scheme. Our technique, Hybrid Discrete Particle Swarm Optimization (HDPSO), combines Tabu-search, communication volume based core swapping, and swarm intelligence. We employ a Tabu-list to discourage swarm particles to re-visit the explored search space and propose an alternative route towards the intended movement direction. In each iteration of swarm, a sub-swarm containing configuration solutions (sub-particles) searches for optimal configuration for the parent particle (mapping solution). Optimization goals include minimum average communication latency, power, area, credit loop latency, and maximum average link duty factor. The proposed technique is tested for well-known multimedia application core graphs and several large synthetic cores-graphs. It was found that on average our hybrid scheme generates high quality NoC mapping and configuration solutions when compared to some existing stochastic optimization techniques.

Prediction of Heart Cancer Data Using Hybrid Optimization and Machine Learning Techniques

2021 ◽  
Vol 3 (1) ◽  
pp. 1-17
Author(s):  
Kalpdrum Passi ◽  
Prayushi Patel ◽  
Chakresh Kumar Jain
Keyword(s):  
Machine Learning ◽  
Particle Swarm Optimization ◽  
Cancer Diagnosis ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Cancer Type ◽  
Grey Wolf ◽  
Swarm Optimization ◽  
Cancer Data ◽  
Hybrid Particle Swarm Optimization

The traditional methods of cancer diagnosis and cancer-type recognition have quite a large number of limitations in terms of speed and accuracy. However, recent studies on cancer diagnosis are focused on molecular level identification so as to improve the capability of diagnosis process. By statistically analyzing the heart cancer datasets using a set of protocols and algorithms, gene expression profiles are efficiently analyzed. Various machine learning classifiers are used to classify the selected data. Cross-validation was performed to avoid overfitting and different ratios of training, and testing data was used to conclude the best optimization technique and classification algorithm for the heart cancer datasets. The data is optimized using optimization techniques like particle swarm optimization (PSO), grey wolf optimization (GWO), and hybrid particle swarm optimization with grey wolf optimizer (HPSOGWO). Results show an improvement in the prediction accuracy of heart cancer by the hybrid algorithm as compared to PSO and GWO algorithms.

scholarly journals Hybrid Particle Swarm Optimization and Its Application to Multimodal 3D Medical Image Registration

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Chen-Lun Lin ◽  
Aya Mimori ◽  
Yen-Wei Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Image Registration ◽  
Medical Image ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Optimization Approach ◽  
Medical Image Registration ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization

In the area of medical image analysis, 3D multimodality image registration is an important issue. In the processing of registration, an optimization approach has been applied to estimate the transformation of the reference image and target image. Some local optimization techniques are frequently used, such as the gradient descent method. However, these methods need a good initial value in order to avoid the local resolution. In this paper, we present a new improved global optimization approach named hybrid particle swarm optimization (HPSO) for medical image registration, which includes two concepts of genetic algorithms—subpopulation and crossover.

scholarly journals A New Auto Adaptive Fuzzy Hybrid Particle Swarm Optimization and Genetic Algorithm

2020 ◽  
Vol 10 (2) ◽  
pp. 95-111 ◽  
Author(s):  
Piotr Dziwiński ◽  
Łukasz Bartczuk ◽  
Józef Paszkowski
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Landscape ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Fuzzy Rules ◽  
Local Optimum ◽  
Genetic Operators ◽  
Adaptation Mechanism ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization

AbstractThe social learning mechanism used in the Particle Swarm Optimization algorithm allows this method to converge quickly. However, it can lead to catching the swarm in the local optimum. The solution to this issue may be the use of genetic operators whose random nature allows them to leave this point. The degree of use of these operators can be controlled using a neuro-fuzzy system. Previous studies have shown that the form of fuzzy rules should be adapted to the fitness landscape of the problem. This may suggest that in the case of complex optimization problems, the use of different systems at different stages of the algorithm will allow to achieve better results. In this paper, we introduce an auto adaptation mechanism that allows to change the form of fuzzy rules when solving the optimization problem. The proposed mechanism has been tested on benchmark functions widely adapted in the literature. The results verify the effectiveness and efficiency of this solution.

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