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.

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.

scholarly journals Data and Feature Reduction in Fuzzy Modeling through Particle Swarm Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
S. Sakinah S. Ahmad ◽  
Witold Pedrycz
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Search Space ◽  
Fuzzy Modeling ◽  
Optimization Techniques ◽  
Feature Reduction ◽  
Data Sets ◽  
Swarm Optimization

The study is concerned with data and feature reduction in fuzzy modeling. As these reduction activities are advantageous to fuzzy models in terms of both the effectiveness of their construction and the interpretation of the resulting models, their realization deserves particular attention. The formation of a subset of meaningful features and a subset of essential instances is discussed in the context of fuzzy-rule-based models. In contrast to the existing studies, which are focused predominantly on feature selection (namely, a reduction of the input space), a position advocated here is that a reduction has to involve both data and features to become efficient to the design of fuzzy model. The reduction problem is combinatorial in its nature and, as such, calls for the use of advanced optimization techniques. In this study, we use a technique of particle swarm optimization (PSO) as an optimization vehicle of forming a subset of features and data (instances) to design a fuzzy model. Given the dimensionality of the problem (as the search space involves both features and instances), we discuss a cooperative version of the PSO along with a clustering mechanism of forming a partition of the overall search space. Finally, a series of numeric experiments using several machine learning data sets is presented.

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.

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.

Optimizing Assembly Sequence Time Using Particle Swarm Optimization (PSO)

2013 ◽  
Vol 315 ◽  
pp. 88-92 ◽  
Author(s):  
Jameel A.A. Mukred ◽  
Mohd Taufiq Muslim ◽  
Hazlina Selamat
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Optimization Techniques ◽  
Assembly Sequence Planning ◽  
Assembly Sequence ◽  
Swarm Optimization ◽  
Large Numbers ◽  
Searching Ability

Assembly sequence planning (ASP) plays an important role in the production planning and should be optimized to minimize production time and cost when large numbers of parts and sub-assemblies are involved in the assembly process. Although the ASP problem has been tackled via a variety of optimization techniques, these techniques are often inefficient when applied to larger-scale problems. In this study, an approach using particle swarm optimization (PSO) is proposed to tackle one of the ASP problems which are optimizing the assembly sequence time. PSO uses a number of agents (particles) that constitute a swarm moving around in the search space looking for the best solution. Each bird, called particle, learns from its own best position and the globally best position. Experimental results show that PSO algorithm can produce good results in optimizing the assembly time, has a powerful global searching ability and fast rate of convergence.

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