Search results for "Particle swarm optimization"
showing 10 items of 44 documents
Multi-application Based Fault-Tolerant Network-on-Chip Design for Mesh Topology Using Reconfigurable Architecture
2019
In this paper, we propose a two-step fault-tolerant approach to address the faults occurred in cores. In the first stage, a Particle Swarm Optimization (PSO) based approach has been proposed for the fault-tolerant mapping of multiple applications on to the mesh based reconfigurable architecture by introducing spare cores and a heuristic has been proposed for the reconfiguration in the second stage. The proposed approach has been experimented by taking several benchmark applications into consideration. Communication cost comparisons have been carried out by taking the failed cores as user input and the experimental results show that our approach could get improvements in terms of communicati…
Multi-application Based Network-on-Chip Design for Mesh-of-Tree Topology Using Global Mapping and Reconfigurable Architecture
2019
This paper outlines a multi-application mapping for Mesh-of-Tree (MoT) topology based Network-on-Chip (NoC) design using reconfigurable architecture. A two phase Particle Swarm Optimization (PSO) has been proposed for reconfigurable architecture to minimize the communication cost. In first phase global mapping is done by combining multiple applications and in second phase, reconfiguration is achieved by switching the cores to near by routers using multiplexers. Experimentations have been carried out for several application benchmarks and synthetic applications generated using TGFF tool. The results show significant improvement in terms of communication cost after reconfiguration.
Fault-Tolerant Network-on-Chip Design for Mesh-of-Tree Topology Using Particle Swarm Optimization
2018
As the size of the chip is scaling down the density of Intellectual Property (IP) cores integrated on a chip has been increased rapidly. The communication between these IP cores on a chip is highly challenging. To overcome this issue, Network-on-Chip (NoC) has been proposed to provide an efficient and a scalable communication architecture. In the deep sub-micron level NoCs are prone to faults which can occur in any component of NoC. To build a reliable and robust systems, it is necessary to apply efficient fault-tolerant techniques. In this paper, we present a flexible spare core placement in Mesh-of-Tree (MoT) topology using Particle Swarm Optimization (PSO) by considering IP core failures…
Torus Topology based Fault-Tolerant Network-on-Chip Design with Flexible Spare Core Placement
2018
The increase in the density of the IP cores being fabricated on a chip poses on-chip communication challenges and heat dissipation. To overcome these issues, Network-onChip (NoC) based communication architecture is introduced. In the nanoscale era NoCs are prone to faults which results in performance degradation and un-reliability. Hence efficient fault-tolerant methods are required to make the system reliable in contrast to diverse component failures. This paper presents a flexible spare core placement in torus topology based faulttolerant NoC design. The communications related to the failed core is taken care by selecting the best position for a spare core in the torus network. By conside…
Adjusted bat algorithm for tuning of support vector machine parameters
2016
Support vector machines are powerful and often used technique of supervised learning applied to classification. Quality of the constructed classifier can be improved by appropriate selection of the learning parameters. These parameters are often tuned using grid search with relatively large step. This optimization process can be done computationally more efficiently and more precisely using stochastic search metaheuristics. In this paper we propose adjusted bat algorithm for support vector machines parameter optimization and show that compared to the grid search it leads to a better classifier. We tested our approach on standard set of benchmark data sets from UCI machine learning repositor…
Applying particle swarm optimization to the motion-cueing-algorithm tuning problem
2017
The MCA tuning problem consists in finding the best values for the parameters/coefficients of Motion Cueing Algorithms (MCA). MCA are used to control the movements of robotic motion platforms employed to generate inertial cues in vehicle simulators. This problem is traditionally approached with a manual pilot-in-the-loop subjective tuning, based on the opinion of several pilots/drivers. Instead, this paper proposes applying Particle Swarm Optimization (PSO) to solve this problem, using simulated motion platforms and objective indicators rather than subjective opinions. Results show that PSO-based tuning can provide a suitable solution for this complex optimization problem.
Binding mode analysis of ABCA7 for the prediction of novel Alzheimer's disease therapeutics
2021
Graphical abstract
Hybrid Particle Swarm Optimization With Genetic Algorithm to Train Artificial Neural Networks for Short-Term Load Forecasting
2019
This research proposes a new training algorithm for artificial neural networks (ANNs) to improve the short-term load forecasting (STLF) performance. The proposed algorithm overcomes the so-called training issue in ANNs, where it traps in local minima, by applying genetic algorithm operations in particle swarm optimization when it converges to local minima. The training ability of the hybridized training algorithm is evaluated using load data gathered by Electricity Generating Authority of Thailand. The ANN is trained using the new training algorithm with one-year data to forecast equal 48 periods of each day in 2013. During the testing phase, a mean absolute percentage error (MAPE) is used …
Artificial Decision Maker Driven by PSO : An Approach for Testing Reference Point Based Interactive Methods
2018
Over the years, many interactive multiobjective optimization methods based on a reference point have been proposed. With a reference point, the decision maker indicates desirable objective function values to iteratively direct the solution process. However, when analyzing the performance of these methods, a critical issue is how to systematically involve decision makers. A recent approach to this problem is to replace a decision maker with an artificial one to be able to systematically evaluate and compare reference point based interactive methods in controlled experiments. In this study, a new artificial decision maker is proposed, which reuses the dynamics of particle swarm optimization f…
Memetic Algorithms in Continuous Optimization
2012
Intuitively, a set is considered to be discrete if it is composed of isolated elements, whereas it is considered to be continuous if it is composed of infinite and contiguous elements and does not contain “holes”.