Michigan Particle Swarm Optimization for Prototype Reduction in Classification Problems

This paper presents a new approach to Particle Swarm Optimization, called Michigan Approach PSO (MPSO), and its applica- tion to continuous classi cation problems as a Nearest Prototype (NP) classi er. In Nearest Prototype classi ers, a collection of prototypes has to be found that accurately represents the input patterns. The classi er then assigns classes based on the nearest prototype in this collection. The MPSO algorithm is used to process training data to nd those prototypes. In the MPSO algorithm each particle in a swarm represents a single pro- totype in the solution and it uses modi ed movement rules with particle competition and cooperation that ensure particle diversity. The proposed method is tested both with arti cial problems and with real benchmark problems and compared with several algorithms of the same family. Re- sults show that the particles are able to recognize clusters, nd decision boundaries and reach stable situations that also retain adaptation po- tential. The MPSO algorithm is able to improve the accuracy of 1-NN classi ers, obtains results comparable to the best among other classi ers, and improves the accuracy reported in literature for one of the problems.Publicad

Building nearest prototype classifiers using a Michigan approach PSO

IEEE Swarm Intelligence Symposium. Honolulu, HI, 1-5 april 2007This paper presents an application of particle swarm optimization (PSO) to continuous classification problems, using a Michigan approach. In this work, PSO is used to process training data to find a reduced set of prototypes to be used to classify the patterns, maintaining or increasing the accuracy of the nearest neighbor classifiers. The Michigan approach PSO represents each prototype by a particle and uses modified movement rules with particle competition and cooperation that ensure particle diversity. The result is that the particles are able to recognize clusters, find decision boundaries and achieve stable situations that also retain adaptation potential. The proposed method is tested both with artificial problems and with three real benchmark problems with quite promising results

AMPSO: A new Particle Swarm Method for Nearest Neighborhood Classification

Nearest prototype methods can be quite successful on many pattern classification problems. In these methods, a collection of prototypes has to be found that accurately represents the input patterns. The classifier then assigns classes based on the nearest prototype in this collection. In this paper, we first use the standard particle swarm optimizer (PSO) algorithm to find those prototypes. Second, we present a new algorithm, called adaptive Michigan PSO (AMPSO) in order to reduce the dimension of the search space and provide more flexibility than the former in this application. AMPSO is based on a different approach to particle swarms as each particle in the swarm represents a single prototype in the solution. The swarm does not converge to a single solution; instead, each particle is a local classifier, and the whole swarm is taken as the solution to the problem. It uses modified PSO equations with both particle competition and cooperation and a dynamic neighborhood. As an additional feature, in AMPSO, the number of prototypes represented in the swarm is able to adapt to the problem, increasing as needed the number of prototypes and classes of the prototypes that make the solution to the problem. We compared the results of the standard PSO and AMPSO in several benchmark problems from the University of California, Irvine, data sets and find that AMPSO always found a better solution than the standard PSO. We also found that it was able to improve the results of the Nearest Neighbor classifiers, and it is also competitive with some of the algorithms most commonly used for classification.This work was supported by the Spanish founded research Project MSTAR::UC3M, Ref: TIN2008-06491-C04-03 and CAM Project CCG06-UC3M/ESP-0774.Publicad

An adaptive Michigan approach PSO for nearest prototype classification

Proceedings of: Second International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2007, La Manga del Mar Menor, Spain, June 18-21, 2007.Nearest Prototype methods can be quite successful on many pattern classification problems. In these methods, a collection of prototypes has to be found that accurately represents the input patterns. The classifier then assigns classes based on the nearest prototype in this collection. In this paper we develop a new algorithm (called AMPSO), based on the Particle Swarm Optimization (PSO) algorithm, that can be used to find those prototypes. Each particle in a swarm represents a single prototype in the solution; the swarm evolves using modified PSO equations with both particle competition and cooperation. Experimentation includes an artificial problem and six common application problems from the UCI data sets. The results show that the AMPSO algorithm is able to find solutions with a reduced number of prototypes that classify data with comparable or better accuracy than the 1-NN classifier. The algorithm can also be compared or improves the results of many classical algorithms in each of those problems; and the results show that AMPSO also performs significantly better than any tested algorithm in one of the problems.This article has been financed by the Spanish founded research MEC project OPLINK::UC3M, Ref: TIN2005-08818-C04-02 and CAM project UC3M-TEC-05-029

Optimization of the Superconducting Linear Magnetic Bearing of a Maglev Vehicle

Considering the need for cost/performance prediction and optimization of superconducting maglev vehicles, we develop and validate here a 3D finite element model to simulate superconducting linear magnetic bearings. Then we reduce the 3D model to a 2D model in order to decrease the computing time. This allows us to perform in a reasonable time a stochastic optimization considering the superconductor properties and the vehicle operation. We look for the permanent magnet guideway geometry that minimizes the cost and maximizes the lateral force during a displacement sequence, with a constraint on the minimum levitation force. The displacement sequence reproduces a regular maglev vehicle operation with both vertical and lateral movements. For the sake of comparison, our reference is the SupraTrans prototype bearing. The results of the optimization suggest that the bearing cost could be substantially reduced, while keeping the same performances as the initial design. Alternatively, the performances could be significantly improved for the same original cost

Optimization of force-limiting seismic devices connecting structural subsystems

This paper is focused on the optimum design of an original force-limiting floor anchorage system for the seismic protection of reinforced concrete (RC) dual wall-frame buildings. This protection strategy is based on the interposition of elasto-plastic links between two structural subsystems, namely the lateral force resisting system (LFRS) and the gravity load resisting system (GLRS). The most efficient configuration accounting for the optimal position and mechanical characteristics of the nonlinear devices is obtained numerically by means of a modified constrained differential evolution algorithm. A 12-storey prototype RC dual wall-frame building is considered to demonstrate the effectiveness of the seismic protection strategy

Self-Learning Hot Data Prediction: Where Echo State Network Meets NAND Flash Memories

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Well understanding the access behavior of hot data is significant for NAND flash memory due to its crucial impact on the efficiency of garbage collection (GC) and wear leveling (WL), which respectively dominate the performance and life span of SSD. Generally, both GC and WL rely greatly on the recognition accuracy of hot data identification (HDI). However, in this paper, the first time we propose a novel concept of hot data prediction (HDP), where the conventional HDI becomes unnecessary. First, we develop a hybrid optimized echo state network (HOESN), where sufficiently unbiased and continuously shrunk output weights are learnt by a sparse regression based on L2 and L1/2 regularization. Second, quantum-behaved particle swarm optimization (QPSO) is employed to compute reservoir parameters (i.e., global scaling factor, reservoir size, scaling coefficient and sparsity degree) for further improving prediction accuracy and reliability. Third, in the test on a chaotic benchmark (Rossler), the HOESN performs better than those of six recent state-of-the-art methods. Finally, simulation results about six typical metrics tested on five real disk workloads and on-chip experiment outcomes verified from an actual SSD prototype indicate that our HOESN-based HDP can reliably promote the access performance and endurance of NAND flash memories.Peer reviewe