Modern power systems require increased intelligence and flexibility in the control and optimization to ensure the capability of maintaining a generation-load balance, following serious disturbances. This issue is becoming more significant today due to the increasing number of microgrids (MGs). The MGs mostly use renewable energies in electrical power production that are varying naturally. These changes and usual uncertainties in power systems cause the classic controllers to be unable to provide a proper performance over a wide range of operating conditions. In response to this challenge, the present paper addresses a new online intelligent approach by using a combination of the fuzzy logic and the particle swarm optimization (PSO) techniques for optimal tuning of the most popular existing proportional-integral (PI) based frequency controllers in the ac MG systems. The control design methodology is examined on an ac MG case study. The performance of the proposed intelligent control synthesis is compared with the pure fuzzy PI and the Ziegler-Nichols PI control design methods.

Intelligent Frequency Control in an AC Microgrid: Online PSO-Based Fuzzy Tuning Approach

Most of web users use various search engines to get specific information. A key factor in the success of web search engines are their ability to rapidly find good quality results to the queries that are based on specific terms. This paper aims at retrieving more relevant documents from a huge corpus based on the required information. We propose a particle swarm optimization algorithm based on latent semantic indexing (PSO+LSI) for text clustering. PSO family of bio-inspired algorithms has recently successfully been applied to a number of real word clustering problems. We use an adaptive inertia weight (AIW) that do proper exploration and exploitation in search space. PSO can merge with LSI to achieve best clustering accuracy and efficiency. This framework provides more relevant documents to the user and reduces the irrelevant documents. It would be seen that for all numbers of dimensions, PSO+LSI are faster than PSO+Kmeans algorithms using vector space model (VSM). It takes 22.3 s for PSO+LSI method with 1000 terms to obtain its best performance on 150 dimensions.

	 

	Key words: Vector space model, particle swarm optimization (PSO) algorithm, latent semantic indexing, text clustering, adaptive inertia weight.

/pdf/text-clustering-on-latent-semantic-indexing-with-particle-1hrh16ibnu.pdf

Text clustering on latent semantic indexing with particle swarm optimization (PSO) algorithm

Grid computing is a high performance computing environment to solve larger scale computational demands. Grid computing contains resource management, task scheduling, security problems, information management and so on. Task scheduling is a fundamental issue in achieving high performance in grid computing systems. A computational GRID is typically heterogeneous in the sense that it combines clusters of varying sizes, and different clusters typically contains processing elements with different level of performance. In this, a heuristic approach based on particle swarm optimization algorithm is adopted for solving task scheduling problem in grid environment. Particle Swarm Optimization (PSO) is one of the latest evolutionary optimization techniques by nature. It has the better ability of global searching and has been successfully applied to many areas such as, neural network training etc. Due to the linear decreasing of inertia weight in PSO the convergence rate becomes faster, which leads to the minimal makespan time when used for scheduling. To make the convergence rate faster, the PSO algorithm is improved by modifying the inertia parameter, such that it produces better performance and gives an optimized result. Keyword : Inertia, position updation, velocity, grid computing.

Grid scheduling using enhanced pso algorithm

Scheduling heterogeneous tasks in a heterogeneous grid environment aims at effectively utilizing the resources and sharing the load among the available resources. Such a task assignment problem is NP-hard. This paper presents a Hybrid Particle Swarm Optimization - Genetic Algorithm (HPSO-GA) for solving the Task Assignment Problem. The novel Particle Swarm Optimization (PSO) implements GA operations such as crossover and mutation in PSO to improve effective resource utilization and complete tasks within deadline. The algorithm aims at distributing load among the heterogeneous resources in the grid environment based on their capacity. Analysis of data and computation intensive applications like web log processing and bioinformatics to achieve optimal performance is time consuming. Hence parallelization of optimization function is essential. Large-scale parallellisation of optimization function must also guarantee efficient communication, load balancing, fault tolerance and reliability. This paper presents a MapReduce HPSO-GA based on MapReduce parallel programming model. The HPSO-GA yields better results than normal PSO, provides better load balancing and resource utilization in grid environment. It identifies the exact node to which a task can be assigned in a Hadoop cluster. Hence, the proposed approach can be used in the resource management system of Hadoop along with Hadoop and system parameters to schedule jobs efficiently in a Hadoop cluster.

A novel parallel hybrid PSO-GA using MapReduce to schedule jobs in Hadoop data grids

Modern power systems require increased intelligenc e and flexibility in the control and optimization to ensure the capability of maintaining a generation-load balance, following serious disturbances. This issue is becoming more si gn ficant today due to the increasing number of Microgrids (MGs). The MGs mostly use renewable energies in electrical pow er production that are varying naturally. These changes and usual uncertainties in power systems cause the classic co ntrollers to be unable to provide a proper performance over a wide range of operating conditions. In response to this challenge , the present paper addresses a new online intelligent approach b y using a combination of the fuzzy logic and the particle swa rm optimization (PSO) techniques for optimal tuning of the most popular existing proportional-integral (PI) based frequency controllers in the ac MG systems. The control design methodology is examined on an ac MG case study. The performance of the proposed intelligent control syn thesis is compared with the pure fuzzy PI and the Ziegler–Nichols PI control design methods.

An On-Line PSO-Based Fuzzy Logic Tuning Approach: Microgrid Frequency Control Case Study

The original PSO usually converges prematurely, and falls into the local optimal solution. Aimed at the shortcoming of PSO, this paper put forward Improved PSO based on neighborhood cognizance (NCPSO) and improved NCPSO based on Swarm Decision (SDNCPSO). These two improved PSO can reduce the possibility of converging prematurely. The results of experiment prove that these two improved PSO can improve the performance of global convergence in PSO and make PSO converge to global optimal solution faster.

Improvement of Particle Swarm Optimization Based on Neighborhood Cognizance and Swarm Decision

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Improvement of Particle Swarm Optimization Based on Neighborhood Cognizance and Swarm Decision