A Comparative Study of Metaheuristic Methods for Transmission Network Expansion Planning

Transmission network expansion planning is a very complex and computationally demanding problem due to the discrete nature of the optimization variables. This complexity has increased even more in a restructured deregulated environment. In this regard, there is a need for development of more rigorous optimization techniques. This paper presents a comparative analysis of three metaheuristic algorithms known as Bacteria foraging (BF), Genetic algorithm (GA), and Particle swarm optimization (PSO) for transmission network expansion planning with and without security constraints. The DC power flow based model is used for analysis and results for IEEE 24 bus system are obtained with the above three metaheuristic drawing a comparison of their performance characteristic.

Application of Machine Learning Techniques to Predict Software Reliability

In this paper, the authors employed machine learning techniques, specifically, Back propagation trained neural network (BPNN), Group method of data handling (GMDH), Counter propagation neural network (CPNN), Dynamic evolving neuro–fuzzy inference system (DENFIS), Genetic Programming (GP), TreeNet, statistical multiple linear regression (MLR), and multivariate adaptive regression splines (MARS), to accurately forecast software reliability. Their effectiveness is demonstrated on three datasets taken from literature, where performance is compared in terms of normalized root mean square error (NRMSE) obtained in the test set. From rigorous experiments conducted, it was observed that GP outperformed all techniques in all datasets, with GMDH coming a close second.

A Heuristic Approach for Multi Objective Distribution Feeder Reconfiguration

The reconfiguration is an operation process used for optimization with specific objectives by means of changing the status of switches in a distribution network. This paper presents an algorithm for network recon-figuration based on the heuristic rules and fuzzy multi objective approach where each objective is normalized with inspiration from fuzzy set to cause optimization more flexible and formulized as a unique multi objective function. Also, the genetic algorithm is used for solving the suggested model, in which there is no risk of non-linear objective functions and constraints. The effectiveness of the proposed method is demonstrated through several examples in this paper.

Three Novel Methods to Predict Traffic Time Series in Reconstructed State Spaces

This article proposes three novel methods—temporal confined (TC), spatiotemporal confined (STC) and spatial confined (SC)—to forecast the temporal evolution of traffic parameters. The fundamental rationales are to embed one-dimensional traffic time series into reconstructed state spaces and then to perform fuzzy reasoning to infer the future changes in traffic series. The TC, STC and SC methods respectively employ different fuzzy reasoning logics to select similar historical traffic trajectories. Theil inequality coefficient and its decomposed components are used to evaluate the predicting power and source of errors. Field observed one-minute traffic counts are used to test the predicting power. The results show that overall prediction accuracies for the three methods are satisfactorily high with small systematic errors and little deviation from the observed data. It suggests that the proposed three methods can be used to capture and forecast the short-term (e.g., one-minute) temporal evolution of traffic parameters.

Ant Custering Algorithms

This paper proposes three ant clustering algorithms (ACAs): ACA-1, ACA-2 and ACA-3. The core logic of the proposed ACAs is to modify the ant colony metaheuristic by reformulating the clustering problem into a network problem. For a clustering problem of N objects and K clusters, a fully connected network of N nodes is formed with link costs, representing the dissimilarity of any two nodes it connects. K ants are then to collect their own nodes according to the link costs and following the pheromone trail laid by previous ants. The proposed three ACAs have been validated on a small-scale problem solved by a total enumeration method. The solution effectiveness at different problem scales consistently shows that ACA-2 outperforms among these three ACAs. A further comparison of ACA-2 with other commonly used clustering methods, including agglomerative hierarchy clustering algorithm (AHCA), K-means algorithm (KMA) and genetic clustering algorithm (GCA), shows that ACA-2 significantly outperforms them in solution effectiveness for the most of cases and also performs considerably better in solution stability as the problem scales or the number of clusters gets larger.

Using Evolution Strategies to Perform Stellar Population Synthesis for Galaxy Spectra from SDSS

In this work, the authors employ Evolution Strategies (ES) to automatically extract a set of physical parameters, corresponding to stellar population synthesis, from a sample of galaxy spectra taken from the Sloan Digital Sky Survey (SDSS). This parameter extraction is presented as an optimization problem and being solved using ES. The idea is to reconstruct each galaxy spectrum by means of a linear combination of three different theoretical models for stellar population synthesis. This combination produces a model spectrum that is compared with the original spectrum using a simple difference function. The goal is to find a model that minimizes this difference, using ES as the algorithm to explore the parameter space. This paper presents experimental results using a set of 100 spectra from SDSS Data Release 2 that show that ES are very well suited to extract stellar population parameters from galaxy spectra. Additionally, in order to better understand the performance of ES in this problem, a comparison with two well known stochastic search algorithms, Genetic Algorithms (GA) and Simulated Annealing (SA), is presented.

A Novel Puzzle Based Compaction (PBC) Strategy for Enhancing the Utilization of Reconfigurable Resources

Partially reconfigurable field programmable gate arrays (FPGAs) can accommodate several independent tasks simultaneously. FPGA, as all reconfigurable chips, relies on the “host-then-compact-when-needed” strategy. Accordingly, it should have the ability to both place incoming tasks at run time and compact the chip whenever needed. Compaction is a proposed solution to alleviate external fragmentations problem, trying to move running tasks closer to each other in order to free a sufficient area for new tasks. However, compaction conditions the suspension of the running tasks, which introduces a high penalty. In order to increase the chip area utilization as well as not affecting the response times of tasks, efficient compaction techniques become increasingly important. Unfortunately, traditional compaction techniques suffer from a variety of faults. This paper introduces a novel Puzzle Based Compaction (PBC) technique that is a shape aware technique, which takes the tasks shapes into consideration. In this regard, it succeeded not only to eliminate the internal fragmentations but also to minimize the external fragmentations. This paper develops a novel formula, which is the first not to estimate, but to exactly calculate the amount of external fragmentations generated by accommodating a set of tasks inside the reconfigurable chip.

Posterior Sampling using Particle Swarm Optimizers and Model Reduction Techniques

Inverse problems are ill-posed and posterior sampling is a way of providing an estimate of the uncertainty based on a finite set of the family of models that fit the observed data within the same tolerance. Monte Carlo methods are used for this purpose but are highly inefficient. Global optimization methods address the inverse problem as a sampling problem, particularly Particle Swarm, which is a very interesting algorithm that is typically used in an exploitative form. Although PSO has not been designed originally to perform importance sampling, the authors show practical applications in the domain of environmental geophysics, where it provides a proxy for the posterior distribution when it is used in its explorative form. Finally, this paper presents a hydrogeological example how to perform a similar task for inverse problems in high dimensional spaces through the combined use with model reduction techniques.

A Scheduling Model with Multi-Objective Optimization for Computational Grids using NSGA-II

Scheduling a job on the grid is an NP Hard problem, and hence a number of models on optimizing one or other characteristic parameters have been proposed in the literature. It is expected from a computational grid to complete the job quickly in most reliable grid environment owing to the number of participants in the grid and the scarcity of the resources available. Genetic algorithm is an effective tool in solving problems that requires sub-optimal solutions and finds uses in multi-objective optimization problems. This paper addresses a multi-objective optimization problem by introducing a scheduling model for a modular job on a computational grid with a dual objective, minimizing the turnaround time and maximizing the reliability of the job execution using NSGA – II, a GA variant. The cost of execution on a node is measured on the basis of the node characteristics, the job attributes and the network properties. Simulation study and a comparison of the results with other similar models reveal the effectiveness of the model.

Comparative Study of Evolutionary Computing Methods for Parameter Estimation of Power Quality Signals

Recently utilities and end users become more concerned about power quality issues because the load equipments are more sensitive to various power quality disturbances, such as harmonics and voltage fluctuation. Harmonic distortion and voltage flicker are the major causes in growing concern about electric power quality. Power quality disturbance monitoring plays an important role in the deregulated power market scenario due to competitiveness among the utilities. This paper presents an evolutionary algorithm approach based on Adaptive Particle Swarm Optimization (APSO) to determine the amplitude, phase and frequency of a power quality signal. In this APSO algorithm the time varying inertia weight is modified as rank based, and re-initialization is used to increase the diversity. In this paper, to the authors highlight the efficacy of different evolutionary optimization techniques like classical PSO, Constriction based PSO, Clonal Algorithm (CLONALOG), Adaptive Bacterial Foraging (ABF) and the proposed Adaptive Particle Swarm Optimization (APSO) to extract different parameters like amplitude, phase and frequency of harmonic distorted power quality signal and voltage flicker.