Multi-objective evolutionary algorithm for single and multiple fault service restoration in large-scale distribution systems

Network reconfiguration for service restoration (SR) in distribution systems is a complex optimization problem. For large-scale distribution systems, it is computationally hard to find adequate SR plans in real time since the problem is combinatorial and non-linear, involving several constraints and objectives. Two Multi-Objective Evolutionary Algorithms that use Node-Depth Encoding (NDE) have proved able to efficiently generate adequate SR plans for large distribution systems: (i) one of them is the hybridization of the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) with NDE, named NSGA-N; (ii) the other is a Multi-Objective Evolutionary Algorithm based on subpopulation tables that uses NDE, named MEAN. Further challenges are faced now, i.e. the design of SR plans for larger systems as good as those for relatively smaller ones and for multiple faults as good as those for one fault (single fault). In order to tackle both challenges, this paper proposes a method that results from the combination of NSGA-N, MEAN and a new heuristic. Such a heuristic focuses on the application of NDE operators to alarming network zones according to technical constraints. The method generates similar quality SR plans in distribution systems of significantly different sizes (from 3860 to 30,880 buses). Moreover, the number of switching operations required to implement the SR plans generated by the proposed method increases in a moderate way with the number of faults.

Non-Dominated Sorting Genetic Algorithm Based on Reinforcement Learning to Optimization of Broad-Band Reflector Antennas Satellite

This paper aims to provide an improved NSGA-II (Non-Dominated Sorting Genetic Algorithm-version II) which incorporates a parameter-free self-tuning approach by reinforcement learning technique, called Non-Dominated Sorting Genetic Algorithm Based on Reinforcement Learning (NSGA-RL). The proposed method is particularly compared with the classical NSGA-II when applied to a satellite coverage problem. Furthermore, not only the optimization results are compared with results obtained by other multiobjective optimization methods, but also guarantee the advantage of no time-spending and complex parameter tuning.

A complex networks approach for data clustering

This work proposes a method for data clustering based on complex networks theory. A data set is represented as a network by considering different metrics to establish the connection between each pair of objects. The clusters are obtained by taking into account five community detection algorithms. The network-based clustering approach is applied in two real-world databases and two sets of artificially generated data. The obtained results suggest that the exponential of the Minkowski distance is the most suitable metric to quantify the similarities between pairs of objects. In addition, the community identification method based on the greedy optimization provides the best cluster solution. We compare the network-based clustering approach with some traditional clustering algorithms and verify that it provides the lowest classification error rate. (C) 2012 Elsevier B.V. All rights reserved.

Multiobjective Biogeography-Based Optimization Based on Predator-Prey Approach

Biogeography is the science that studies the geographical distribution and the migration of species in an ecosystem. Biogeography-based optimization (BBO) is a recently developed global optimization algorithm as a generalization of biogeography to evolutionary algorithm and has shown its ability to solve complex optimization problems. BBO employs a migration operator to share information between the problem solutions. The problem solutions are identified as habitat, and the sharing of features is called migration. In this paper, a multiobjective BBO, combined with a predator-prey (PPBBO) approach, is proposed and validated in the constrained design of a brushless dc wheel motor. The results demonstrated that the proposed PPBBO approach converged to promising solutions in terms of quality and dominance when compared with the classical BBO in a multiobjective version.

Evolving decision trees with beam search-based initialization and lexicographic multi-objective evaluation

Decision tree induction algorithms represent one of the most popular techniques for dealing with classification problems. However, traditional decision-tree induction algorithms implement a greedy approach for node splitting that is inherently susceptible to local optima convergence. Evolutionary algorithms can avoid the problems associated with a greedy search and have been successfully employed to the induction of decision trees. Previously, we proposed a lexicographic multi-objective genetic algorithm for decision-tree induction, named LEGAL-Tree. In this work, we propose extending this approach substantially, particularly w.r.t. two important evolutionary aspects: the initialization of the population and the fitness function. We carry out a comprehensive set of experiments to validate our extended algorithm. The experimental results suggest that it is able to outperform both traditional algorithms for decision-tree induction and another evolutionary algorithm in a variety of application domains.

Identification of multi-component trail pheromones in the most evolutionarily derived termites, the Nasutitermitinae (Termitidae)

In the present study, trail pheromone blends are identified for the first time in termites. In the phylogenetically complex Nasutitermitinae, trail-following pheromones are composed of dodecatrienol and neocembrene, the proportions of which vary according to species, although neocembrene is always more abundant than dodecatrienol (by 25-250-fold). Depending on species, termites were more sensitive to dodecatrienol or to neocembrene but the association of both components always elicited significantly higher trail following, with a clear synergistic effect in most of the studied species. A third component, trinervitatriene, was identified in the sternal gland secretion of several species, but its function remains unknown. The secretion of trail pheromone blends appears to be an important step in the evolution of chemical communication in termites. The pheromone optimizes foraging, and promotes their ecological success. (C) 2010 The Linnean Society of London, Biological Journal of the Linnean Society, 2010, 99, 20-27.

Simple heuristics for the assembly line worker assignment and balancing problem

We propose simple heuristics for the assembly line worker assignment and balancing problem. This problem typically occurs in assembly lines in sheltered work centers for the disabled. Different from the well-known simple assembly line balancing problem, the task execution times vary according to the assigned worker. We develop a constructive heuristic framework based on task and worker priority rules defining the order in which the tasks and workers should be assigned to the workstations. We present a number of such rules and compare their performance across three possible uses: as a stand-alone method, as an initial solution generator for meta-heuristics, and as a decoder for a hybrid genetic algorithm. Our results show that the heuristics are fast, they obtain good results as a stand-alone method and are efficient when used as a initial solution generator or as a solution decoder within more elaborate approaches.

Decision-directed feedback equalisation for QAM signals

Proposed is a symbol-based decision-directed algorithm for blind equalisation of quadrature amplitude modulation (QAM) signals using a decision feedback scheme. Independently of QAM order, it presents: (i) an error equal to zero when the equaliser output coincides with the transmitted signal; (ii) simultaneous recovery of the modulus and phase of the signal; (iii) a misadjustment close to that of the normalised least-mean squares algorithm; (iv) fast convergence; and (v) the avoidance of degenerative solutions. Additionally, its stability is ensured when the step-size is properly chosen.

Asynchronous teams for joint lot-sizing and scheduling problem in flow shops

The integrated production scheduling and lot-sizing problem in a flow shop environment consists of establishing production lot sizes and allocating machines to process them within a planning horizon in a production line with machines arranged in series. The problem considers that demands must be met without backlogging, the capacity of the machines must be respected, and machine setups are sequence-dependent and preserved between periods of the planning horizon. The objective is to determine a production schedule to minimise the setup, production and inventory costs. A mathematical model from the literature is presented, as well as procedures for obtaining feasible solutions. However, some of the procedures have difficulty in obtaining feasible solutions for large-sized problem instances. In addition, we address the problem using different versions of the Asynchronous Team (A-Team) approach. The procedures were compared with literature heuristics based on Mixed Integer Programming. The proposed A-Team procedures outperformed the literature heuristics, especially for large instances. The developed methodologies and the results obtained are presented.

Vancomycin-resistant enterococcus outbreak in a pediatric intensive care unit: report of successful interventions for control and prevention

The objective of this study is to retrospectively report the results of interventions for controlling a vancomycin-resistant enterococcus (VRE) outbreak in a tertiary-care pediatric intensive care unit (PICU) of a University Hospital. After identification of the outbreak, interventions were made at the following levels: patient care, microbiological surveillance, and medical and nursing staff training. Data were collected from computer-based databases and from the electronic prescription system. Vancomycin use progressively increased after March 2008, peaking in August 2009. Five cases of VRE infection were identified, with 3 deaths. After the interventions, we noted a significant reduction in vancomycin prescription and use (75% reduction), and the last case of VRE infection was identified 4 months later. The survivors remained colonized until hospital discharge. After interventions there was a transient increase in PICU length-of-stay and mortality. Since then, the use of vancomycin has remained relatively constant and strict, no other cases of VRE infection or colonization have been identified and length-of-stay and mortality returned to baseline. In conclusion, we showed that a bundle intervention aiming at a strict control of vancomycin use and full compliance with the Hospital Infection Control Practices Advisory Committee guidelines, along with contact precautions and hand-hygiene promotion, can be effective in reducing vancomycin use and the emergence and spread of vancomycin-resistant bacteria in a tertiary-care PICU.