Nélida Beatriz Brignole

On Stopping Criteria for Genetic Algorithms

In this work we present a critical analysis of various aspects associated with the specification of termination conditions for simple genetic algorithms. The study, which is based on the use of Markov chains, identifies the main difficulties that arise when one wishes to set meaningful upper bounds for the number of iterations required to guarantee the convergence of such algorithms with a given confidence level. The latest trends in the design of stopping rules for evolutionary algorithms in general are also put forward and some proposals to overcome existing limitations in this respect are suggested.

CGD-GA: A graph-based genetic algorithm for sensor network design

Abstract The foundations and implementation of a genetic algorithm (GA) for instrumentation purposes are presented in this paper. The GA constitutes an initialization module of a decision support system for sensor network design. The method development entailed the definition of the individual’s representation as well as the design of a graph-based fitness function, along with the formulation of several other ad hoc implemented features. The performance and effectiveness of the GA were assessed by initializing the instrumentation design of an ammonia synthesis plant. The initialization provided by the GA succeeded in accelerating the sensor network design procedures. It also accomplished a great improvement in the overall quality of the resulting instrument configuration. Therefore, the GA constitutes a valuable tool for the treatment of real industrial problems.

ModGen: a model generator for instrumentation analysis

Abstract A computer software tool for the automatic generation of steady-state process models to be used in instrumentation analysis was developed. We describe the program, called ModGen, discussing its main advantages and potential benefits. ModGen constitutes the front-end of a complete decision support system (DSS) for plant instrumentation design and revamp. This DSS is currently under development. The paper concludes with the description of ModGens application to the classification of unmeasured variables of an existing medium-size process plant by means of GS-FLCNs structural technique for observability analysis.

A novel application of evolutionary computing in process systems engineering

In this article we present a Multi-Objective Genetic Algorithm for Initialization (MOGAI) that finds a starting sensor configuration for Observability Analysis (OA), this study being a crucial stage in the design and revamp of process-plant instrumentation. The MOGAI is a binary-coded genetic algorithm with a three-objective fitness function based on cost, reliability and observability metrics. MOGAI’s special features are: dynamic adaptive bit-flip mutation and guided generation of the initial population, both giving a special treatment to non-feasible individuals, and an adaptive genotypic convergence criterion to stop the algorithm. The algorithmic behavior was evaluated through the analysis of the mathematical model that represents an ammonia synthesis plant. Its efficacy was assessed by comparing the performance of the OA algorithm with and without MOGAI initialization. The genetic algorithm proved to be advantageous because it led to a significant reduction in the number of iterations required by the OA algorithm.

SID-GA: An evolutionary approach for improving observability and redundancy analysis in structural instrumentation design

In this paper the core of a genetic algorithm designed to define a sensor network for instrumentation design (ID) is presented. The tool has been incorporated into a decision support system (DSS) that assists the engineer during the ID process. The algorithm satisfactorily deals with non-linear mathematical models, and considers four design objectives, namely observability, cost, reliability and redundancy, exhibiting properties that were either never addressed by existing techniques or partially dealt with in the literature. Its performance was tested by carrying out the ID of an ammonia synthesis industrial plant. Results were statistically analysed. A face validity study on the fitness functions soundness was also assessed by a chemical engineer with insight and expertise in this problem. The technique performed satisfactorily from the point of view of the expert in ID, and therefore it constitutes a significant upgrading for the DSS.

Parallel observability analysis on networks of workstations

Abstract In this work we present the parallelisation of the global strategy with first least-connected node (GS-FLCN), which is a novel structural technique for the classification of unmeasured variables in process plant instrumentation design. The algorithm aims at partitioning the process’ occurrence matrix to a specific block lower-triangular form. A parallel master–workers philosophy is employed to search for all the paths of a given length existing in the associated graph. The code was conceived for distributed environments and the implementation was carried out using the parallel virtual machine (PVM) library. The performance of the parallel algorithm was tested for industrial case studies and the results were compared with those yielded by the sequential version. The time savings achieved thanks to the parallelisation were significant. Besides, in the parallel version, more paths can be explored per unit time. In practice, this implies greater robustness.

A Structural Analysis of topic ontologies

Abstract DMOZ is the largest human-edited topic ontology available on the Web. This article studies the structural properties of the DMOZ graph. A number of global and local properties of this graph and the subgraphs resulting from isolating edges of different types are examined by means of metrics commonly used in complex network analysis. In particular, we investigate the presence of various features that characterize small-world networks. This analysis is complemented by examining other characteristics of the graphs such as connectivity and centrality measures. The connectivity and centrality patterns are further studied by means of visualizations of the graphs’ k-core decomposition and a selection of strongly connected components. Several non-trivial regularities that are also encountered in other artificial and natural complex networks provide a general picture of this large human-edited topic ontology. This analysis is of major pragmatic interest as it allows a better understanding of notions such as navigability among topics, hierarchical structure and topic cohesiveness, which are of great importance in the design of topic ontologies.

A study of relevance propagation in large topic ontologies

Topic ontologies or web directories consist of large collections of links to websites, arranged by topic in different categories. The structure of these ontologies is typically not flat because there are hierarchical and nonhierarchical relationships among topics. As a consequence, websites classified under a certain topic may be relevant to other topics. Although some of these relevance relations are explicit, most of them must be discovered by an analysis of the structure of the ontologies. This article proposes a family of models of relevance propagation in topic ontologies. An efficient computational framework is described and used to compute nine different models for a portion of the Open Directory Project graph consisting of more than half a million nodes and approximately 1.5 million edges of different types. After performing a quantitative analysis, a user study was carried out to compare the most promising models. It was found that some general difficulties rule out the possibility of defining flawless models of relevance propagation that only take into account structural aspects of an ontology. However, there is a clear indication that including transitive relations induced by the nonhierarchical components of the ontology results in relevance propagation models that are superior to more basic approaches.

Bus Network Optimization Through Time-Dependent Hybrid Algorithm

This paper focuses on a new hybrid technique that combines a genetic algorithm with simulation to solve the bus-network scheduling problem (BNSP). The BNSP has several factors that complicate both the problem formulation and the selection of efficient algorithms for its resolution. This problem is challenging because not only the BNSP is NP-complete, but also the existing methods fail to contemplate environment dependent dynamic variables. The hybrid algorithm proposed in this article comprises two stages: a modified GRASP (greedy randomized adaptive search procedures) as an initialization method, and the genetic algorithm with simulation to find the values of the environment- dependent dynamic variables. The final goal consisted in designing a meta-heuristic technique that yields an adequate scheduling to solve this general problem. The BNSP, chosen as case study, satisfies both the demand and the offer of transport. The method was applied to a solution of experimental examples with good results.

Optimization of industrial problems using parallel processing under distributed environments

A parallel optimization algorithm implemented in a distributed computing environment was applied to nonlinear engineering problems. We deal with the Parallel Variable Distribution (PVD) algorithm, discussing how to handle nonlinear constraints and proposing a new domain-partitioning heuristics. The quality of the proposal was first assessed by analyzing its performance for several small nonlinear models associated with classical engineering problems. Then, the parallel distributed code was employed to solve the rigorous model of an existing expander plant, whose constraint-evaluation stage was more complex. Satisfactory speed-up and efficiency values were achieved.