Data Mining - Concepts and Techniques.

The first cooperative co-evolutionary algorithm (CCEA) was proposed by Potter and De Jong in 1994 and since then many CCEAs have been proposed and successfully applied to solving various complex optimization problems. In applying CCEAs, the complex optimization problem is decomposed into multiple subproblems, and each subproblem is solved with a separate subpopulation, evolved by an individual evolutionary algorithm (EA). Through cooperative co-evolution of multiple EA subpopulations, a complete problem solution is acquired by assembling the representative members from each subpopulation. The underlying divide-and-conquer and collaboration mechanisms enable CCEAs to tackle complex optimization problems efficiently, and hence CCEAs have been attracting wide attention in the EA community. This paper presents a comprehensive survey of these CCEAs, covering problem decomposition, collaborator selection, individual fitness evaluation, subproblem resource allocation, implementations, benchmark test problems, control parameters, theoretical analyses, and applications. The unsolved challenges and potential directions for their solutions are discussed.

A Survey on Cooperative Co-Evolutionary Algorithms

In recent years, machine learning algorithms have been successfully applied to industrial processes. However, the concurrent analysis of static and dynamic representations has not been comprehensively addressed for industrial process fault classification. In this paper, an enhanced random forest algorithm with a concurrent analysis of static and dynamic nodes is proposed to address this issue for fault classification. First, the standard slow feature analysis is modified by designing a new slowness index that is more suitable for a supervised fault classification problem. Second, a feature ranking process is conducted to determine the significant features. These features, which substitute the raw variables in the nodes, are used to build the enhanced random forest. Using this scheme, the significant static and dynamic nodes are selected to enhance the discriminative ability and interpretation. Additionally, the slow features that are uncorrelated are more suitable for training the forest than the initial correlated variables, and the dynamic characteristics of industrial processes are thus comprehensively addressed. The application of the proposed method to fault classification is evaluated by both the Tennessee Eastman benchmark and a real-world three-phase flow process. The experimental results show that the proposed method outperforms the traditional learning algorithms with remarkable accuracy and F1 score that both exceed 70% for the 16-class Tennessee Eastman process and exceed 99% for the 4-class three-phase flow process. The selected significant features reveal that both the static and dynamic information play important roles in fault classification.

Enhanced Random Forest With Concurrent Analysis of Static and Dynamic Nodes for Industrial Fault Classification

We present a hybrid metaheuristic optimization algorithm for solving economic dispatch problems in power systems. The proposed algorithm, based on bat algorithm, combines chaotic map and random black hole model together. Chaotic map is used to prevent premature convergence, and the random black hole model is helpful not only in avoiding premature convergence, but also in increasing the global search ability, enlarging exploitation area and accelerating convergence speed. The pseudocode and related parameters of the proposed algorithm are also given in this paper. Different from other related works, the costs of conventional thermal generators and random wind power are both included in the cost function because of the increasing penetration of wind power. The proposed algorithm has no requirement on the convexity or continuous differentiability of the cost function, although the effect on fuel cost, caused by the underestimation and overestimation of wind power, is included. This makes it feasible to take more practical nonlinear constraints into account, such as prohibited operating zones and ramp rate limits. Three test cases are given to illustrate the effectiveness of the proposed method.

A Hybrid Bat Algorithm for Economic Dispatch With Random Wind Power

Modeling and assessing interdependencies between critical infrastructures using Bayesian network: A case study of inland waterway port and surrounding supply chain network

Factored evolutionary algorithms (FEAs) are a new class of evolutionary search-based optimization algorithms that have successfully been applied to various problems, such as training neural networks and performing abductive inference in graphical models. An FEA is unique in that it factors the objective function by creating overlapping subpopulations that optimize over a subset of variables of the function. In this paper, we give a formal definition of FEA algorithms and present empirical results related to their performance. One consideration in using an FEA is determining the appropriate factor architecture, which determines the set of variables each factor will optimize. For this reason, we present the results of experiments comparing the performance of different factor architectures on several standard applications for evolutionary algorithms. Additionally, we show that FEA’s performance is not restricted by the underlying optimization algorithm by creating FEA versions of hill climbing, particle swarm optimization, genetic algorithm, and differential evolution and comparing their performance to their single-population and cooperative coevolutionary counterparts.

Factored Evolutionary Algorithms

Particle Swarm Optimization (PSO) has been shown to perform very well on a wide range of optimization problems. One of the drawbacks to PSO is that the base algorithm assumes continuous variables. In this paper, we present a version of PSO that is able to optimize over discrete variables. This new PSO algorithm, which we call Integer and Categorical PSO (ICPSO), incorporates ideas from Estimation of Distribution Algorithms (EDAs) in that particles represent probability distributions rather than solution values, and the PSO update modifies the probability distributions. In this paper, we describe our new algorithm and compare its performance against other discrete PSO algorithms. In our experiments, we demonstrate that our algorithm outperforms comparable methods on both discrete benchmark functions and NK landscapes, a mathematical framework that generates tunable fitness landscapes for evaluating EAs.

A New Discrete Particle Swarm Optimization Algorithm

In this paper we propose several approximation algorithms for the problems of full and partial abductive inference in Bayesian belief networks. Full abductive inference is the problem of finding the $$k$$ k most probable state assignments to all non-evidence variables in the network while partial abductive inference is the problem of finding the $$k$$ k most probable state assignments for a subset of the non-evidence variables in the network, called the explanation set. We developed several multi-swarm algorithms based on the overlapping swarm intelligence framework to find approximate solutions to these problems. For full abductive inference a swarm is associated with each node in the network. For partial abductive inference, a swarm is associated with each node in the explanation set and each node in the Markov blankets of the explanation set variables. Each swarm learns the value assignments for the variables in the Markov blanket associated with that swarm's node. Swarms learning state assignments for the same variable compete for inclusion in the final solution.

https://www.cs.montana.edu/sheppard/pubs/softcomp-2014.pdf

Abductive inference in Bayesian networks using distributed overlapping swarm intelligence

In model-based diagnostic algorithms, it is assumed that the model is correct. If the model is incorrect, the diagnostic algorithm may diagnose the wrong fault, which can be costly and time consuming. Using past maintenance events, one should be able to make corrections to the model in order for diagnostic algorithm to correctly diagnosis faults. In this paper, a maturation approach is proposed which uses the graph-theoretic representations of Timed Failure Propagation Graph (TFPG) models and diagnostic sessions based on recently standardized diagnostic ontologies to determine statistical discrepancies between that which is expected by the models and that which has been encountered in practice. These discrepancies are then analyzed to generate recommendations for maturing the diagnostic models. Maturation recommendations include identifying new dependencies and erroneous or tenuous dependencies. 1 2

/pdf/graph-based-ontology-guided-data-mining-for-d-matrix-model-mwm61xqpa7.pdf

Graph-based ontology-guided data mining for D-matrix model maturation

Diagnostic model development presents a significant engineering challenge to ensure subsequent diagnostic processes using such models will yield accurate results. One approach to developing system-level diagnostic models that has been receiving attention is the Timed Failure Propagation Graph (TFPG), developed at Vanderbilt University. Unfortunately, developing TFPG models is also difficult and error-prone. In this paper, we extend previous work in using historical maintenance and diagnostic information to identify potential errors in the TFPG-based diagnostic models and recommend ways of maturing these models. This is done by extending the maturation process to incorporate historical alarm sequences and to model these sequences using a probabilistic transition matrix (similar to a Markov chain). The resulting sequence model is compared to the causal relationships identified in the original TFPG to discover discrepancies between the two. Potential sequence modeling errors with recommendations are given back to an engineer or analyst. We report on the maturation process and algorithms and also provide preliminary experimental results.

/pdf/diagnostic-alarm-sequence-maturation-in-timed-failure-52zkj4uwhl.pdf

Shane Strasser

Papers

Factored Evolutionary Algorithms

A New Discrete Particle Swarm Optimization Algorithm

Abductive inference in Bayesian networks using distributed overlapping swarm intelligence

Graph-based ontology-guided data mining for D-matrix model maturation

Diagnostic alarm sequence maturation in timed failure propagation graphs