Evolutionary model type selection for global surrogate modeling

Due to the scale and computational complexity of currently used simulation codes, global surrogate (metamodels) models have become indispensable tools for exploring and understanding the design space. Due to their compact formulation they are cheap to evaluate and thus readily facilitate visualization, design space exploration, rapid prototyping, and sensitivity analysis. They can also be used as accurate building blocks in design packages or larger simulation environments. Consequently, there is great interest in techniques that facilitate the construction of such approximation models while minimizing the computational cost and maximizing model accuracy. Many surrogate model types exist ( Support Vector Machines, Kriging, Neural Networks, etc.) but no type is optimal in all circumstances. Nor is there any hard theory available that can help make this choice. In this paper we present an automatic approach to the model type selection problem. We describe an adaptive global surrogate modeling environment with adaptive sampling, driven by speciated evolution. Different model types are evolved cooperatively using a Genetic Algorithm ( heterogeneous evolution) and compete to approximate the iteratively selected data. In this way the optimal model type and complexity for a given data set or simulation code can be dynamically determined. Its utility and performance is demonstrated on a number of problems where it outperforms traditional sequential execution of each model type

A method for the architectural design of distributed control systems for large, civil jet engines: a systems engineering approach

The design of distributed control systems (DCSs) for large, civil gas turbine engines is a complex architectural challenge. To date, the majority of research into DCSs has focused on the contributing technologies and high temperature electronics rather than the architecture of the system itself. This thesis proposes a method for the architectural design of distributed systems using a genetic algorithm to generate, evaluate and refine designs. The proposed designs are analysed for their architectural quality, lifecycle value and commercial benefit. The method is presented along with results proving the concept. Whilst the method described here is applied exclusively to Distributed Control System (DCS) for jet engines, the principles and methods could be adapted for a broad range of complex systems

Steady state particle swarm

The following grant information was disclosed by the authors: Fundação para a Ciência e Tecnologia (FCT), Research Fellowship: SFRH/BPD/66876/2009. FCT PROJECT: UID/EEA/50009/2013. EPHEMECH: TIN2014-56494-C4-3-P, Spanish Ministry of Economy and Competitivity. PROY-PP2015-06: Plan Propio 2015 UGR. CEI2015-MP-V17 of the Microprojects program 2015 from CEI BioTIC Granada.This paper investigates the performance and scalability of a new update strategy for the particle swarm optimization (PSO) algorithm. The strategy is inspired by the Bak–Sneppen model of co-evolution between interacting species, which is basically a network of fitness values (representing species) that change over time according to a simple rule: the least fit species and its neighbors are iteratively replaced with random values. Following these guidelines, a steady state and dynamic update strategy for PSO algorithms is proposed: only the least fit particle and its neighbors are updated and evaluated in each time-step; the remaining particles maintain the same position and fitness, unless they meet the update criterion. The steady state PSO was tested on a set of unimodal, multimodal, noisy and rotated benchmark functions, significantly improving the quality of results and convergence speed of the standard PSOs and more sophisticated PSOs with dynamic parameters and neighborhood. A sensitivity analysis of the parameters confirms the performance enhancement with different parameter settings and scalability tests show that the algorithm behavior is consistent throughout a substantial range of solution vector dimensions.This work was supported by Fundação para a Ciência e Tecnologia (FCT) Research Fellowship SFRH/BPD/66876/2009 and FCT Project (UID/EEA/50009/2013), EPHEMECH (TIN2014-56494-C4-3-P, Spanish Ministry of Economy and Competitivity), PROY-PP2015-06 (Plan Propio 2015 UGR), project CEI2015-MP-V17 of the Microprojects program 2015 from CEI BioTIC Granada

Performance analysis of multimodal biometric fusion

Biometrics is constantly evolving technology which has been widely used in many official and commercial identification applications. In fact in recent years biometric-based authentication techniques received more attention due to increased concerns in security. Most biometric systems that are currently in use typically employ a single biometric trait. Such systems are called unibiometric systems. Despite considerable advances in recent years, there are still challenges in authentication based on a single biometric trait, such as noisy data, restricted degree of freedom, intra-class variability, non-universality, spoof attack and unacceptable error rates. Some of the challenges can be handled by designing a multimodal biometric system. Multimodal biometric systems are those which utilize or are capable of utilizing, more than one physiological or behavioural characteristic for enrolment, verification, or identification. In this thesis, we propose a novel fusion approach at a hybrid level between iris and online signature traits. Online signature and iris authentication techniques have been employed in a range of biometric applications. Besides improving the accuracy, the fusion of both of the biometrics has several advantages such as increasing population coverage, deterring spoofing activities and reducing enrolment failure. In this doctoral dissertation, we make a first attempt to combine online signature and iris biometrics. We principally explore the fusion of iris and online signature biometrics and their potential application as biometric identifiers. To address this issue, investigations is carried out into the relative performance of several statistical data fusion techniques for integrating the information in both unimodal and multimodal biometrics. We compare the results of the multimodal approach with the results of the individual online signature and iris authentication approaches. This dissertation describes research into the feature and decision fusion levels in multimodal biometrics.State of Kuwait – The Public Authority of Applied Education and Trainin

Ant Colony Optimization

Ant Colony Optimization (ACO) is the best example of how studies aimed at understanding and modeling the behavior of ants and other social insects can provide inspiration for the development of computational algorithms for the solution of difficult mathematical problems. Introduced by Marco Dorigo in his PhD thesis (1992) and initially applied to the travelling salesman problem, the ACO field has experienced a tremendous growth, standing today as an important nature-inspired stochastic metaheuristic for hard optimization problems. This book presents state-of-the-art ACO methods and is divided into two parts: (I) Techniques, which includes parallel implementations, and (II) Applications, where recent contributions of ACO to diverse fields, such as traffic congestion and control, structural optimization, manufacturing, and genomics are presented

Artificial immune systems based committee machine for classification application

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A new adaptive learning Artificial Immune System (AIS) based committee machine is developed in this thesis. The new proposed approach efficiently tackles the general problem of clustering high-dimensional data. In addition, it helps on deriving useful decision and results related to other application domains such classification and prediction. Artificial Immune System (AIS) is a branch of computational intelligence field inspired by the biological immune system, and has gained increasing interest among researchers in the development of immune-based models and techniques to solve diverse complex computational or engineering problems. This work presents some applications of AIS techniques to health problems, and a thorough survey of existing AIS models and algorithms. The main focus of this research is devoted to building an ensemble model integrating different AIS techniques (i.e. Artificial Immune Networks, Clonal Selection, and Negative Selection) for classification applications to achieve better classification results. A new AIS-based ensemble architecture with adaptive learning features is proposed by integrating different learning and adaptation techniques to overcome individual limitations and to achieve synergetic effects through the combination of these techniques. Various techniques related to the design and enhancements of the new adaptive learning architecture are studied, including a neuro-fuzzy based detector and an optimizer using particle swarm optimization method to achieve enhanced classification performance. An evaluation study was conducted to show the performance of the new proposed adaptive learning ensemble and to compare it to alternative combining techniques. Several experiments are presented using different medical datasets for the classification problem and findings and outcomes are discussed. The new adaptive learning architecture improves the accuracy of the ensemble. Moreover, there is an improvement over the existing aggregation techniques. The outcomes, assumptions and limitations of the proposed methods with its implications for further research in this area draw this research to its conclusion

Optimum Allocation of Inspection Stations in Multistage Manufacturing Processes by Using Max-Min Ant System

In multistage manufacturing processes it is common to locate inspection stations after some or all of the processing workstations. The purpose of the inspection is to reduce the total manufacturing cost, resulted from unidentified defective items being processed unnecessarily through subsequent manufacturing operations. This total cost is the sum of the costs of production, inspection and failures (during production and after shipment). Introducing inspection stations into a serial multistage manufacturing process, although constituting an additional cost, is expected to be a profitable course of action. Specifically, at some positions the associated inspection costs will be recovered from the benefits realised through the detection of defective items, before wasting additional cost by continuing to process them. In this research, a novel general cost modelling for allocating a limited number of inspection stations in serial multistage manufacturing processes is formulated. In allocation of inspection station (AOIS) problem, as the number of workstations increases, the number of inspection station allocation possibilities increases exponentially. To identify the appropriate approach for the AOIS problem, different optimisation methods are investigated. The MAX-MIN Ant System (MMAS) algorithm is proposed as a novel approach to explore AOIS in serial multistage manufacturing processes. MMAS is an ant colony optimisation algorithm that was designed originally to begin an explorative search phase and, subsequently, to make a slow transition to the intensive exploitation of the best solutions found during the search, by allowing only one ant to update the pheromone trails. Two novel heuristics information for the MMAS algorithm are created. The heuristic information for the MMAS algorithm is exploited as a novel means to guide ants to build reasonably good solutions from the very beginning of the search. To improve the performance of the MMAS algorithm, six local search methods which are well-known and suitable for the AOIS problem are used. Selecting relevant parameter values for the MMAS algorithm can have a great impact on the algorithm’s performance. As a result, a method for tuning the most influential parameter values for the MMAS algorithm is developed. The contribution of this research is, for the first time, a methodology using MMAS to solve the AOIS problem in serial multistage manufacturing processes has been developed. The methodology takes into account the constraints on inspection resources, in terms of a limited number of inspection stations. As a result, the total manufacturing cost of a product can be reduced, while maintaining the quality of the product. Four numerical experiments are conducted to assess the MMAS algorithm for the AOIS problem. The performance of the MMAS algorithm is compared with a number of other methods this includes the complete enumeration method (CEM), rule of thumb, a pure random search algorithm, particle swarm optimisation, simulated annealing and genetic algorithm. The experimental results show that the effectiveness of the MMAS algorithm lies in its considerably shorter execution time and robustness. Further, in certain conditions results obtained by the MMAS algorithm are identical to the CEM. In addition, the results show that applying local search to the MMAS algorithm has significantly improved the performance of the algorithm. Also the results demonstrate that it is essential to use heuristic information with the MMAS algorithm for the AOIS problem, in order to obtain a high quality solution. It was found that the main parameters of MMAS include the pheromone trail intensity, heuristic information and evaporation of pheromone are less sensitive within the specified range as the number of workstations is significantly increased

A dynamic scheduling model for construction enterprises

The vast majority of researches in the scheduling context focused on finding optimal or near-optimal predictive schedules under different scheduling problem characteristics. In the construction industry, predictive schedules are often produced in advance in order to direct construction operations and to support other planning activities. However, construction projects operate in dynamic environments subject to various real-time events, which usually disrupt the predictive optimal schedules, leading to schedules neither feasible nor optimal. Accordingly, the development of a dynamic scheduling model which can accommodate these real-time events would be of great importance for the successful implementation of construction scheduling systems. This research sought to develop a dynamic scheduling based solution which can be practically used for real time analysis and scheduling of construction projects, in addition to resources optimization for construction enterprises. The literature reviews for scheduling, dynamic scheduling, and optimization showed that despite the numerous researches presented and application performed in the dynamic scheduling field within manufacturing and other industries, there was dearth in dynamic scheduling literature in relation to the construction industry. The research followed two main interacting research paths, a path related to the development of the practical solution, and another path related to the core model development. The aim of the first path (or the proposed practical solution path) was to develop a computer-based dynamic scheduling framework which can be used in practical applications within the construction industry. Following the scheduling literature review, the construction project management community s opinions about the problem under study and the user requirements for the proposed solution were collected from 364 construction project management practitioners from 52 countries via a questionnaire survey and were used to form the basis for the functional specifications of a dynamic scheduling framework. The framework was in the form of a software tool fully integrated with current planning/scheduling practices with all core modelling which can support the integration of the dynamic scheduling processes to the current planning/scheduling process with minimal experience requirement from users about optimization. The second research path, or the dynamic scheduling core model development path, started with the development of a mathematical model based on the scheduling models in literature, with several extensions according to the practical considerations related to the construction industry, as investigated in the questionnaire survey. Scheduling problems are complex from operational research perspective; so, for the proposed solution to be functional in optimizing construction schedules, an optimization algorithm was developed to suit the problem's characteristics and to be used as part of the dynamic scheduling model's core. The developed algorithm contained few contributions to the scheduling context (such as schedule justification heuristics, and rectification to schedule generation schemes), as well as suggested modifications to the formulation and process of the adopted optimization technique (particle swarm optimization) leading to considerable improvement to this techniques outputs with respect to schedules quality. After the completion of the model development path, the first research path was concluded by combining the gathered solution's functional specifications and the developed dynamic scheduling model into a software tool, which was developed to verify & validate the proposed model s functionalities and the overall solution s practicality and scalability. The verification process started with an extensive testing of the model s static functionality using several well recognized scheduling problem sets available in literature, and the results showed that the developed algorithm can be ranked as one of the best state-of-the-art algorithms for solving resource-constrained project scheduling problems. To verify the software tool and the dynamic features of the developed model (or the formulation of data transfers from one optimization stage to the next), a case study was implemented on a construction entity in the Arabian Gulf area, having a mega project under construction, with all aspects to resemble an enterprise structure. The case study results showed that the proposed solution reasonably performed under large scale practical application (where all optimization targets were met in reasonable time) for all designed schedule preparation processes (baseline, progress updates, look-ahead schedules, and what-if schedules). Finally, to confirm and validate the effectiveness and practicality of the proposed solution, the solution's framework and the verification results were presented to field experts, and their opinions were collected through validation forms. The feedbacks received were very positive, where field experts/practitioners confirmed that the proposed solution achieved the main functionalities as designed in the solution s framework, and performed efficiently under the complexity of the applied case study

APPLYING PARTICLE SWARM OPTIMIZATION TO ESTIMATE PSYCHOMETRIC MODELS WITH CATEGORICAL RESPONSES

Current psychometrics tend to model response data hypothesized to arise from multiple attributes. As a result, the estimation complexity has been greatly increased so that traditional approaches such as the expected-maximization algorithm would fail to produce accurate results. To improve the estimation quality, high-dimensional models are estimated via a global optimization approach- particle swarm optimization (PSO), which is an efficient stochastic method of handling the complexity difficulties. The PSO has been widely used in machine learning fields but remains less-known in the psychometrics community. Details on the integration of the proposed approach to current psychometric model estimation practices are provided. The algorithm tuning process and the accuracy of the proposed approach are demonstrated with simulations. As an illustration, the proposed approach is applied to log-linear cognitive diagnosis models and multi-dimensional item response theory models. These two model families are fairly popular yet challenging frameworks used in assessment and evaluation research to explain how participants respond to item level stimuli. The aim of this dissertation is to fill the gap between the field of psychometric modeling and machine learning estimation techniques

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016)

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.The PhD Symposium was a very good opportunity for the young researchers to share information and knowledge, to present their current research, and to discuss topics with other students in order to look for synergies and common research topics. The idea was very successful and the assessment made by the PhD Student was very good. It also helped to achieve one of the major goals of the NESUS Action: to establish an open European research network targeting sustainable solutions for ultrascale computing aiming at cross fertilization among HPC, large scale distributed systems, and big data management, training, contributing to glue disparate researchers working across different areas and provide a meeting ground for researchers in these separate areas to exchange ideas, to identify synergies, and to pursue common activities in research topics such as sustainable software solutions (applications and system software stack), data management, energy efficiency, and resilience.European Cooperation in Science and Technology. COS