Uncertainty And Evolutionary Optimization: A Novel Approach

Evolutionary algorithms (EA) have been widely accepted as efficient solvers for complex real world optimization problems, including engineering optimization. However, real world optimization problems often involve uncertain environment including noisy and/or dynamic environments, which pose major challenges to EA-based optimization. The presence of noise interferes with the evaluation and the selection process of EA, and thus adversely affects its performance. In addition, as presence of noise poses challenges to the evaluation of the fitness function, it may need to be estimated instead of being evaluated. Several existing approaches attempt to address this problem, such as introduction of diversity (hyper mutation, random immigrants, special operators) or incorporation of memory of the past (diploidy, case based memory). However, these approaches fail to adequately address the problem. In this paper we propose a Distributed Population Switching Evolutionary Algorithm (DPSEA) method that addresses optimization of functions with noisy fitness using a distributed population switching architecture, to simulate a distributed self-adaptive memory of the solution space. Local regression is used in the pseudo-populations to estimate the fitness. Successful applications to benchmark test problems ascertain the proposed method's superior performance in terms of both robustness and accuracy.Comment: In Proceedings of the The 9th IEEE Conference on Industrial Electronics and Applications (ICIEA 2014), IEEE Press, pp. 988-983, 201

Reduced computation for evolutionary optimization in noisy environment

ABSTRACT Evolutionary Algorithms' (EAs') application to real world optimization problems often involves expensive fitness function evaluation. Naturally this has a crippling effect on the performance of any population based search technique such as EA. Estimating the fitness of individuals instead of actually evaluating them is a workable approach to deal with this situation. Optimization problems in real world often involve expensive fitness. In Categories and Subject Descriptors INTRODUCTION Many real world optimization problems involve very expensive function evaluation, making it impractical for a population based search technique such as EA to be used in such problem domains. In such problems, the run-time for a single function evaluation could be in the range from a fraction of a second to hours of supercomputer time. A suitable alternative is to use approximation instead of actual function evaluation to substantially reduce the computation time [8, 10, and 11]. Use of approximate model to speed up optimization dates all the way back to the sixties The DAFHEA (dynamic approximate fitness based hybrid evolutionary algorithm) framework proposed in our earlier researc

Surrogate-based modeling strategy for design optimization of passenger car suspension system

The dynamic response of a Low-Fidelity (LoFi) vehicle model exhibits a discrepancy when compared to a High-Fidelity (HiFi) vehicle model. HiFi model construction involves complex state-space equations, a high degree of freedom, and requires a huge quantity of early data to completely define this model. This causes a delay and makes the computation process less efficient. On the other hand, the LoFi model developed using simpler state-space equations is faster and computationally cheaper. However, the response accuracy of this model is lower than that of HiFi. Due to this competence mismatch, it constrains the ability and integration of LoFi model or HiFi model applications in vehicle dynamics research. In previous researches, the proposed surrogate model has been completely replaced any physics-based model for subsequent engineering applications once it has been generated. However, this model has limitation to perform fine tuning either on LoFi or HiFi models. The primary aim of this research was to formulate a surrogate-based modeling strategy by tuning LoFi model for optimizing the design of the passenger car suspension system. The study began with the development of HiFi and LoFi models in Matlab, and their performances were verified by comparing the results produced by MSC Adams software. The LoFi model was used to determine the overall relationship between the suspension system's main elements, namely spring stiffness (Ks) and damper rate (Cs), and the design criteria, namely Body Acceleration (BAcc), Dynamic Tire Load (DTL), and Suspension Workspace (SWS). Based on the Design Criteria Space (DCS) map and recommendations from the literature, the Design Objective Space (DOS) map for a passenger car suspension system was established. Following that, three approaches to formulating surrogate models were introduced, namely the Response-Based Approach (RBA), the Variable-Based Approach (VBA), and the Parameter-Based Approach (PBA). The VBA for the Quadratic Transformation Scheme (QTS) was found to be the most suitable for the proposed newly surrogate model. Next, the surrogate model was linked to an optimization strategy to tune the suspension elements. Finally, a single optimal solution was obtained using the Min-Max method. The optimal tuning for the suspension elements of the chosen passenger car was Ks = 12535.6 N/m and Cs= 1416.7 Ns/m which increased the BAcc by 12.6% but at the expense of DTL performance by 6.4%, and keeping the SWS below the 7 mm restriction. In conclusion, the proposed surrogate-based modeling strategy could be a potential tool for optimizing the design of a passenger car suspension system

The relationship between search based software engineering and predictive modeling

Search Based Software Engineering (SBSE) is an approach to software engineering in which search based optimization algorithms are used to identify optimal or near optimal solutions and to yield insight. SBSE techniques can cater for multiple, possibly competing objectives and/or constraints and applications where the potential solution space is large and complex. This paper will provide a brief overview of SBSE, explaining some of the ways in which it has already been applied to construction of predictive models. There is a mutually beneficial relationship between predictive models and SBSE. The paper sets out eleven open problem areas for Search Based Predictive Modeling and describes how predictive models also have role to play in improving SBSE

ONLINE APPROXIMATION ASSISTED MULTIOBJECTIVE OPTIMIZATION WITH HEAT EXCHANGER DESIGN APPLICATIONS

Computer simulations can be intensive as is the case in Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA). The computational cost can become prohibitive when using these simulations with multiobjective design optimization. One way to address this issue is to replace a computationally intensive simulation by an approximation which allows for a quick evaluation of a large number of design alternatives as needed by an optimizer. This dissertation proposes an approach for multiobjective design optimization when combined with computationally expensive simulations for heat exchanger design problems. The research is performed along four research directions. These are: (1) a new Online Approximation Assisted Multiobjective Optimization (OAAMO) approach with a focus on the expected optimum region, (2) a new approximation assisted multiobjective optimization with global and local metamodeling that always produces feasible solutions, (3) a framework that integrates OAAMO with multiscale simulations (OAAMOMS) for design of heat exchangers at the segment and heat exchanger levels, and (4) applications of OAAMO combined with CFD for shape design of a header for a new generation of heat exchangers using Non-Uniform Rational B-Splines (NURBS). The approaches developed in this thesis are also applied to optimize a coldplate used in electronic cooling devices and different types of plate heat exchangers. In addition many numerical test problems are solved by the proposed methods. The results of these studies show that the proposed online approximation assisted multiobjective optimization is an efficient approach that can be used to predict optimum solutions for a wide class of problems including heat exchanger design problems while reducing significantly the computational cost when compared with existing methods

Controlled Branching of Industrially Important Polymers: Modeling and Multi-objective Optimization

Long chain branching (LCB) in any polymerization is of profound importance. It helps in improving certain properties such as melt strength and strain hardening. Branched polymers are, therefore, having different characteristics than linear polymers. In addition to having good end use properties, they are well suited for various processing applications such as blow molding, thermoforming, extrusion coating etc. As real world applications demand different extents of branching of polymers for different applications, this study aims to perform an investigation for a controlled way of long chain branching of polymers with enhanced properties. The main goal of this research is, therefore, three fold; viz. i) Finding the optimal process conditions for the desired combination of conflicting objectives, ii) Development of a kinetic model for long chain branched polypropylene system based on the available experimental data from open literature and simultaneously performing the multi objective optimization for the desired combination of conflicting performance objectives within experimental limits, and iii) Development of Kriging based surrogate model to replace the first principles based computationally expensive model to save execution time, while performing the multi objective optimization task for a highly non-linear, multi-modal search space. First, a batch optimization study for the bulk polymerization of vinyl acetate has been considered to find optimal process conditions for imparting LCB in polymer architecture. A theoretical study has been conducted with a validated model to observe the effect of live radical concentration on long chain branching as this is an important factor for branching in polymer molecule via ‘chain transfer to polymer’ route

Algorithme intelligent d'optimisation d'un design structurel de grande envergure

RÉSUMÉ L’implémentation d’un système automatisé d’aide à la décision en design et d’optimisation structurelle peut donner un avantage significatif à toute industrie oeuvrant dans le domaine du design de pièces mécaniques. En effet, en fournissant des idées de solutions au designer ou en améliorant les solutions existantes pendant qu’il n’est pas au travail, ce système lui permet de réduire le temps de conception, ou encore d’explorer davantage de possibilités de design dans un même délai de réalisation. Cette thèse présente une approche originale permettant l’automatisation d’un processus de design basée sur le raisonnement par cas (RPC), mieux connu sous l’appellation « Case-Based Reasoning » ou CBR. Cette approche a été développée avec l’optique de nécessiter le moins de ressources possible pour l’entretien et le fonctionnement du système. Elle nécessite toutefois une quantité appréciable de ressources lors de l’implémentation, et convient par conséquent davantage aux problèmes de design de grande envergure pour lesquels on envisage à moyen terme de répondre à plusieurs ensembles de spécifications différentes. Dans un premier temps, le processus de RPC utilise une banque de données contenant toutes les solutions antérieures connues aux problèmes de design similaires. Ensuite, une sélection de solutions de la banque de données est choisie en comparant les spécifications actuelles du problème avec celles des solutions antérieures. Un réseau de neurones substitut est alors utilisé pour produire une solution en adaptant les solutions antérieures aux spécifications actuelles. Les solutions émergeant du RPC servent ensuite à générer chacune un îlot de solutions initiales pour un algorithme génétique oeuvrant lors de la phase de raffinement du processus.----------ABSTRACT The implementation of an automated decision support system in the field of design and structural optimisation can give a significant advantage to any industry working on mechanical designs. Indeed, by providing solution ideas to a designer or by upgrading existing design solutions while the designer is not at work, the system may reduce the project cycle time, or allow more time to produce a better design. This thesis presents a new approach to automate a design process based on Case-Based Reasoning (CBR), in combination with a new genetic algorithm named Genetic Algorithm with Territorial core Evolution (GATE). This approach was developed in order to reduce the operating cost of the process. However, as the system implementation cost is quite expensive, the approach is better suited for large scale design problem, and particularly for design problems that the designer plans to solve for many different specification sets. First, the CBR process uses a databank filled with every known solution to similar design problems. Then, the closest solutions to the current problem in term of specifications are selected. After this, during the adaptation phase, an artificial neural network (ANN) interpolates amongst known solutions to produce an additional solution to the current problem using the current specifications as inputs. Each solution produced and selected by the CBR is then used to initialize the population of an island of the genetic algorithm. The algorithm will optimise the solution further during the refinement phase

Improving performance of genetic algorithms by using novel fitness functions

This thesis introduces Intelligent Fitness Functions and Partial Fitness Functions both of which can improve the performance of a genetic algorithm which is limited to a fixed run time. An Intelligent Fitness Function is defined as a fitness function with a memory. The memory is used to store information about individuals so that duplicate individuals do not need to have their fitness tested. Different types of memory (long and short term) and different storage strategies (fitness based, time base and frequency based) have been tested. The results show that an intelligent fitness function, with a time based long term memory improves the efficiency of a genetic algorithm the most. A Partial Fitness Function is defined as a fitness function that only partially tests the fitness of an individual at each generation. Thus only promising individuals get fully tested. Using a partial fitness function gives the genetic algorithm more evolutionary steps in the same length of time as a genetic algorithm using a normal fitness function. The results show that a genetic algorithm using a partial fitness function can achieve higher fitness levels than a genetic algorithm using a normal fitness function. Finally a genetic algorithm designed to solve a substitution cipher is compared to one equipped with an intelligent fitness function and another equipped with a partial fitness function. The genetic algorithm with the intelligent fitness function and the genetic algorithm with the partial fitness function both show a significant improvement over the genetic algorithm with a conventional fitness function.EThOS - Electronic Theses Online ServiceGBUnited Kingdo