Genetic Algorithms for Design of Liquid Retaining Structure

Recent advances in non-gradient based optimization methods (e.g., Genetic Algorithms, Particle Swarm Optimization) enhanced the abilities of discrete, integer, and mixed optimization problems. However, the very nature of non-gradient based algorithms is that the number of analyses required to get to an optimal solution is several orders of magnitude higher than for traditional gradient based optimization methods or response surface optimization methods, when considering continuous problems. Instead of these approaches we propose to use a response surface approximate optimization method modified to work with discrete design variables. In this case whenever it is required to perform the actual analysis of responses for the purpose of fitting a response surface approximation, the design variables will be converted to corresponding discrete values. Two discretization techniques are proposed. We demonstrate that although lacking global search properties like Genetic Algorithms and Particle Swarm Optimization, the discrete response surface optimization provides a computationally efficient way of improving an initial design and getting into a region of an optimum using only discrete points for analysis.

Sensitivity analyses for transverse cracking of Continuously Reinforced Concrete Pavement (CRCP) were investigated for the CRCP structure of Lei-Yi (Leiyang to Yizhang) Expressway in Hunan Province, China. The investigation included 13 main parameters (slab thickness, elastic modulus of steel, temperature shrinkage coefficient of steel, drying shrinkage strain, compression strength, creep coefficient of concrete, elastic modulus of concrete, temperature shrinkage coefficient of concrete, diameter and spacing of longitudinal reinforcement, base friction coefficient, reinforcement bond strength and temperature differences) at 5 different levels. The average crack spacing, the average crack width and the maximum reinforcement stress were simultaneously calculated with the iterative algorithms according to the one-dimension nonlinear analysis method. It considered the creep action and the nonlinear boundary properties that took into account the bond-slip between reinforcement and concrete, and the friction-slip between slab and base. The sensitivities of CRCP transverse cracking influenced by each factor were analyzed with the relative comparison method quantitatively and qualitatively. The results show that the main factors impacting the CRCP transverse cracking included shrinkage properties of concrete, concrete grade, spacing and diameter of reinforcement, and the grade and type of reinforcement.

Introduction: Many patients who present themselves with mental health complaints to a general practitioner GP also suffer from problems in other domains. Consequently, many of these multi-problem patients qualify for health care as well as social welfare. Since 2015, social welfare professionals in most municipalities in the Netherlands work organized in so called 'area-based social welfare teams' SWTs. Especially in the Lindenholt district in the city of Nijmegen an integrated approach is important. In this area the prevalence of psychiatric diagnoses and mental health symptoms are highest of Nijmegen and a considerate part of the population has problems in various life domains. However, collaboration between a GP facility and the local SWT is in most municipalities in the Netherlands, including Nijmegen, not self-evident. Description of practice change implemented: Professionals from GP facility Lindenholt and the local SWT have experimented with a new work format for collaboration, the so called “drie gesprek”in English: joint consultation with three parties in which the GP, a SWT professional and a neighborhood resident talk about the concerns and needed care of the resident. Aim and theory of change: Aim of the “drie gesprek” is an effective and efficient integration of healthcare and social welfare and an integral, preventive and person-centered approach for people with mental health complaints and problems in different life domains. To implement this new work format for collaboration, three cycles of ‘plan-do-check/evaluate-act’ were used. Targeted population: The targeted population are neighborhood residents of Lindenholt with mental health complaints in combination with problems in other domains and who receive a high level of care by both the GP and the SWT ‘high users’. Timeline: This experiment started in 2015 and had a timeline of two years. Highlights: Actual gain lies mostly in a closer connection between GPs and the SWT. Professionals meet more often and know each other better, therefore information is shared more easily. Also, awareness about different expectations of those involved was increased. Conclusions: The results of the practice change are not yet as hoped for. The first step of creating conditions for good collaboration was successful, but the next step to effective forms of integrative working is not yet achieved. Discussions: A follow-up demands a revision of urgency, shared interests, ambitions for the district and a collectively drafted vision by the municipality, GPs and SWT. Only when there is enough support in time and resources, by the professionals as well as policy makers, an elaboration of this vision has a chance of succeeding. Lessons learned: - Instead of jumping to an intervention, invest in getting to know your collaborators - Experienced barriers for effective collaboration are often not work-related but emotional. - Don’t assume you share the same vision but explicate a shared one. - Innovation starts with visionary health professionals, but if the conditions for implementation are not well organized e.g., financial support or extra hours, integral collaboration stagnates. - A condition for good collaboration seems to be that participating organizations are stable and there is a clear shared interest.

Optimal learning group formation: A multi-objective heuristic search strategy for enhancing inter-group homogeneity and intra-group heterogeneity

A new method is presented for an application of the Gaussian mixture model (GMM) to a multi-objective robust design optimization (RDO) of planar steel frame structures under aleatory (stochastic) uncertainty in material properties, external loads, and discrete design variables. Uncertainty in the discrete design variables is modeled in the wide range between the smallest and largest values in the catalog of the cross-sectional areas. A weighted sum of Gaussians is statistically trained based on the sampled training data to capture an underlying joint probability distribution function (PDF) of random input variables and the corresponding structural response. A simple regression function for predicting the structural response can be found by extracting the information from a conditional PDF, which is directly derived from the captured joint PDF. A multi-objective RDO problem is formulated with three objective functions, namely, the total mass of the structure, and the mean and variance values of the maximum inter-story drift under some constraints on design strength and serviceability requirements. The optimization problem is solved using a multi-objective genetic algorithm utilizing the trained GMM for calculating the statistical values of objective and constraint functions to obtain Pareto-optimal solutions. Since the three objective functions are highly conflicting, the best trade-off solution is desired and found from the obtained Pareto-optimal solutions by performing fuzzy-based compromise programming. The robustness and feasibility of the proposed method for finding the RDO of planar steel frame structures with discrete variables are demonstrated through two design examples.

Genetic algorithms (GA) are stochastic search procedures which have been used for solving many complex optimization problems. It is obvious that GA collect and exploit some statistical information about the search space, but this information isn't processed in explicit way. In this paper, we consider GA with binary representation and its explicit statistics in a form of probability distribution of unit-values. The binary GA convergence property is discussed and a new convergence prediction method is proposed. The results of the prediction algorithm effectiveness investigation over the set of complex continuous and discrete deceptive problems are presented.

Introduction I N the last years genetic algorithms (GAs) have received much attention as a powerful design tool in many areas of aerospace engineering. These probabilistic-typemethods, based on the principle of the natural evolution, have shown their effectiveness and robustness in a wide range of optimizationproblems. i 5 Compared with classical deterministic approaches, GAs’ superior effectiveness in performing optimization tasks is mainly from some speciŽ c features such as the capability to handle simultaneously continuous integer and discrete design variables, a parallel-like searching method leadingto a greatereffectivenessin Ž ndingglobalminimum within the design space. Moreover, as genetic-type optimization is guided only by a function of merit (or Ž tness) value, no mathematical informationon the objective function is required.Therefore function discontinuitiescan be easily managed. In this Note an application in the Ž eld of aircraft conceptual design is described. A genetic optimizer has been coupled with a sizing code to deŽ ne a short/medium range preliminary aircraft conŽ guration,powered by turbofan engines, fully compliant with given requirements and allowingminimumdirect operatingcost. Parametric formulas6 i 8 have been used for aerodynamics, weights, and low-speed performance calculation.Rationale for GA application in conceptualdesignwith reference to selection and sizing is well presented in Ref. 9. Other relevantexamplesof geneticapproachin aircraftdesignare reported in Refs. 10–13.

Genetic algorithms (GAs) have become a popular optimization tool for many areas of research and topology optimization an effective design tool for obtaining efficient and lighter structures. In this paper, a versatile, robust and enhanced GA is proposed for structural topology optimization by using problem-specific knowledge. The original discrete black-and-white (0–1) problem is directly solved by using a bit-array representation method. To address the related pronounced connectivity issue effectively, the four-neighbourhood connectivity is used to suppress the occurrence of checkerboard patterns. A simpler version of the perimeter control approach is developed to obtain a well-posed problem and the total number of hinges of each individual is explicitly penalized to achieve a hinge-free design. To handle the problem of representation degeneracy effectively, a recessive gene technique is applied to viable topologies while unusable topologies are penalized in a hierarchical manner. An efficient FEM-based function evaluation method is developed to reduce the computational cost. A dynamic penalty method is presented for the GA to convert the constrained optimization problem into an unconstrained problem without the possible degeneracy. With all these enhancements and appropriate choice of the GA operators, the present GA can achieve significant improvements in evolving into near-optimum solutions and viable topologies with checkerboard free, mesh independent and hinge-free characteristics. Numerical results show that the present GA can be more efficient and robust than the conventional GAs in solving the structural topology optimization problems of minimum compliance design, minimum weight design and optimal compliant mechanisms design. It is suggested that the present enhanced GA using problem-specific knowledge can be a powerful global search tool for structural topology optimization. Copyright © 2005 John Wiley & Sons, Ltd.

Predictive models that use machine learning techniques has been useful tools to guide software project managers in making decisions under uncertainty. However in practice collecting metrics or defect data has been a troublesome job and researchers often have to deal with incomplete datasets in their studies. As a result both researchers and practitioners shy away from implementing such models. Missing data is a common problem in other domains to build recommender systems. We believe that the techniques used to overcome missing data problem in other domains can also be employed in software engineering. In this paper we propose Matrix Factorization algorithm to tackle with missing data problem in building predictive models in software development domain.

This paper proposes a Sequential Approximate Optimization (SAO) for the discrete design variable problems using Radial Basis Function (RBF) Network. We consider that there are two important keys for SAO: One is the parameter adjustment for good approximation, and the other is to explore the sparse region for global approximation. The authors have proposed the simple estimate of the width in the Gaussian Kernel for good approximation. In addition, in order to explore the sparse region, we have developed the density function. The density function with the simple estimate of the width works well in the case of the continuous design variables. However, a simple application of the density function to the discrete design variable problems will cause some difficulties. In order to overcome these difficulties and find the sparse region of the discrete design variables with the density function, the new variables for the discrete design variables are introduced. By using the new variables, it is possible to find the sparse region of the discrete design variables. A simple sampling algorithm is shown, in which the Discrete Differential Evolution (DDE) for the discrete design variables is employed. Through typical numerical examples, the validity of proposed approach is examined.

This paper describes a new approach for reducing the number of the fitness and constraint function evaluations required by a genetic algorithm (GA) for optimization problems with mixed continuous and discrete design variables. The proposed modification improves the efficiency of the memory constructed in terms of the continuous variables. The work presents the algorithmic implementation of the proposed memory scheme and demonstrates the efficiency of the proposed multivariate approximation procedure for the weight optimization of a segmented open cross section composite beam subjected to axial tension load. Results are generated to demonstrate the advantages of the proposed improvements to a standard genetic algorithm.

Genetic algorithms (GAs) are a stochastic search method for optimization problems based on the mechanics of natural selection and natural genetics (i.e., survival of the fittest). GAs have demonstrated considerable success in providing good solutions to many complex optimization problems and received more and more attentions during the past three decades. When the objective functions to be optimized in the optimization problems are multimodal or the search spaces are particularly irregular, algorithms need to be highly robust in order to avoid getting stuck at a local optimal solution. The advantage of GAs is just able to obtain the global optimal solution fairly. In addition, GAs do not require the specific mathematical analysis of optimization problems, which makes GAs themselves easily coded by users who are not necessarily good at mathematics and algorithms.KeywordsGenetic AlgorithmNash EquilibriumGenetic OperatorSoft ConstraintRoulette WheelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Mechanical sizing of a composite launcher structure by hybridizing a genetic algorithm with a local search method

A modification to the genetic algorithm (GA) based search procedure, based on the modeling of a biological immune system, is proposed as an approach to solving the multicriterion design problem. Such problems have received considerable attention, given that decisions in engineering design practice typically require allocation of resources to satisfy multiple, and frequently conflicting requirements. The approach is particularly amenable to problems with a mix of continuous, discrete, and integer design variables, where the GA has been shown to perform in an effective manner. The approach considered in the present work is based on the concept of converting the multicriterion problem into one with a scalar objective through the use of the utility function. The strength of the approach is in its ability to generate the Pareto-Edgeworth front of compromise solutions in a single execution of the GA. A characteristic feature of biological immune systems which allows for the generation of multiple specialist antibodies, is shown to be an effective approach to facilitate the generation of the Pareto-Edgeworth front. Solutions to problems in structural design are presented in support of the proposed approach.

Automotive body frames, which profoundly affect automotive performance such as crashworthiness, are generally formed using pressed metal sheets, and the assembled cross-sectional shapes govern the frame characteristics. This paper proposes a cross-sectional shape generating method for achieving the cross-sectional properties assigned by design engineers. The cross-sectional shape-generating problem for pressed metal sheets is formulated as a multiobjective optimization problem that involves a marriage of continuous variables, such as shape dimensions, and discrete design variables, such as types of material and their thicknesses. Genetic algorithms are applied to solve the optimization problem.