There are many diverse applications that are mathematically modelled in terms of mixed discrete-continuous variables. The optimization of these models is typically difficult due to their combinatorial nature and potential existence of multiple local minima in the search space. Genetic algorithms (GAs) are powerful tools for solving such problems. GAs do not require gradient or Hessian information. However, to reach an optimal solution with a high degree of confidence, they typically require a large number of analyses during the optimization search. Performance of these methods is even more of an issue for problems that include continuous variables. The work here enhances the efficiency and accuracy of the GA with memory using multivariate approximations of the objective and constraint functions individually instead of direct approximations of the overall fitness function. The primary motivation for the proposed improvements is the nature of the fitness function in constrained engineering design optimization problems. Since GAs are algorithms for unconstrained optimization, constraints are typically incorporated into the problem formulation by augmenting the objective function of the original problem with penalty terms associated with individual constraint violations. The resulting fitness function is usually highly nonlinear and discontinuous, which makes the multivariate approximation highly inaccurate unless a large number of exact function evaluations are performed. Since the individual response functions in many engineering problems are mostly smooth functions of the continuous variables (although they can be highly nonlinear), high quality approximations to individual functions can be constructed without requiring a large number of function evaluations. The proposed modification improve the efficiency of the memory constructed in terms of the continuous variables. The dissertation 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. / Ph. D.

Genetic algorithms are well known for being expensive optimization tools, especially if the cost for the analysis of each individual design is high. In the past few years, significant effort has been put forth in addressing the high computational cost GAs. The research conducted in the first part of this thesis continues this effort by implementing new multiple elitist and variable elitist selection schemes for the creation of successive populations in the genetic search process. The new selection schemes allow the GA to take advantage of a greater amount of important genetic information that may be contained in the parent designs, information that is not utilized when using a traditional elitist method selection scheme. By varying the amount of information that may be passed to successive generations from the parent population, the explorative and exploitative characteristics of the GA can be adjusted throughout the genetic search also. The new schemes provided slight reductions in the computational cost of the GA and produced many designs with good fitness' in the final population, while maintaining a high level of reliability. Genetic algorithms can be easily adapted to many different optimization problems also. This capability is demonstrated by modifying the basic GA, which utilizes a single chromosome string, to include a second string so that composite laminates comprised of multiple materials can be studied with greater efficiently. By using two strings, only minor adjustments to the basic GA were required. The modified GA was used to simultaneously minimize the cost and weight of a simply supported composite plate under different combinations of axial loading. Two materials were used, with one significantly stronger, but more expensive than the other. The optimization formulation was implemented by using convex combinations of cost and weight objective functions into a single value for laminate fitness, and thus required no additional modifications to the GA. To obtain a Pareto-optimal set of designs, the influence of cost and weight on the overall fitness of a laminate configuration was adjusted from one extreme to the other by adjusting the scale factors accordingly. The modified GA provided a simple yet reliable means of designing high performance composite laminates at costs lower than laminates comprised of one material. / Master of Science

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yes / Condensate stabilization is a process where hydrocarbon condensate recovered from natural gas reservoirs is processed to meet the required storage, transportation, and export specifications. The process involves stabilizing of hydrocarbon liquid by separation of light hydrocarbon such as methane from the heavier hydrocarbon constituents such as propane. An industrial scale back-up condensate stabilization unit was simulated using Aspen HYSYS software and validated with the plant data. The separation process consumes significant amount of energy in form of steam. The objectives of the paper are to find the minimum steam consumption of the process and conduct sensitivity and exergy analyses on the process. The minimum steam consumption was found using genetic algorithm optimization method for both winter and summer conditions. The optimization was carried out using MATLAB software coupled with Aspen HYSYS software. The optimization involves six design variables and four constraints, such that realistic results are achieved. The results of the optimization show that savings in steam consumption is 34% as compared to the baseline process while maintaining the desired specifications. The effect of natural gas feed temperature has been investigated. The results show that steam consumption is reduced by 46% when the natural gas feed temperature changes from 17.7 to 32.7°C. Exergy analysis shows that exergy destruction of the optimized process is 37% less than the baseline process.

In this study, the parameters of population, gross domestic product (GDP), heat price, U-value, and temperature have been used to predict heat consumption for Sweden till 2050. It should be noted that the heat consumption has been considered for multi-family houses. Most multi-family houses (MFH) get their primary heat from district heating (DH). A literature analysis of various models and variables has been conducted to enhance comprehension of forecasting and its process. The majority of earlier research has focused on electricity or energy rather than heat. The aim of this study is to create a model (linear and non-linear) from 1993 to 2019 with a minimum error as possible, and then use the genetic algorithm (GA) and neural network (NN) to predict Sweden's heat consumption till 2050

No / A new approach in achieving beam steering in array antenna is introduced using the genetic algorithm optimization. The binary switching technique uses simple binary ON/OFF diodes placed in the feeding network of the array element to achieve beam steering. Constantly feeding the driven element and continuous binary variation of the ON/OFF state of each parasitic array elements which determines its conducting ability defines a beam steering angle. Each beam steered angle is distinguished by series of binary combination determined by the genetic algorithm. A uniform circular array antenna consisting of 13 elements is used to implement this technique. The simulation and result analysis of the binary switched array is presented with several beam steering angles scanned.

A pre-optimizer has been developed which modifies existing turbomachinery blades to create new geometries with improved selected aerodynamic coefficients calculated using a linear panel method. These blade rows can then be further refined using a Navier-Stokes method for evaluation. This pre-optimizer was developed in hopes of reducing the overall CPU time required for optimization when using only Navier-Stokes evaluations. The primary method chosen to effect this optimization is a parallel evolutionary algorithm. Variations of this method have been analyzed and compared for convergence and degree of improvement. Test cases involved both single and multiple row turbomachinery. For each case, both single and multiple criteria fitness evaluations were used.

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