This study aims to develop a computer program to optimize the performance of loader mechanisms in backhoe-loaders. The complexity and the constraints imposed on the loader mechanism does not permit the use of classical optimization techniques used in the synthesis of mechanisms. Genetic algorithm is used to determine the values of the design parameters of the mechanism while satisfying the constraints and trying to maximize breakout forces that the machine can generate.

The realization of planning, design, construction, operation and maintenance of water supply systems pictures one of the largest infrastructure projects of municipalities / water distribution networks should be designed very meticulously. Genetic algorithm is an optimization method that is based on natural evolution and is used for the optimization of water distribution networks. Genetic algorithm is comprised of operators and the operators affect the performance of the algorithm. Although these operators are related with parameters, not much attention has been given for the determination of these parameters for this specific field of water distribution networks. This study represents a novel methodology, which investigates the parameters of the algorithm for different networks. The developed computer program is applied to three networks. Two of these networks are well known examples from the literature / the third network is a pressure zone of Ankara water distribution network. It is found out that, the parameters of the algorithm are related with the network, the case to be optimized and the developed computer program. The pressure penalty constant value varied depending on the pipe costs and the network characteristics. The mutation rate is found to vary in a range of [0.0075 &ndash / 0.0675] for three networks. Elitism rate is determined as the minimum value for the corresponding population size. Crossover probability is found to vary in a range of [0.5 &ndash / 0.9]. The methodology should be applied to determine the appropriate parameter set of genetic algorithm for each optimization study. Using the method described, fairly well results are obtained.

We consider a hierarchical maximal covering location problem to locate p health centers and q hospitals in such a way that maximum demand is covered, where health centers and hospitals have successively inclusive hierarchy. Demands are 3 types: demand requiring low-level service only, demand requiring high-level service only, and demand requiring both levels of service at the same time. All types of requirements of a demand point should be either covered by hospital providing both levels of service or referred to hospital via health center since a demand point is not covered unless all levels of requirements are satisfied. Thus, a health center cannot be opened unless it is suitable to refer its covered demand to a hospital. Referral is defined as coverage of health centers by hospitals. We also added partial coverage to this complex hierarchic structure, that is, a demand point is fully covered up to the minimum critical distance, non-covered after the maximum critical distance and covered with a decreasing quality while increasing distance to the facility between minimum and maximum critical distances. We developed an MIP formulation to solve the Hierarchical Maximal Covering Location Problem with referral in the presence of partial coverage. We solved small-size problems optimally using GAMS. For large-size problems we developed a Genetic Algorithm that gives near-optimal results quickly. We tested our Genetic Algorithm on randomly generated problems of sizes up to 1000 nodes.

We investigate the assignment problem, which is the problem of matching two sets with each other, optimizing a given function on the possible matchings. Among different definitions, a graph theoretical definition of the linear sum assignment problem is as follows: Given a weighted complete bipartite graph, find a maximum (or minimum) one-to-one matching between the two equal-size sets of the graph, where the score of a matching is the total weight of the matched edges. We investigate extensions and variations like the incremental assignment problem, maximum subset matching problem, maximum-weighted tree matching problem. We present a genetic algorithm scheme for maximum-weighted tree matching problem, and experimental results of our implementation.

Application of optimization techniques for determining the optimal operating policy for reservoirs is a major title in water resources planning and management. Genetic algorithms, ruled by evolution techniques, have become popular for solving optimization problems in diversified fields of science. The main aim of this research was to explore the efficiency and effectiveness of the applicability of genetic algorithm in optimization of multi-reservoirs. A computer code has been constructed for this purpose and verified by means of a reference problem with a known global optimum. Three reservoirs in the Colorado River Storage Project were optimized for maximization of energy production. Besides, a real-time approach utilizing a blend of online and a posteriori data was proposed. The results achieved were compared to the real operational data and genetic algorithms were found to be effective, competitive and can be utilized as an alternative technique to other traditional optimization techniques.

This study concerns with the automated structural optimization of an excavator boom. The need for this work arises due to the fact that the preparation of the CAD model, performing finite element analysis and model data evaluation are time consuming processes and require experienced man power. The previously developed software OptiBOOM, which generates a CAD model using a finite set of parameters and then performs a finite element analysis by using a commercial program has been modified. The model parameter generation, model creation, analysis data collection and data evaluation phases are done by the Python and Delphi based computer codes. A global heuristic search strategy such as genetic algorithm is chosen to search different boom models and select an optimum.

The aim of this study is to perform the shape optimization of the lower chassis of the wheeled excavator. A computer program is designed to generate parametric Finite Element Analysis (FEA) of the structure by using the commercial program, MSC. Marc-Mentat. The model parameters are generated in the Microsoft Excel platform and the analysis data is collected by the Python based computer codes. The previously developed software Smart Designer [5], which performs the shape optimization of an excavator boom by using genetic algorithm, is modified and embedded in the designed program.

An aerodynamic design and optimization tool for wind turbines is developed by using both Blade Element Momentum (BEM) Theory and Genetic Algorithm. Turbine blades are optimized for the maximum power production for a given wind speed, a rotational speed, a number of blades and a blade radius. The optimization variables are taken as a fixed number of sectional airfoil profiles, chord lengths, and twist angles along the blade span. The airfoil profiles and their aerodynamic data are taken from an airfoil database for which experimental lift and drag coefficient data are available. The BEM analysis tool developed is first validated with the experimental data for low wind speeds. A 100 kW wind turbine, which is used in the validation, is then optimized. As a result of the optimization, the power production is improved by 40 to 80 percent. The optimization methodology is then employed to design a 1MW wind turbine with a 25m radius.

In this study, a design procedure incorporating a genetic algorithm (GA) is developed for optimization of the wing structure of a two seated trainer aircraft with single turboprop engine. The objective function considered is the total weight of the structure. The objective function is minimized subjected to certain strength requirements. In order to evaluate the design constraints and model the wing structure, finite element analysis is performed by using a conventional finite element solver (i.e. MSC/NASTRAN&reg / ). In addition, MSC/PATRAN&reg / commercial package program is used as preprocessor and postprocessor tool. VISUAL FORTRAN programming language is also utilized as the genetic algorithm implementation tool. Several conclusions drawn from the optimization results are presented.

The growing extent and variety of present military operations forces to use the resources in hand at its best. Especially, the optimum usage and assignment of limited number of the air force resources to missions will provide a considerable advantage in the battle field. The problem of finding the feasible and optimum assignment has been known to be studied / yet performing the process faster is still a topic that captures researchers&rsquo / attention because of the computational complexity that the assignment problem involves within. In this thesis, exploring the optimal assignment of fleets/aircrafts to targets/groups of targets is going to be performed via algorithms and heuristics. As the best choice for finding the exact solution, Branch-and-Bound algorithm, which is an intelligent way of searching for the solution on a solution tree where the nodes with potential of not leading to the solution are fathomed, has been investigated and applied according to the specific problem needs. The number of nodes on the search tree increases exponentially as the problem size increases. Moreover / as the size of the assignment problem increases, attaining the solution solely by Branch-and-Bound algorithm is definitely computationally expensive due to memory and time requirements. Therefore, Genetic algorithm which can provide good solutions in a relatively short time without having computational difficulties is considered as the second algorithm. Branch-and-Bound algorithm and Genetic algorithm are separately used for obtaining the solution. Hybrid algorithms which are combinations of Branch-and-Bound and Genetic algorithms are used with heuristics for improving the results.