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Control and Shape Optimization of Wave Energy Converters
Published in Ossama Abdelkhalik, Algorithms for Variable-Size Optimization, 2021
Another case study is here presented. Here we assume the buoy shape is divided into multiple lateral sections. The classical cylindrical buoy consists of one section, of a cylindrical shape, with a constant cross section area. Here, we assume multiple sections, each section may have a different shape. For example, a buoy may have three sections, the top one is cylindrical, the middle one is spherical, and the bottom one is conical. The discussion presented in Section 2.3 shows that it is possible to compute the nonlinear Froude Krylov force for each section separately, and combine them. Section 2.3 also shows how to compute the nonlinear Froude Krylov force for different shapes. Here is this section, we let the optimizer search for the optimal shapes of all sections (section elements), along with the optimal control coefficients. Genetic algorithms are used for optimization.
Direct computation of Variable Speed Pumps for water distribution system analysis
Published in Bogumil Ulanicki, Kalanithy Vairavamoorthy, David Butler, Peter L.M. Bounds, Fayyaz Ali Memon, Water Management Challenges in Global Change, 2020
E. Todini, M.E. Tryby, Z.Y. Wu, T.M. Walski
The hybrid genetic algorithm combines genetic algorithm and simulated annealing algorithm (Ilich & Simonovic, 1998). The principal advantage of genetic algorithms is their inherent ability to intelligently explore the solution space from many different points simultaneously enabling higher probability for locating global optimum without having to analyze all possible solutions available and without requiring derivatives (or numerical approximations) or other auxiliary knowledge. Simulated annealing algorithm has an advantage of local search ability, The combination of genetic algorithm and simulated annealing algorithm, learning from each other's strong points to offset one's weakness, develops an eximious global search algorithm. And relevant program was developed. The proposed program was tested and verified on a actual large-scale water distribution systems in Tianjin city, China.
Resource allocation in an LTE-A network for Machine Type Communications (MTC) over a Wi-Fi spectrum under an LAA framework
Published in Artde D.K.T. Lam, Stephen D. Prior, Siu-Tsen Shen, Sheng-Joue Young, Liang-Wen Ji, Engineering Innovation and Design, 2019
Rosabella Ika Yuanita, Ding-Bing Lin, Gamantyo Hendrantoro
A genetic algorithm is an heuristic search algorithm inspired by Darwin’s theory of natural selection. The algorithm is solved iteratively, beginning with a randomly generated group composed of solutions represented by the number of chromosomes, which is also known as one population. By referring to the system‘s throughput comparison result when using Taguchi’s method as an optimization algorithm, we can determine the Proportional Fair (PF) scheduler as a fitness function in the evaluation phase. We can then perform the selection process by using roulette wheel selection to produce offsprings. This will pick the chromosome with high probability, which is proportionate to its fitness. If the random number generation is smaller than the crossover probability, then the next step will be to undergo the crossover procedure. The last step in the GA is mutation, which occurs when the random number generation is smaller than the mutation probability. The algorithm can be stopped if it no longer produces a greater improvement in the fitness of the best individual for successive iterations. Table 3 represents the GA simulation parameters.
Gradient-Informed Design Optimization of Select Nuclear Systems
Published in Nuclear Science and Engineering, 2022
John Pevey, Briana Hiscox, Austin Williams, Ondřej Chvála, Vladimir Sobes, J. Wesley Hines
A genetic algorithm22 for challenge problem 1B is coded in Python with KENO used as the keff objective solver. The same geometry as challenge problem 1A is used. A genetic algorithm is a global optimization algorithm that uses ideas such as mutation, crossover (breeding), and survival of the fittest. While not guaranteed to find the optimal solution, given a correctly tuned set of hyperparameters, heuristics, and computation time, genetic algorithms often are successfully implemented to help optimize nuclear systems. The hyperparameters used in the analysis presented in this work can be found in Table I. An additional simple heuristic was implemented to reduce redundant individuals from being produced using hashing of the geometries. A mutation is forced in any individual that has been found to be a copy of a previously evaluated individual.
Alternative subgraphs assembly line balancing problem with resource selection and parallel stations
Published in Engineering Optimization, 2022
Daria Leiber, Anh-Tu Vuong, Gunther Reinhart
A genetic algorithm (Figure 3) is used to solve the optimization problem, which is described in the following section. A genetic algorithm is a heuristic optimization algorithm inspired by the natural process of evolution. It generates a population of individuals that represent potential solutions for the optimization problem, evaluates their fitness in terms of the optimization objective and then creates the next generation. The idea is that by using suitable genetic operators, the individuals’ fitness increases from one generation to the next, approximating the optimal solution (Mitchell 1999). In a genetic algorithm, the fitness is usually maximized. To address the goal of minimizing cost, the fitness of an individual is defined as the negation of the costs to be minimized according to the optimization objective (Equation 1).
Coned Disc Spring Compound Vertical Isolation: Testing and Modelling
Published in Journal of Earthquake Engineering, 2022
Wei Wang, Xingxing Wang, Aiqun Li
Crossover and mutation operations are mainly used to generate new individuals and to improve the global and local search capabilities of GAs while maintaining population diversity and preventing premature phenomena. In genetic algorithms, the crossover is a genetic operator used to combine the genetic information of two parents to generate new offspring. It is one way to stochastically generate new solutions from an existing population, and analogous to the crossover that happens during sexual reproduction in biology. The mutation is a genetic operator used to maintain genetic diversity from one generation of a population of genetic algorithm chromosomes to the next. It is analogous to biological mutation. The mutation alters one or more gene values in a chromosome from its initial state. In mutation, the solution may change entirely from the previous solution. Hence, GA can come to a better solution by using mutation.