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In the optimization method, there are several algorithms to solve optimization problems, such as deterministic algorithms that use gradient information. For example, Newton Raphson method which uses the function value and its derivatives that works well for unimodal problems (single optimum value)....
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Format: | Final Project |
Language: | Indonesia |
Online Access: | https://digilib.itb.ac.id/gdl/view/19371 |
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Institution: | Institut Teknologi Bandung |
Language: | Indonesia |
Summary: | In the optimization method, there are several algorithms to solve optimization problems, such as deterministic algorithms that use gradient information. For example, Newton Raphson method which uses the function value and its derivatives that works well for unimodal problems (single optimum value). However, when it is used in multimodal objective function, the method is shows poor result because it will get stuck on the locals value. therefore, the method is not based on the gradient is needed. An example of algorithm that does not use the gradient information is metaheuristics that can be classified in some ways, such as : population-based and trajectory . Cuckoo Search is classified as population-based metaheuristic method because its application requires a number of bird populations to find optimum value. Another example of population based method is Particle Swarm Optimization ( PSO ). As for the metaheuristic algorithm based on the trajectory, Simulated Annealing is an example. Cuckoo Search is a method that inspired by cuckoo bird behaviors that nurture their children parasitically. This method is used to find the global optimum value, both maximum and minimum value. The model presented in this thesis is a system of linear equations which is later converted into a global optimization problem and nonlinear equations with constraints which are converted into unconstraint problems using a penalty function, then calculated using Cuckoo Search. |
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