| Summary: | 碩士 === 元智大學 === 工業工程與管理學系 === 98 === The virus, an infectious agent that can reproduce only inside a host cell can apparently spread without any control, but as we already know that the cells without any protection will tend to give better chances to the virus in the reproduction activity. In this thesis, we developed a novel metaheuristic, named Virus Optimization Algorithm (VOA) which imitates the behavior of the virus, this metaheuristic could be considered as a type of evolutionary algorithms (EA) since mechanisms that simulate reproduction and population maintenance are performed. The host cell represents the entire search space while the virus reproduction denotes the generation of new solutions. VOA is a population-based method that usually begins the search with a small set of solutions and the number of those solutions will grow at each iteration and a mechanism called antivirus will be in charge of maintain the population of viruses, the whole process will be repeated until the stopping criterion is reached. We compared this new metaheuristic with three widely known algorithms in the EA area such as Genetic Algorithm (GA), Harmony Search (HS), and Particle Swarm Optimization (PSO), where the problem solved was the continuous curve fitting by using generalized mixture gaussian models for nine stock index markets. As a conclusion from the tests performed the proposed VOA showed to be a competitive and robust tool for solving continuous optimization problems.
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