Metaheuristic optimization with MapReduce

In today's world, number of the functions that needs to be optimized and parameters of these functions are increasing and optimization of these parameters has significant importance, in this regard; a metaheuristic optimization algorithm has broad usage. However these algorithms can be more effective if they can work in distributed environment. It has been noticed that such a distributed optimization applications exist but an application in MapReduce platform has not be done yet. In this regard MapReduce framework has been chosen to work with. With MapReduce framework bigger problems may be solved with lesser cost and time. In this thesis, optimization is inspected, some of the metaheuristic algorithms are explained, a novel optimization algorithm is designed, an introduction to MapReduce framework and finally an optimization application with MapReduce framework is provided.