Solving Simulation Optimization Problems Using Artificial Bee Colony and Ranking and Selection Methods

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Firooze, Hamid Reza | 2011

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 41283 (01)
University: Sharif University of Technology
Department: Industrial Engineering
Advisor(s): Akhavan Niaki, Taghi
Abstract:
In this thesis the simulation optimization problems are solved by using Artificial Bee Colony (ABC). The main objective is to improve and adapt the ABC algorithm for solving the optimization problems in deterministic and stochastic environments. For solving deterministic problems, directed search in neighborhood and Nelder-Mead algorithm are combined with ABC algorithm to improve the convergence rate and solutions. Moreover; in stochastic environment, hypothesis test and Kim-Nelson (KN) indifference zone ranking and selection procedure are helping bees to produce solutions with better confidence level on the quality of the solution. Results of optimizing an extensive complex benchmark problems show the high performance of the proposed algorithms
Keywords:
Optimization ; Simulation ; Nelder-Mead Method ; Hypothesis Test ; Artificial Bee Colony ALgorithm ; Directed Search ; Kim-Nelson Method

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