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URN etd-0805114-160832 Statistics This thesis had been viewed 1362 times. Download 1276 times. Author GUAN-SYUN HUNG Author's Email Address No Public. Department Computer Science and Enginerring Year 2013 Semester 2 Degree Master Type of Document Master's Thesis Language zh-TW.Big5 Chinese Page Count 87 Title A Study on Hybrid Particle Swarm Optimization and Simplex Search to Solve Multiple Objective Problems Keyword Pareto front simplex search method Archive controller Multiple objective Particle swarm optimization Particle swarm optimization Multiple objective Archive controller simplex search method Pareto front Abstract Multi-objective optimization problems is a type of problem where trade-offs must to made among multiple objectives in order to select the most appropriate choice. For example, a company will have to trade-off between profit and quality during production. This research combines the advantages of Nelder-Mead and particle swarm optimization and develops a new algorithm to solve this type of problem. To verify this algorithm, The computational results of nine benchmark multiple objective functions taken from the literature are compared with other results found in the literature. The results show this algorithm can effectively deal with multi-objective optimization problems and converges faster while looking for non-dominated solutions. Advisor Committee Yi-Tung Kao - advisor
Files Date of Defense 2014-07-14 Date of Submission 2014-08-06