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URN etd-0815114-162246 Statistics This thesis had been viewed 1344 times. Download 379 times. Author Hsuan-Hung Hsin 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 41 Title A Hybrid Evolutionary Algorithm for Multi-Objective Optimization of Synthesis Gas Production Keyword Pareto front Synthesis gas multi-objective Particle Swarm Optimization Nelder-Mead simplex method Nelder-Mead simplex method Particle Swarm Optimization multi-objective Synthesis gas Pareto front Abstract A combined process of gas reforming is often used in chemical industry for different demands. In this work, hybridized Nelder-Mead simplex method and particle swarm optimization algorithm is employed to solve multi-objective problems. Some effects were expected by hybrid strengths and weaknesses of the two algorithms. The use of archive controller keeps each Pareto solution found during computing. By using the same measurement method, it was shown that hybrid evolutionary algorithm outperforms general evolutionary algorithms. In addition, the experimental results compared favorably with those found in the literature in terms of the degree of convergence and the dispersion of particles. This study demonstrates that the hybrid method is superior to PSO, and that the hybrid algorithm can effectively handle multi-objective optimization problems. Advisor Committee Yi-Tung Kao - advisor
Files Date of Defense 2014-07-28 Date of Submission 2014-08-15