Title page for etd-0806115-162831


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URN etd-0806115-162831
Author Yao-ting Huang
Author's Email Address No Public.
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Department Computer Science and Engineering
Year 2015
Semester 1
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Non-dominated Sorting Firefly Algorithm for Multi-objective Optimization
Date of Defense 2015-08-27
Page Count 62
Keyword
  • NSGA-II
  • pareto optimality
  • multi-objective problems
  • firefly algorithm
  • functional optimization
  • Abstract The so-called multi-objective optimization problem (MOP) has become a critical research subject because many multi-objective optimization problems exist in our daily life. Unlike solving a single-objective problem, solving a multi-objective optimization problem requires that many conflicting objectives be optimized altogether at the same time. Instead of finding a single solution as in the single-objective problem, how to find approximate solutions or a pareto set within a reasonable time has become an active research topic in recent years. In this thesis, we present a high-performance algorithm that leverages the strengths of firefly algorithm (FA) and a fast and elitist non-dominated sorting genetic algorithm (NSGA-II). In order to get a more uniformly distributed and completed pareto set, we also propose a new way to determine the crowding distance. Simulation results show that the proposed algorithm can provide a better result than all the state-of-the-art multi-objective optimization algorithms compared in this thesis in most cases.
    Advisory Committee
  • Tzung-Pei Hong - chair
  • Chun-Wei Tsai - co-chair
  • Ming-chao Chiang - advisor
  • Files
  • etd-0806115-162831.pdf
  • Indicate in-campus at 99 year and off-campus access at 99 year.
    Date of Submission 2015-09-07

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