| URN |
etd-0806115-162831 |
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| Author |
Yao-ting Huang |
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| Author's Email Address |
No Public. |
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| Statistics |
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| Department |
Computer Science and Engineering |
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| Year |
2015 |
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| Semester |
1 |
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| Degree |
Master |
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| Type of Document |
|
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| Language |
zh-TW.Big5 Chinese |
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| Title |
Non-dominated Sorting Firefly Algorithm for Multi-objective Optimization |
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| Date of Defense |
2015-08-27 |
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| Page Count |
62 |
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| Keyword |
NSGA-II
pareto optimality
multi-objective problems
firefly algorithm
functional optimization
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| 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. |
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| Advisory Committee |
Tzung-Pei Hong - chair
Chun-Wei Tsai - co-chair
Ming-chao Chiang - advisor
|
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| Files |
Indicate in-campus at 99 year and off-campus access at 99 year. |
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| Date of Submission |
2015-09-07 |
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