Sub-population Genetic Algorithm II for Multi-objective Parallel Machine Scheduling Problems
碩士 === 元智大學 === 資訊管理學系 === 99 === In recent years, industrial manufacturing usually faces the tradeoff of multi-objective decision problems. Many researchers have become more aware of the efficiency of heuristics for solving multi-objective problems. In this paper, we improve the previous SPGA appro...
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| Format: | Others |
| Language: | zh-TW |
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2011
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| Online Access: | http://ndltd.ncl.edu.tw/handle/30519543702160904149 |
| Summary: | 碩士 === 元智大學 === 資訊管理學系 === 99 === In recent years, industrial manufacturing usually faces the tradeoff of multi-objective decision problems. Many researchers have become more aware of the efficiency of heuristics for solving multi-objective problems. In this paper, we improve the previous SPGA approach and present a Sub-population Genetic Algorithm II (SPGA-II). SPGA-II takes advantage of the Tchebycheff Decomposition and effective Pareto Fronts and Reference Points generated during the evolutionary process to enhance the performance of the proposed approach. Our experimental results show that SPGA-II is able to improve the performance of SPGA in solving Parallel Machine Scheduling Problems.
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