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    Please use this identifier to cite or link to this item: http://ir.lib.ksu.edu.tw/handle/987654321/3647


    Title: 一個兼具效率與效能的多目標基因演算法
    An efficient and effective multiobjective genetic algorithm
    Authors: 林張群
    曾生元
    Keywords: 多目標最佳化
    效率前緣
    多目標基因演算法
    數學交換運算
    Multiobjective optimization
    Pareto front
    Multiobjective genetic algorithm
    Mathematical crossover operator
    Date: 2006-10-30
    Issue Date: 2009-08-15 18:15:41 (UTC+8)
    Abstract: 多目標基因演算法可以在一個族群中同時獲得許多Pareto 解,因此成為解多目標最佳化問題的利器,特別是對非凸或呈現片段的效率前緣而言。目前最佳的某些多目標基因演算法,如IMOEA、NSGA-II 與 MOPSO,都相當複雜與低效率。本研究提出一個使用數學交換運算子的多目標基因演算法,此演算法可能是目前為止最簡單的一個。由於缺乏一個通用的比較基準,多目標基因演算法的效能很少被比較,即使有,比較的方法大多過於主觀而不一致。為了解決此一問題,本研究也提出了一個測量效率前緣效度的方法,並據此將所提出的演算法與IMOEA、NSGA-II、MOPSO 進行比較。結果顯示,本研究之方法不僅極具效率與效能,其表現也不會隨問題的不同而有大幅的變化。Capable of processing many solutionsin a population and thus obtaining manyPareto solutions simultaneously,multiobjective genetic algorithms arenaturally conducive to dealing withmultiobjective optimization, especially whenthe Pareto front is non-convex or segmented.Some state-of-the-art genetic algorithms formultiobjective optimization, such asIMOEA, NSGA-II and MOPSO, are quitecomplex and inefficient. This study proposesa multiobjective genetic algorithm equippedwith a mathematical crossover operator,which is probably the simplest algorithm yetfound. Since the lack of a general metric for measuring the quality of Pareto fronts,algorithm efficiencies are rarely compared,and the comparison criteria are subjectiveand inconsistent. To resolve this problem, auniversal criterion for measuring the qualityand maturity of Pareto fronts is alsodeveloped. The proposed algorithm iscompared with the IMOEA, NSGA-II andMOPSO based on this metric, revealing thatthe proposed algorithm is not only efficientand effective, but also robust formultiobjective optimization problems.
    Appears in Collections:[資訊管理系所] 研究計畫

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