基于自适应Pareto存档进化策略的多目标优化

5th International Conference on Intelligent Systems Design and Applications (ISDA'05) Pub Date : 2005-09-08 DOI:10.1109/ISDA.2005.69

Mihai Oltean, C. Grosan, A. Abraham, M. Köppen

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引用次数: 21

摘要

针对某股票建模问题，提出了一种新的自适应进化多目标优化算法。标准的Pareto实现进化策略(PAES)使用实数或二进制表示来编码解决方案。自适应帕累托存档进化策略(APAES)使用动态字母编码解决方案。应用APAES对涉及4个目标函数的两种流行股票指数进行建模。此外，还使用两个多目标优化基准测试函数来说明算法的性能。实证结果表明，与标准PAES相比，APAES具有良好的性能。

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Multiobjective optimization using adaptive Pareto archived evolution strategy

This paper proposes a novel adaptive representation for evolutionary multiobjective optimization for solving a stock modeling problem. The standard Pareto achieved evolution strategy (PAES) uses real or binary representation for encoding solutions. Adaptive Pareto archived evolution strategy (APAES) uses dynamic alphabets for encoding solutions. APAES is applied for modeling two popular stock indices involving 4 objective functions. Further, two bench mark test functions for multiobjective optimization are also used to illustrate the performance of the algorithm. Empirical results demonstrate APAES performs well when compared to the standard PAES,.

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5th International Conference on Intelligent Systems Design and Applications (ISDA'05)

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