Hybrid chaos optimization algorithm with artificial emotion
Hybrid chaos optimization algorithm with artificial emotion
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DOI：
10.1016/j.amc.2011.09.028
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发表时间：
2012-02
期刊：
影响因子：
--
通讯作者：
Yimin Yang;Yaonan Wang;X. Yuan;F. Yin
中科院分区：
文献类型：
--
作者：
Yimin Yang;Yaonan Wang;X. Yuan;F. Yin
人工智能（AI）的许多研究一直集中在探索智能系统的各种潜在应用。在大多数情况下，研究试图通过模仿大脑的结构和功能来模拟人类的智能，但他们忽略了人类学习和决策的一个重要方面：人工情感。本文提出了一种新的无约束全局优化方法--人工情感混合混沌优化算法（HCOAAE），该算法避免了陷入局部极小，提高了大空间高维优化问题的收敛性。人工情感的主要目的是模仿人类的决策行为过程，选择最合适的HCOAAE参数，并决定在下一次迭代中是否改变当前的搜索策略。对13个不同维数的基准函数进行了数值模拟，测试了HCOAAE的性能。实验结果表明，该方法在收敛速度、计算效率和数值稳定性方面明显优于现有方法。
Much research on Artificial Intelligence (AI) has been focusing on exploring various potential applications of intelligent systems. In most cases, the researches attempt to model human intelligence by mimicking the brain structure and function, but they ignore an important aspect in human learning and decision making: the artificial emotion. In this paper, we present a new unconstrained global optimization method, hybrid chaos optimization algorithm with artificial emotion (HCOAAE), which avoids trapping to local minima, and improves convergence in large space and high-dimension optimization problems. The main purpose of artificial emotion is to mimic decision making behavior process of humans, to choose most suitable parameters of HCOAAE and decide whether to change current search strategy or not in the next iteration. Numerical simulations of 13 benchmark functions with different dimensions are used to test the performance of HCOAAE. Experimental results show that the proposed method significantly outperforms the existing methods in terms of convergence speed, computational effectiveness, and numerical stability.