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Termite colony optimization: A novel approach for optimizing continuous problems

机译:白蚁菌群优化:一种优化连续问题的新方法

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摘要

In this paper, a novel approach, called Termite colony optimization (or TCO), for optimizing numerical functions is presented. TCO is a population based optimization technique which is inspired from intelligent behaviors of termites. The proposed approach provides a decision making model which is used by termites to adjust their movement trajectories. Termites move randomly in the search space, but their trajectories are biased towards regions with more pheromones. TCO is compared with existing population-based algorithms on a set of well known numerical test functions. The experimental results show that the TCO is effective and robust; produce good results, and outperform other algorithms investigated in this consideration.
机译:在本文中,提出了一种新颖的方法,称为白蚁菌落优化(或TCO),用于优化数值函数。 TCO是一种基于种群的优化技术,其灵感来自于白蚁的智能行为。所提出的方法提供了一种决策模型,白蚁使用该模型来调整它们的运动轨迹。白蚁在搜索空间中随机移动,但是它们的轨迹偏向具有更多信息素的区域。在一组众所周知的数值测试函数上,将TCO与现有的基于人口的算法进行比较。实验结果表明,总体拥有成本是有效且稳定的。产生良好的结果,并且优于在此考虑下研究的其他算法。

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