Termite colony optimization: A novel approach for optimizing continuous problems

R. Hedayatzadeh, Foad Akhavan Salmassi, M. Keshtgari, R. Akbari, K. Ziarati
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引用次数: 77

Abstract

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.
蚁群优化:一种优化连续问题的新方法
本文提出了一种优化数值函数的新方法——蚁群优化(TCO)。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。该方法为白蚁调整运动轨迹提供了一种决策模型。白蚁在搜索空间中随机移动,但它们的轨迹偏向于信息素较多的区域。在一组已知的数值测试函数上,将TCO与现有的基于种群的算法进行了比较。实验结果表明,该方法具有较好的鲁棒性和有效性;产生良好的结果,并且优于在此考虑中调查的其他算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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