摘要翻译：
本文提出了一种指数信息素沉积方法，以改善经典蚂蚁系统算法的性能，该算法采用均匀沉积规则。用微分方程进行了简化分析，研究了具有指数和常数沉积规则的基本蚂蚁系统动力学的稳定性。以连通城市路线图为平台，将最大最小蚂蚁系统模型（蚁群算法的一种改进和流行模型）与指数沉积规则和常数沉积规则进行了比较。对非均匀沉积方法的最佳参数设置进行了大量的仿真，实验结果表明，该方法在求解质量和收敛时间方面都大大优于传统方法。
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英文标题：
《Extension of Max-Min Ant System with Exponential Pheromone Deposition
  Rule》
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作者：
Ayan Acharya, Deepyaman Maiti, Aritra Banerjee, R. Janarthanan, Amit
  Konar
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最新提交年份：
2008
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  The paper presents an exponential pheromone deposition approach to improve the performance of classical Ant System algorithm which employs uniform deposition rule. A simplified analysis using differential equations is carried out to study the stability of basic ant system dynamics with both exponential and constant deposition rules. A roadmap of connected cities, where the shortest path between two specified cities are to be found out, is taken as a platform to compare Max-Min Ant System model (an improved and popular model of Ant System algorithm) with exponential and constant deposition rules. Extensive simulations are performed to find the best parameter settings for non-uniform deposition approach and experiments with these parameter settings revealed that the above approach outstripped the traditional one by a large extent in terms of both solution quality and convergence time.
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PDF链接：
https://arxiv.org/pdf/0811.0136