Ant colony system for the multi objective problems using the collective knowledge center

this source code proposes a dynamic parameterization approach to the ant colony optimization algorithm configuration applied to multi-objective optimization problems. Indeed, the inertia of the static vision of the pheromone or visibility preferences values makes our dynamic approach a desired approach. We propose a model based on a collective knowledge center shared by the colony members, storing the best configurations based on the old experiments of the colony during the learning phase on random problems. The construction of this center is based on a statistical and qualitative study of the evaluation criteria that will be explained over the paper. Our model gives results that show a rise in quality of the outputs, as well as a proof of concept of the artificial learning approach.本源代码提出了一种面向多目标优化问题（Multi-objective Optimization Problems）的蚁群优化（Ant Colony Optimization, ACO）算法配置动态参数化方法。诚然，信息素（pheromone）与可见度（visibility）偏好值采用静态设定所带来的惯性局限，使得本动态方法成为更具优势的解决方案。我们构建了一种由蚁群成员共享的集体知识中心模型，该中心可存储蚁群在随机问题学习阶段中，过往实验得到的最优配置方案。该中心的构建基于本文后续将详细阐述的评估准则的统计与定性研究。实验结果表明，本模型不仅提升了输出结果的质量，同时完成了人工学习方法的概念验证。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于多目标优化问题的蚁群系统源代码，提出了一种基于集体知识中心的动态参数化方法，旨在提高算法配置的灵活性和输出质量。数据集包含Python实现的代码，需要Linux、Python 3.6+和MySQL环境运行，适用于蚁群优化算法的研究和应用。