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Comparative Study of Ant Colony Algorithms for Multi-Objective Optimization

机译:蚁群算法多目标优化的比较研究

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摘要

In recent years, when solving MOPs, especially discrete path optimization problems, MOACOs concerning other meta-heuristic algorithms have been used and improved often, and they have become a hot research topic. This article will start from the basic process of ant colony algorithms for solving MOPs to illustrate the differences between each step. Secondly, we provide a relatively complete classification of algorithms from different aspects, in order to more clearly reflect the characteristics of different algorithms. After that, considering the classification result, we have carried out a comparison of some typical algorithms which are from different categories on different sizes TSP (traveling salesman problem) instances and analyzed the results from the perspective of solution quality and convergence rate. Finally, we give some guidance about the selection of these MOACOs to solve problem and some research works for the future.
机译:近年来,在解决MOP,特别是离散路径优化问题时,与其他元启发式算法有关的MOACO经常被使用和改进,已成为研究的热点。本文将从解决MOP的蚁群算法的基本过程开始,以说明每个步骤之间的差异。其次,我们从不同方面提供了相对完整的算法分类,以便更清晰地反映不同算法的特征。之后,考虑分类结果,我们对不同规模的TSP(旅行商问题)实例上不同类别的一些典型算法进行了比较,并从解决方案质量和收敛速度的角度分析了结果。最后,我们为选择这些MOACO解决问题提供了一些指导,并为将来的研究工作提供了指导。

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