基于蚁群优化的多目标社区检测算法

蚁群优化算法作为单目标优化问题，由于只有一个目标函数，通常会将解限制到特定的范围内。当优化的目标不恰当时，算法可能失效，比如分辨率限制问题。我们将多目标优化的思想与传统的用于社区检测的蚁群优化算法相结合，增加了目标函数个数，即增加了解的评价指标数目。该算法引入多目标策略，提出多目标ACO算法，该算法在一次运行过程中会产生一组Pareto最优解。并在三个真实世界网络证明该算法的有效性和准确性。

Multi Target of Community Detection Algorithm Based on Ant Colony Optimization

As a single objective optimization problem, usually the solution of ant colony optimization algorithm is restricted to a specific range, since there is only one objective function. When the optimization target is not appropriate, the algorithm may fall into failure, such as resolution limitation. This paper combined the concept of multi-objective optimization with ant colony optimization algorithm for the traditional community detection, which increased the number of objective function. The algorithm introduced multi goal strategy, proposed multi-objective ACO algorithm, and generated a set of Pareto optimal solutions in a single run process. And the three real world networks show the effectiveness and accuracy of the algorithm.