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A sociologically inspired heuristic for optimization algorithms: A case study on ant systems

机译：社会学启发式启发式优化算法：以蚂蚁系统为例

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摘要

This paper discusses how social network theory can provide optimization algorithms with social heuristics. The foundations of this approach were used in the SAnt-Q.(Social Ant-Q) algorithm, which combines theory from different fields to build social structures for state-space search, in terms of the ways that interactions between states occur and reinforcements are generated. Social measures are therefore used as a heuristic to guide exploration and approximation processes. Trial and error optimization techniques are based on reinforcements and are often used to improve behavior and coordination between individuals in a multi-agent system, although without guarantees of convergence in the short term. Experiments show that identifying different social behavior within the social structure that incorporates patterns of occurrence between states explored helps to improve ant coordination and optimization process within Ant-Q. and SAnt-Q, giving better results that are statistically significant.
机译：本文讨论了社交网络理论如何为优化算法提供社交启发。这种方法的基础被用于SAnt-Q。（Social Ant-Q）算法，该算法结合了不同领域的理论来构建用于状态空间搜索的社会结构，这取决于状态之间发生相互作用和增强的方式。产生。因此，社会措施被用作一种指导探索和近似过程的启发式方法。试错优化技术基于增强功能，通常用于改善多主体系统中个体之间的行为和协调，尽管短期内无法保证收敛。实验表明，识别社会结构中的不同社会行为并结合所探索的状态之间的发生模式，有助于改善Ant-Q中的蚂蚁协调和优化过程。和SAnt-Q，可得出具有统计意义的更好结果。

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