结合免疫与蚁群算法求解几何约束

First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008) Pub Date : 2008-01-23 DOI:10.1109/WKDD.2008.58

Hua Yuan, Yi Li, Wenhui Li, Kong Zhao, Duo Wang, Rongqing Yi

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引用次数: 5

摘要

几何约束问题可以转化为目标函数和约束为非凸函数的优化问题。本文提出了一种基于蚁群优化算法和免疫系统模型的进化算法来解决几何约束问题。在新算法中，嵌入亲和力计算过程和信息素轨迹，以保持多样性，在多个方向上进行全局搜索和局部搜索，而不是围绕同一个体同时进行一个方向的搜索。该算法与现有优化方法的不同之处在于，它通过排除坏解而得到好解。本文的实验结果将进一步揭示混合算法在求解几何约束问题时的搜索能力是如何受到影响的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Combining Immune with Ant Colony Algorithm for Geometric Constraint Solving

Geometric constraint problem can be transformed to an optimization problem which the objective function and constraints are non-convex functions. In this paper an evolutionary algorithm based on ant colony optimization algorithm and the immune system model is proposed to provide solution to the geometric constraints problem. In the new algorithm, affinity calculation process and pheromone trail lying is embedded to maintain diversity and carry out the global search and the local search in many directions rather than one direction around the same individual simultaneously. This new algorithm different with current optimization methods in that it gets the good solution by excluding bad solutions. The experimental results reported here will shed more light into how affects the hybrid algorithm's search power in solving geometric constraint problem.

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First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008)

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