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Solving Continuous Optimization Using Ant Colony Algorithm

机译：使用蚁群算法解决连续优化

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摘要

One shortcoming of ant colony optimization is that it can not be applied on continuous optimization problems directly. In this paper we propose a new approach for solving continuous optimization problems using ant colony algorithm. While the method maintains the framework of the classical ant colony algorithm, it replaces the discrete frequency in the ant selecting probability by a continuous probability distribution formula using the continuous integral instead of discrete summation. We also use the direction towards the optimum in each dimension as the heuristic information guiding the ants' searching. Experimental results on benchmarks show that our algorithm not only has faster convergence speed than other similar methods, but also effectively improves the accuracy of solution and enhances its robustness.
机译：蚁群优化的一个缺点是它不能直接应用于连续优化问题。本文提出了一种利用蚁群算法解决连续优化问题的新方法。虽然该方法保持了经典蚁群算法的框架，但它可以通过连续积分而不是离散求和来替换蚂蚁选择概率中的离散频率。我们还使用指导蚂蚁搜索的启发式信息中的每个维度的最佳方向。基准测试结果表明，我们的算法不仅具有比其他类似方法更快的收敛速度，而且还有效提高了解决方案的准确性并提高了其鲁棒性。

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