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首页> 外文会议>International Conference on Natural Computation;ICNC '09 >Comparative Study on Bionic Optimization Algorithms for Sewer Optimal Design
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Comparative Study on Bionic Optimization Algorithms for Sewer Optimal Design

机译：下水道优化设计中仿生优化算法的比较研究

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Sewer network as a necessary urban infrastructure plays an important role in peopleȁ9;s daily life. Conventional optimization techniques have significant limitations on solving the problems of sewer optimal design. Because as a high-dimensional discrete complex optimization problem, sewer optimal design is characterized by its discrete objective function and, as an integer discrete variable, its decision variable amount keeps the same pace with engineering scales. Over the last decade, various kinds of modern bionic optimization algorithms with their special advantages have been created and applied into sewer optimal design successfully. Based on previous studies, this paper analyses and compares the solution performances of Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Ant Colony Algorithms (ACA) from the three aspects respectively, they are convergence, speed and complexity of algorithm. The research result shows that compared with the other two algorithms, the ACA manifests its superiority for better convergence, satisfactory speed and relatively small algorithm complexity, which are very suitable for solving the problems of sewer optimal design.
机译：下水道网络作为必不可少的城市基础设施，对人们的日常生活起着重要的作用。传统的优化技术在解决下水道优化设计问题上有很大的局限性。由于作为高维离散复杂优化问题，下水道优化设计的特征在于其离散目标函数，并且作为整数离散变量，其决策变量的数量与工程规模保持一致。在过去的十年中，已经创建了各种具有特殊优势的现代仿生优化算法，并将其成功应用于下水道优化设计中。本文在前人研究的基础上，分别从遗传算法的收敛性，速度和复杂度三个方面对遗传算法（GA），粒子群优化算法（PSO）和蚁群算法（ACA）的求解性能进行了比较和比较。研究结果表明，与其他两种算法相比，ACA具有收敛性更好，速度令人满意，算法复杂度相对较小的优越性，非常适合解决下水道优化设计问题。

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