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Applicability of surrogates to improve efficiency of particle swarm optimization for simulation-based problems

机译：代理人在提高基于模拟问题的粒子群优化效率方面的适用性

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摘要

Particle swarm optimization (PSO) is a population-based, heuristic technique based on social behaviour that performs well on a variety of problems including those with non-convex, non-smooth objective functions with multiple minima. However, the method can be computationally expensive in that a large number of function calls is required. This is a drawback when evaluations depend on an off-the-shelf simulation program, which is often the case in engineering applications. An algorithm is proposed which incorporates surrogates as a stand-in for the expensive objective function, within the PSO framework. Numerical results are presented on standard benchmarking problems and a simulation-based hydrology application to show that this hybrid can improve efficiency. A comparison is made between the application of a global PSO and a standard PSO to the same formulations with surrogates. Finally, data profiles, probability of success, and a measure of the signal-to-noise ratio of the the objective function are used to assess the use of a surrogate.
机译：粒子群优化（PSO）是一种基于人群的启发式技术，它基于社会行为，可以很好地解决各种问题，包括那些具有非凸，非光滑目标函数且具有多个极小值的问题。然而，该方法在计算上是昂贵的，因为需要大量的函数调用。当评估依赖于现成的仿真程序时，这是一个缺点，这在工程应用中通常是这种情况。提出了一种算法，该算法在PSO框架内将替代方案作为昂贵目标函数的替代方案。给出了关于标准基准问题和基于仿真的水文学应用的数值结果，表明该混合动力系统可以提高效率。将全局PSO和标准PSO应用于具有替代物的相同配方之间进行比较。最后，使用数据配置文件，成功概率以及目标函数信噪比的度量来评估代理的使用。

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    《Engineering Optimization》 |2012年第5期|p.521-535|共15页
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    Aerospace Computational Design Laboratory, Computation for Design Optimization, MIT, Cambridge, MA, 02139, USA;

    Simulation, Systems Optimization and Robotics Group, Department of Computer Science, Technische Universität Darmstadt, D-64289, Darmstadt, Ge;

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