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

作者：Parno M D; Hemker T; Fowler K R^*

来源：Engineering Optimization, 2012, 44(5): 521-535.

DOI：10.1080/0305215X.2011.598521

• surrogates
• black-box optimization
• particle swarm

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.

• 出版日期2012