SOMS: SurrOgate MultiStart algorithm for use with nonlinear programming for global optimization

SOMS is a general surrogate-based multistart algorithm, which is used in combination with any local optimizer to find global optima for computationally expensive functions with multiple local minima. SOMS differs from previous multistart methods in that a surrogate approximation is used by the multistart algorithm to help reduce the number of function evaluations necessary to identify the most promising points from which to start each nonlinear programming local search. SOMS’s numerical results are compared with four well-known methods, namely, Multi-Level Single Linkage (MLSL), MATLAB’s MultiStart, MATLAB’s GlobalSearch, and GLOBAL. In addition, we propose a class of wavy test functions that mimic the wavy nature of objective functions arising in many black-box simulations. Extensive comparisons of algorithms on the wavy testfunctions and on earlier standard global-optimization test functions are done for a total of 19 different test problems. The numerical results indicate that SOMS performs favorably in comparison to alternative methods and does especially well on wavy functions when the number of function evaluations allowed is limited.

Does the use of mobile devices (tablets and smartphones) affect survey quality and choice behaviour in web surveys?

Web surveys are becoming increasingly popular in survey research including stated preference surveys. Compared with face-to-face, telephone and mail surveys, web surveys may contain a different and new source of measurement error and bias: the type of device that respondents use to answer the survey questions. This is the first study that tests whether the use of mobile devices, tablets or smartphones, affects survey characteristics and stated preferences in a web-based choice experiment. The web survey on expanding renewable energy production in Germany was carried out with 3182 respondents, of which 12% used a mobile device. Propensity score matching is used to account for selection bias in the use of mobile devices for survey completion. We find that mobile device users spent more time than desktop/laptop users to answer the survey. Yet, desktop/laptop users and mobile device users do not differ in acquiescence tendency as an indicator of extreme response patterns. For mobile device users only, we find a negative correlation between screen size and interview length and a positive correlation between screen size and acquiescence tendency. In the choice experiment data, we do not find significant differences in the tendency to choose the status quo option and scale between both subsamples. However, some of the estimates of implicit prices differ, albeit not in a unidirectional fashion. Model results for mobile device users indicate a U-shaped relationship between error variance and screen size. Together, the results suggest that using mobile devices is not detrimental to survey quality.