The performance of an algorithm often largely depends on some hyper parameter which should be optimized before its usage. Since most conventional optimization methods suffer from some drawbacks, we developed an alternative way to find the best hyper parameter values. Contrary to the well known procedures, the new optimization algorithm is based on statistical methods since it uses a combination...

What do investigators like to know about the surfaces of their biomaterials? Historically, the focus has shifted from studies of proteins adsorbed on surfaces to assays of surface ligands for cell attachment. Currently, a large number of investigators are interested to find methods for persuading cells to modify their phenotype in order to achieve a useful end result. Typical areas of interest ...

Original scientific paper In this paper a systematic exploration of a typical two-stroke slow speed marine diesel propulsion shafting design space is performed. First, a set of the four most influential design parameters is defined, consisting of shafting stiffness, propeller, turning wheel and tuning wheel mass moments of inertia. Then, a full set of 625 design points is defined. For each desi...

R surface methodology (RSM) is a widely used method for simulation optimization. Its strategy is to explore small subregions of the decision space in succession instead of attempting to explore the entire decision space in a single attempt. This method is especially suitable for complex stochastic systems where little knowledge is available. Although RSM is popular in practice, its current appl...

This research aims at optimizing the production and transportation scheduling components at an automotive company. In particular, the transportation time, cost, as well as the capacity constraint constitute elements of vital significance in the decision making process. As found in a research conducted in Iran’s management and planning organization, an estimated 30% of all price increases was as...

Striking the correct balance between global exploration of search spaces and local exploitation of promising basins of attraction is one of the principal concerns in the design of global optimization algorithms. This is true in the case of techniques based on global response surface approximation models as well. After constructing such a model using some initial database of designs it is far fr...

In modern quality engineering, dual response surface methodology is a powerful tool to monitor an industrial process by using both the mean and the standard deviation of the measurements as the responses. The least squares method in regression is often used to estimate the coefficients in the mean and standard deviation models, and various decision criteria are proposed by researchers to find t...

There have been many productive methods developed so far for optimization of multiple response surface (MRS) problems. This paper tends to review the most seminal approaches in MRS and discuss the strength and weakness of each of the approaches through existing aspects in MRS. A numerical example is included to compare results by different methods. Finally some of the prominent areas for future...