There has been a surge of research interest in developing tools and analysis for Bayesian optimization, the task of finding the global maximizer of an unknown, expensive function through sequential evaluation using Bayesian decision theory. However, many interesting problems involve optimizing multiple, expensive to evaluate objectives simultaneously, and relatively little research has addresse...

Many practically relevant problems have several objectives to be maximized or minimized, taking us into the area of Multiple Objective Optimization (MOO). In multi-objective optimization several conflicting objectives have to be simultaneously optimized. Therefore, there is usually no single solution which would give the best values for all the objective functions considered by the decision mak...

Many practically relevant problems have several objectives to be maximized or minimized, taking us into the area of Multiple Objective Optimization (MOO). In multi-objective optimization several conflicting objectives have to be simultaneously optimized. Therefore, there is usually no single solution which would give the best values for all the objective functions considered by the decision mak...

response surface methodology is a common tool in optimizing processes. it mainly concerns situations when there is only one response of interest. however, many designed experiments often involve simultaneous optimization of several quality characteristics. this is called a multiresponse surface optimization problem. a common approach in dealing with these problems is to apply desirability funct...

Real world engineering design problems are usually characterized by the presence of many conflicting objectives. Therefore, it is natural to look at the engineering design problem as a multiobjective optimization problem. This report summarizes a survey of techniques to conduct multiobjective optimization in an engineering design context. The report starts with discussing some of the difficulti...

Many real-world optimization problems involve objectives, constraints, and parameters which constantly change with time. Optimization in a changing environment is a challenging task, especially when multiple objectives are required to be optimized simultaneously. Nowadays the common way to solve dynamic multiobjective optimization problems (DMOPs) is to utilize history information to guide futu...