Robust Design Optimization is the most appropriate approach to face problems characterized by uncertainties in the operating conditions that represent a crucial point of aeronautical research activities. The Robust Design methodology illustrated in this paper is based on the multi-objective approach: applying the statistical definition of stability, the method finds, at the same time, optimized...

When dealing with many-criteria decision making and many-objectives optimization problems the concepts of Pareto optimality and Pareto-dominance are inefficient in modelling and simulating human decision making. Different fuzzy-based definitions of optimality and dominated solution are introduced and tested on analytical test cases in order to show their validity and closeness to human decision...

Managing the biodiversity extinction crisis requires wise decision-making processes able to account for the limited resources available. In most decision problems in conservation biology, several conflicting objectives have to be taken into account. Most methods used in conservation either provide suboptimal solutions or use strong assumptions about the decision-maker's preferences. Our paper r...

Abstract A simple multi-physical system for the potential flow of a fluid through shroud, in which mechanical component, like turbine vane, is placed, modeled mathematically. We then consider multi-criteria shape optimization problem, where component allowed to vary under certain set second-order Hölder continuous differentiable transformations baseline with boundary same continuity class. As o...

In this paper, a new method based on genetic-fuzzy algorithm for multi-objective optimization is proposed. This method is successfully applied to several multi-objective optimization problems. Two examples are presented: the first example is the optimization of two nonlinear mathematical functions and the second one is the design of PI controller for control of an induction motor drive supplie...

This talk will cover the main components of sequential modelbased optimization algorithms. Algorithms of this kind represent the state-of-the-art for expensive black-box optimization problems and are getting increasingly popular for hyper-parameter optimization of machine learning algorithms, especially on larger data sets. The talk will cover the main components of sequential model-based optim...

In this paper, a parallel evolutionary multi-criteria optimization algorithm: DGA and DRMOGA are applied to block layout problems. The results are compared to the results of SGA and discussed. Because block layout problems are NP hard and can have several types of objectives, these problems are suitable to evolutionary multicriterion optimization algorithms. DRMOGA is a DGA model that can deriv...

In the present paper we demonstrate application of single-objective and multi-objective optimization methods for solving problem ship design characteristics optimization. The impact analysis local criteria on values elements a has been carried out. most frequently used multicriteria their to substantiation, with taking into account requirements seakeeping economic efficiency are considered. res...

In this paper, a new method based on genetic-fuzzy algorithm for multi-objective optimization is proposed. This method is successfully applied to several multi-objective optimization problems. Two examples are presented: the first example is the optimization of two nonlinear mathematical functions and the second one is the design of PI controller for control of an induction motor drive supplie...

Multi-criteria optimization has gained increasing attention during the last decades. This article exemplifies multi-criteria features, which are implemented in the statistical software package SPOT. It describes related software packages such as moo and emoa and gives a comprehensive introduction to simple multi criteria optimization tasks. Several hands-on examples are used for illustration. T...