This paper presents a multi-objective wavelet identification procedure for fault detection in dynamic systems. For this purpose, a multi-objective genetic algorithm is used to search for the Pareto frontier. Two objectives are taken into account, the minimization of the residual signal in nominal operating conditions and its maximization in faulty operating conditions. Thus, the proposed approa...

The increased ability to access repositories of representations of complex objects, such as biological molecules or financial time series, has not been matched by the availability of tools that permit locating them, visualizing their characteristics, and describe them in terms that are close to the language of the intended users of those data collections. The representation methods and organiza...

Hard multi-criteria (MC) problems are computationally intractable problems requiring optimization of more than one criterion. However, the optimization of two or more criteria tends to yield not just one optimal solution, but rather a set of non-dominated solutions. As a result, the evolution of a Pareto-Optimai set of non-dominated solutions from some population of candidate solutions is often...

majority of models in location literature are based on assumptions such as point demand, absence of competitors, as well as monopoly in location, products, and services. however in real-world applications, these assumptions are not well-matched with reality. in this study, a new mixed integer nonlinear programming model based on weighted goal programming approach is proposed to maximize the cap...

Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk objectives. In this paper, we studied the extended Markowitz’s meanvariance portfolio optimization model. We considered the cardinality, quantity, pre-assignment and round lot constraints in the extended model. These four rea...

Mathematical programming methods dominate in the portfolio optimization problems, but they cannot be used if we introduce a constraint limiting the number of different assets included in the portfolio. To solve this model some of the heuristics methods (such as genetic algorithm, neural networks and particle swarm optimization algorithm) must be used. In this paper we utilize binary particle sw...

Evolutionary optimization algorithms have been used to solve multiple objective problems. However, most of these methods have focused on search a sufficient Pareto front, and no efforts are made to explore the diverse Pareto optimal solutions corresponding to a Pareto front. Note that in semi-obnoxious facility location problems, diversifying Pareto optimal solutions is important. The paper the...

This article describes a simulation model in which a multi-objective approach is utilized for evolving an artificial neural networks (ANNs) controller for an autonomous mobile robot. A mobile robot is simulated in a 3D, physics-based environment for the RF-localization behavior. The elitist Pareto-frontier Differential Evolution (PDE) algorithm is used to generate the Pareto optimal set of ANNs...