despite the growing use of evolutionary multi-objective optimization algorithms in different categories of science, these algorithms as a powerful tool in portfolio optimization and specially solving multi-objective portfolio optimization problem is still in its early stages. in this paper, moeas have been used for solving multi-objective portfolio optimization problem in tehran stock market. f...

Inventory management requires thousands or millions of individual transactions each year. Classification the items influences results inventory management. Traditionally, this is usually classified with considering an annual dollar usage criterion but maybe other criteria such as lead time, criticality, perishability, cost, and demand type can be affected on that classification. have more than ...

In this contribution, a novel methodology for multi-class classification in the field of Parkinson’s disease is proposed. The structured two phases. first phase, most relevant volumes interest (VOI) brain are selected by means an evolutionary multi-objective optimization (MOE) algorithm. Each these VOIs subjected to volumetric feature extraction using Three-Dimensional Discrete Wavelet Transfor...

a wind turbine transformer (wtt) is designed using a 3d wound core while the transformer’s total owning cost (toc) and its inrush current performance realized as the two objective functions in a multi-objective optimization process. multi-objective genetic algorithm is utilized to derive pareto optimal solutions. the effects of inrush current improvement on other operating and design parameters...

In this paper, we describe a multi-objective evolutionary algorithm, that uses clustering selection and does not need any additional parameter like others. It clusters the population into a exible number of clusters employing x-means from [Pelleg and Moore, 2000]. First, the selective tness is assigned to clusters and in second place to individuals of clusters. We show three hybrid variants inc...

Evolutionary Algorithms are recognized to be efficient to deal withMulti-objective Optimization Problems(MOPs) which are difficult to be solved with traditional methods. Here a newMulti-objective Optimization Evolutionary Algorithm named DGPS which is compound with Geometrical Pareto Selection Method (GPS), Weighted SumMethod (WSM) and Dynamical Evolutionary Algorithm (DEA) is proposed. Some fa...

A new software tool making use of a genetic algorithm for multi-objective experimental optimization (GAME.opt) was developed based on a strength Pareto evolutionary algorithm. The software deals with high dimensional variable spaces and unknown interactions of design variables. This approach was evaluated by means of multi-objective test problems replacing the experimental results. A default pa...

Multiobjective optimization is progressively more applied as it allows being closer to real engineering problems that may be found in industrial applications. This paper proposes applied techniques to optimize the design of a pressure vessel for hollow cylinder and hemi-spherical head in terms of the cost of the material and manufacturing. The problem is studied with two objectives: minimise co...

A hybrid unsupervised learning algorithm, which is termed as Parallel Rough-based Archived Multi-Objective Simulated Annealing (PARAMOSA), is proposed in this article. It comprises a judicious integration of the principles of the rough sets theory and the scalable distributed paradigm with the archived multi-objective simulated annealing approach. While the concept of boundary approximations of...