Big data refers to the enormous heterogeneous being produced at a brisk pace by large number of diverse generating sources. Since traditional processing technologies are unable process big efficiently, is processed using newer distributed storage and frameworks. view materialization technique queries efficiently on these It generates valuable information, which can be used take timely decisions...

Many-objective evolutionary optimisation is a recent research area that is concerned with the optimisation of problems consisting of a large number of performance criteria using evolutionary algorithms. Despite the tremendous development that multi-objective evolutionary algorithms (MOEAs) have undergone over the last decade, studies addressing problems consisting of a large number of objective...

As the design of electrical/electronic (E/E)-architectures is becoming more complex, multi-objective optimization algorithms such as evolutionary algorithms (EAs) have been proposed for generating resource optimized architectures. In this paper we extend existing approaches by excluding infeasible solutions from the search space and thereby enhance the quality and runtime behavior of the optimi...

The use of computational methodologies for the optimization of aesthetic parameters is not frequent mainly due to the fact that these parameters are not quantifiable and are subjective. In this work an interactive methodology based on the use of multi-objective optimization algorithms is proposed. This strategy associates the results of different optimization runs considering the existent quant...

This paper presents a technique that incorporates preference information within the framework of multi-objective evolutionary algorithms for the solution of many-objective optimization problems. The proposed approach employs a single reference point to express the preferences of a decision maker, and adaptively biases the search procedure toward the region of the Pareto-optimal front that best ...

Classical evolutionary multi-objective optimization algorithms aim at finding an approximation of the entire set of Pareto optimal solutions. By considering the preferences of a decision maker within evolutionary multi-objective optimization algorithms, it is possible to focus the search only on those parts of the Pareto front that satisfy his/her preferences. In this paper, an extended prefere...

Many real-world optimization problems have several, usually conflicting objectives. Evolutionary multi-objective optimization usually solves this predicament by searching for the whole Pareto-optimal front of solutions, and relies on a decision maker to finally select a single solution. However, in particular if the number of objectives is large, the number of Pareto-optimal solutions may be hu...