Integrating multi-objective genetic algorithm based clustering and data partitioning for skyline computation

Abstract

Skyline computation in databases has been a hot topic in the literature because of its interesting applications. The basic idea is to find non-dominated values within a database. The task is mainly a multi-objective optimization process as described in this paper. This motivated for our approach that employs a multi-objective genetic algorithm based clustering approach to find the pareto-optimal front which allows us to locate skylines within a given data. To tackle large data, we simply split the data into manageable subsets and concentrate our analysis on the subsets instead of the whole data at once. The proposed approach produced interesting results as demonstrated by the outcome from the conducted experiments.

Thermodynamic and thermo-economic analysis and optimization of an irreversible regenerative closed Brayton cycle

“…Multi-objective optimization is useful way for solving engineering problems at various fields for research [32][33][34].…”

Optimization performance and thermodynamic analysis of an irreversible nano scale Brayton cycle operating with Maxwell–Boltzmann gas

“…Multi objective optimization is a great asset for solving engineering problems [36][37][38]. Özyer and colleagues [36] evolved an intellectual model for prospect calculation by using of evolutionary algorithms.…”

“…Özyer and colleagues [36] evolved an intellectual model for prospect calculation by using of evolutionary algorithms. The multi-objective optimization is performed by various engineering issues such as vehicle routing problems with Time Windows [37].…”

Multi-objective optimization of GPU3 Stirling engine using third order analysis

“…The multi-objective optimization has been applied by different engineering problems such as skyline computation and vehicle routing issues [10][11][12]. Evolutionary algorithms (EA) were applied in an attempt to stochastically solve problems of this generic class during the 18th century [13].…”