Evolutionary hybrid computation in view of design information by data mining

Abstract

Design Informatics has three points of view. First point is the effident exploration in design space using evolu tionary computation. Second point is the structurization and visualization of design space using data mining. Third point is the application to practical problems. In the present study, the influence of the seven pure and hybrid optimizers for design information has been investigated in order to explain the selection manner of optimizer for data mining. A single-stage hybrid rocket design problem is picked up as the present design object. As a result, mining result depends on not the number of generation (convergence) but the optimizers (diversity). Consequently, the optimizer with diversity performance should be selected in order to obtain global design information in the design space. Therefore, the diversity performance has also been explained for the seven optimization methods by using three standard mathematical test problems with/without noise. The resuIt indicates that the hybrid method between the differential evolution and the genetic algorithm is benefidal performance for effident exploration in the design space under the condition for large-scale design problems within 102 order evolution at most. Moreover, the comparison among eight crossovers indicates that the prindpal component analysis blended crossover is good selection on the hybrid method between the differential evolution and the genetic algorithm.Index Terms-design informatics, evolutionary hybrid opti mization method, data mining, large-scale practical problem.

Structurisation and visualisation of design space for launch vehicle with hybrid rocket engine

“…In the present study, the evolutionary hybrid optimisation methodology between DE and GA is employed. It was confirmed that this methodology had the high performance regarding the convergence and diversity, as well as the strength for noise (Chiba, 2013). Note that noise imitates the error on computational analyses and experiments and is described as the perturbation on objective functions.…”

“…In addition, since the designers require to present many exact optimum solutions for the decision of a compromise one, an evolutionary-based Pareto approach as an efficient multi-thread algorithm, which the plural individuals are parallel conducted, is employed instead of gradient-based methods. The optimiser used in the present study is the hybrid evolutionary method between the differential evolution (DE) and the genetic algorithm (GA) (Chiba, 2013). Moreover, global design information is primarily essential in order to determine a compromise solution.…”

“…The crossover rate is 100%. The principal component analysis blended crossover -(PCABLX) (Takahashi and Kita, 2001) and the confidence interval-based crossover (CIX) using L 2 norm (Hervas-Martinez et al, 2002) are used because of the high performance for the convergence and the diversity as well as the strength for noise (Chiba, 2013). The subpopulation size served by GA is equally divided for these two crossovers.…”

Conceptual Design of Single-Stage Launch Vehicle with Hybrid Rocket Engine for Scientific Observation Using Design Informatics

“…In the present study, the evolutionary hybrid optimization methodology between DE and GA is employed. It was confirmed that this methodology had the high performance regarding the convergence and diversity, as well as the strength for noise [11]. Note that noise imitates the error on computational analyses and experiments and is described as the perturbation on objective functions.…”

“…In addition, since the designers require to present many exact optimum solutions for the decision of a compromise one, an evolutionary-based Pareto approach as an efficient multi-thread algorithm, which the plural individuals are parallel conducted, is employed instead of gradient-based methods. The optimizer used in the present study is the evolutionary hybrid method between the differential evolution (DE) and the genetic algorithm (GA) [11]. Moreover, global design information is primarily essential in order to determine a compromise solution.…”