An optimization technique using the characteristics of genetic algorithm

Abstract

Optimization problems could happen often in discrete or discontinuous search space. Therefore, the traditional gradient-based methods are not able to apply to this kind of problems. The discrete design variables are considered reasonably and the heuristic techniques are generally adopted to solve this problem, and the genetic algorithm based on stochastic search technique is one of these. The genetic algorithm method with discrete variables can be applied to structural optimization problems, such as composite laminated structures or trusses. However, the discrete optimization adopted in genetic algorithm gives rise to a troublesome task that is a mapping between each strings and discrete variables. And also, its solution quality could be restricted in some cases. In this study, a technique using the genetic algorithm characteristics is developed to utilize continuous design variables instead of discrete design variables in discontinuous solution spaces. Additionally, the proposed algorithm, which is manipulating a fitness function artificially, is applied to example problems and its results are compared with the general discrete genetic algorithm. The example problems are to optimize support positions of an unstable structure with discontinuous solution spaces.

Optimum selection of novel developed implant material using hybrid entropy‐PROMETHEE approach

“…As a result, it is necessary to decide the best material that has the highest scale of satisfaction for all the related properties. Using simple and logical methods, the criterion that influences the performance a given engineering application must be identified to eradicate inappropriate alternatives and to choose the most relevant one [19][20][21]. In this regard, various multi-criteria decision-making (MCDM) methods have been projected to assist in selecting the best compromise alternative involving finite number of criterion [22][23][24][25][26][27][28][29].…”