Comparative study of genetic algorithm and simulated annealing for optimal tolerance design formulated with discrete and continuous variables

Abstract

Optimal tolerance design has been the focus of extensive research for a few decades. This has resulted in several formulations and solution algorithms for systematic tolerance design considering various aspects. Availability of different alternative manufacturing processes or machines for realization of a dimension is frequently encountered. In such cases optimal tolerance design must also consider optimal selection of a set of manufacturing processes or machines as appropriate. Such a non-linear multivariate optimal tolerance design problem results in a combinatorial and multi-modal solution space. Optimal solution of this advanced tolerance design problem is difficult using traditional optimization techniques. The problem formulation becomes more complex with simultaneous selection of design and manufacturing tolerances. The focus of the current research is on the optimal solution of this advanced and complex tolerance design problem. Genetic algorithm and simulated annealing as non-traditional global optimization techniques have been used to obtain the solution. Application of the solution techniques has been demonstrated with the help of appropriate examples. Comparison of the results establishes that the genetic algorithm is the superior of the two approaches.

Effectiveness prediction of abrasive jetting stream of accelerator tank using normalized sparse autoencoder-adaptive neural fuzzy inference system

“…Furthermore, an extensive analysis is performed here to quantify the applicability and efficiency of this new approach with other representative ones, including genetic optimization, 37,38 simulated annealing–genetic Algorithm (SA–GA), 39 Taguchi estimation, 40,41 ANN–SA prediction, 42 and genetically optimized neural network (GONN), 43 together with their detailed calculations being referenced from the above-mentioned literature; a statistical evaluation concerning with their respective predictive performances could be implemented in identical test conditions. Figure 20 (a)–(e) show that NSAE-ANFIS outperforms other approaches in precision and reliability, since it presents effectiveness indexes in accordance with the actual measured ones with lower estimation errors, demonstrating that this proposed approach ensures an optimal prediction for abrasive jetting stream effectiveness in experiments.…”

Optimisation process for robotic assembly of electronic components

“…The genetic algorithm technique dates back to 1975 and was first introduced by Holland [12]. In the literature, the genetic algorithm has been widely used to solve optimisation problems in many industrial applications such as job shop sequencing and scheduling [13], assembly planning [14], selection of machining parameters in turning operation [15], design of sheet-metal assembly and machining fixtures [16] and tolerance design [17]. The processes of optimisation for the feeder-slot allocation and component sequencing are the two most important factors for improving the efficiency of this assembly operation.…”

Discrete tolerance allocation for product families

“…A selection of other solutions involving genetic algorithms for solving the two above-discussed problems includes those of Iannuzzi and Sandgren (1994), Prabhaharan et al (2004) and Singh et al (2005).…”