Development of hybrid algorithm based on simulated annealing and genetic algorithm to reliability redundancy optimization

Abstract

Purpose

The purpose of this paper is to present an application of the simulated annealing algorithm to the redundant system reliability optimization. Its main aim is to analyze and compare this optimization method performance with those of similar application.

Design/methodology/approach

The methods that were used to compare results are the genetic algorithm, the Lagrange Multipliers, and the evolution strategy. A hybrid algorithm composed by simulated annealing and genetic algorithm was developed in order to achieve the general applicability of the methods. The hybrid algorithm also tries to exploit the positive aspects of each method.

Findings

The results presented by the simulated annealing and the hybrid algorithm are significant, and validate the methods as a robust tool for parameter optimization in mechanical projects development.

Originality/value

The main objective is to propose a method for redundancy optimization in mechanical systems, which are not as large as electric and electronic systems, but involves high costs associated to redundancy and requires a high level of safety standards like: automotive and aerospace systems.

Citation

Dalanezi Mori, B., Fiori de Castro, H. and Lucchesi Cavalca, K. (2007), "Development of hybrid algorithm based on simulated annealing and genetic algorithm to reliability redundancy optimization", International Journal of Quality & Reliability Management, Vol. 24 No. 9, pp. 972-987. https://doi.org/10.1108/02656710710826225