Multi-objective fatigue life optimization using Tabu Genetic Algorithms

Abstract

Purpose – The purpose of this paper is to present a real world application of an innovative hybrid system reliability optimization algorithm combining Tabu search with an evolutionary algorithm (TSEA). This algorithm combines Tabu search and Genetic algorithm to provide a more efficient search method. Design/methodology/approach – The new algorithm is applied to an aircraft structure to optimize its reliability and maintain its structural integrity. For retrofitting the horizontal stabilizer under severe stall buffet conditions, a decision support system (DSS) is developed using the TSEA algorithm. This system solves a reliability optimization problem under cost and configuration constraints. The DSS contains three components: a graphical user interface, a database and several modules to provide the optimized retrofitting solutions. Findings – The authors found that the proposed algorithm performs much better than state-of-the-art methods such as Strength Pareto Evolutionary Algorithms on bench mark problems. In addition, the proposed TSEA method can be easily applied to complex real world optimization problem with superior performance. When the full combination of all input variables increases exponentially, the DSS become very efficient. Practical implications – This paper presents an application of the TSEA algorithm for solving nonlinear multi-objective reliability optimization problems embedded in a DSS. The solutions include where to install doublers and stiffeners. Compromise programming is used to rank all non-dominant solutions. Originality/value – The proposed hybrid algorithm (TSEA) assigns fitness based upon global dominance which ensures its convergence to the non-dominant front. The high efficiency of this algorithm came from using Tabu list to guidance the search to the Pareto-optimal solutions.