Hamid Saeedipour - Corresponding Author, PhD, MSc, MBS, BSc, IT، School of Aerospace Engineering, University of Science Malaysia (USM), Penang, Malaysia
Sathyanarayana - Lecturer, PhD, MSc, BSc, School of Aerospace Engineering, University of Science Malaysia (USM), Penang, Malaysia

Genetic Algorithm (GA) in aeronautics may be considered as an adaptive search method premised on the evolutionary ideas of natural selection and genetic. In this paper, the GA concept in aircraft weight optimization is designed to simulate process in an integrated aircraft system ecessary for minimum gross mass, specifically the one that follow the GA principles of survival of the fittest. This paper describes the results of a research to broaden the application of an available genetic algorithm for design optimization named GADO to weight optimization of a high-subsonic civil jet transport aircraft. It was initially developed for minimizing take-off mass of a supersonic transport aircraft. This process represents an intelligent exploitation of a random search within a defined search space to solve the problem of minimizing the aircraft gross weight at take-off (GWTO). The GA method has been performed well as the population converged to an optimal solution to the GWTO dilemma. All of the genes have converged when 97% of the population sharing the same value. Ten random populations of 120 points each were generated, and for each population the GA is allowed to proceed for 12000 iterations.

Genetic Algorithm, Design Optimisation, Aircraft Design, Minimum Weight