Mechanical engineering design optimisation using modified harmony elements algorithm

In this paper a new optimisation algorithm, harmony elements algorithm (HEA), for solving mechanical engineering design optimisation problems is presented. This algorithm is inspired by an ancient Chinese philosophy, called as theory of five elements. The basic harmony element algorithm proposed by Cui and Guo (2008) is modified in this paper to reduce the computational effort by dividing the population into equal parts and by incorporating the mutation operator. The efficiency and ease of application of the proposed optimisation algorithm is demonstrated by solving five different mechanical components design problems such as pressure vessel, tension/compression spring, Belleville spring, welded beam and gear train. The results of the proposed method are compared with the results given by other optimisation techniques such as genetic algorithm (GA), particle swarm optimisation (PSO), ant colony algorithm (ACA), Lagrangian multiplier approach and branch and bound approach. In all the cases, the solutions obtained using the proposed modified HEA are superior to those obtained by other optimisation techniques.

[1]  K. M. Ragsdell,et al.  Optimal Design of a Class of Welded Structures Using Geometric Programming , 1976 .

[2]  Carlos A. Coello Coello,et al.  Constraint-handling in genetic algorithms through the use of dominance-based tournament selection , 2002, Adv. Eng. Informatics.

[3]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[4]  Kalyanmoy Deb,et al.  GeneAS: A Robust Optimal Design Technique for Mechanical Component Design , 1997 .

[5]  Kalyanmoy Deb,et al.  Optimal design of a welded beam via genetic algorithms , 1991 .

[6]  C. Guo,et al.  Swarm intelligence for mixed-variable design optimization , 2004, Journal of Zhejiang University. Science.

[7]  S. Wu,et al.  GENETIC ALGORITHMS FOR NONLINEAR MIXED DISCRETE-INTEGER OPTIMIZATION PROBLEMS VIA META-GENETIC PARAMETER OPTIMIZATION , 1995 .

[8]  Kalyanmoy Deb,et al.  A combined genetic adaptive search (GeneAS) for engineering design , 1996 .

[9]  Carlos A. Coello Coello,et al.  Use of a self-adaptive penalty approach for engineering optimization problems , 2000 .

[10]  Ashok Dhondu Belegundu,et al.  A Study of Mathematical Programming Methods for Structural Optimization , 1985 .

[11]  Jasbir S. Arora,et al.  Introduction to Optimum Design , 1988 .

[12]  Ali Rıza Yıldız,et al.  A novel particle swarm optimization approach for product design and manufacturing , 2008 .

[13]  Shen Zy,et al.  Basic theory of traditional Chinese medicine , 1997 .

[14]  S. N. Kramer,et al.  An Augmented Lagrange Multiplier Based Method for Mixed Integer Discrete Continuous Optimization and Its Applications to Mechanical Design , 1994 .

[15]  K. Lee,et al.  A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice , 2005 .

[16]  Tapabrata Ray,et al.  Society and civilization: An optimization algorithm based on the simulation of social behavior , 2003, IEEE Trans. Evol. Comput..

[17]  Ling Wang,et al.  An effective co-evolutionary particle swarm optimization for constrained engineering design problems , 2007, Eng. Appl. Artif. Intell..