Abstract

Traditional mathematical algorithms are incapable of solving real time engineering design problems because of its rigid procedure mainly due to discrete or random data and multi-objective functions in a problem. An optimization algorithm is a procedure which is executed iteratively by comparing various solutions till the optimum or a satisfactory solution is found. There are two population based Swarm inspired methods in computational intelligence areas: Ant colony optimization (ACO) and Particle swarm optimization (PSO). This paper made an attempt to evaluate their performance of these two swarm intelligence techniques. A real engineering application of bevel gear design optimization is considered and results are analyzed with respect to the context.

References

• Majid Jaberipour , Esmaile Khorram 2010, “Two improved harmony search algorithms for solving engineering optimization problems”, Commun Nonlinear Sci Numer Simulat 15 (2010), pp 3316–3331.
• Lin, C., Liu Y., and Lee, C., 2008, “An efficient neural fuzzy network based on immune particle swarm optimization for prediction and control applications”, Journal of Innovative Computing, Information and Control Vol.4, No.7, pp.1711-1722.
• Wang C-Q, Cao Y-F, Dai G-Z 2004, “ Bi-directional convergence ACO for job-shop scheduling”, Computer Integrated Manufacturing System Vol.10 (7), pp 820–824.
• Davoud Sedighizadeh and Ellips Masehian 2009, “Particle Swarm Optimization Methods, Taxonomy and Applications” International Journal of Computer Theory and Engineering, Vol. 1, No. 5, December 2009.
• V. Savsani, R.V. Rao, D.P. Vakharia 2010, “Optimal weight design of a gear train using particle swarm optimization and simulated annealing algorithms”, Mechanism and Machine Theory , Vol.45, pp 531–541.
• Ruifeng Bo, Ruiqin Li and Hongxia Pan 2008, “Concept optimization for mechanical product by using ant colony system”, Journal of Mechanical Science and Technology 22, pp. 628~638.
• Zhou P, Li X-P, Zhang H-F 2004 “ An ant colony algorithm for job shop scheduling problem”, Proceedings of the 5th worldcongress on intelligent control and automation, China, 15–19, pp 2899–2903.
• S. M. Kannan & R. Sivasubramanian and V. Jayabalan 2009, “Particle swarm optimization for minimizing assembly variation in selective assembly”, Int Jour. Adv Manuf Technology, Vol 42, pp 793–803.
• Shu-Kai S. Fan, Ju-Ming Chang 2009, “A parallel particle swarm optimization algorithm for multi-objective optimization problems”, Engineering Optimization, Vol. 41, No. 7, pp 673–697.
• Ju Seok Kang and Yeon-Sun Choi 2008, “Optimization of helix angle for helical gear system”, Journal of Mechanical Science and Technology, Vol 22, pp. 2393~2402.
• Zhang Shaojun, WAN Zhong and LIU GuangLian 2011, “Global optimization design method for maximizing the capacity of V-belt drive”, Science China Press and Springer-Verlag Berlin Heidelberg, Vol.54 No.1: 140–147.
• Yin, P. Y. 2006, “Genetic particle swarm optimization for polygonal approximation of digital curves” Journal of Pattern Recognition and Image Analysis., Vol. 16, No. 2, pp. 223-233.
• Rania Hassan, Babak Cohanim, Olivier de Weck 2004, “A Comparison of Particle Swarm Optimization and the Genetic Algorithm”, American Institute of Aeronautics and Astronautics.
• Kalyanmoy Deb and Sachin Jain 2003. “Multi-Speed Gearbox Design Using Multi-Objective Evolutionary Algorithm’s. Journal of Mechanical design, 125: pp 609-619.
• Padmanabhan.S, M.Chandrasekaran and V.Srinivasa Raman 2010, “Optimization of Spur Gear Design Using Metaheuristic Algorithms”, In Proceedings of International Conference on Advances in Industrial Engineering Applications (ICAIEA 2010), Anna University, Chennai.
• Peng-Yeng Yin 2006, “Particle Swarm Optimization for point pattern matching”. Journal of Visual Communication and image representation. pp 143- 162.
• Kennedy, J. and Eberhart, R. C. 1995, Particle swarm optimization. Proc. IEEE int'l conf. on neural networks Vol. IV, IEEE service centre, Piscataway, NJ. pp. 1942-1948.
• Dorigo M, Albert Colorini, 1996, “The ant system: Optimization by a colony of cooperating agents”,IEEE Transaction of Systems, Man and Cybernetics; Part B 26(1), pp1-13.
• Jayaram V K, Kulkarni B D, Sachin Karale, Prakash Shelokan, 2000, “Ant Colony Frame Work For Optimal Design And Scheduling Of Batch Plants” , International Journal of computers and chemical engineering ; vol.24, pp1901-1912.
• Baskar.N, Asokan.P, Saravanan.R and Prabhakaran.G, “Optimization of machining parameters for milling operations using non-conventional methods”, International Journal of Advanced Manufacturing Technology, Vol.25, 2005, pp. 1078-1088.
• Jain P, Agogino A M, Theory of design: An optimization perspective. Mech. Mach. Theory 1990; 25 (3), pp.287-303.
• Rao S S, Eslampour H R, 1986, “Multi stage multi objective optimization of gear boxes” Journal of mechanisms, Transmissions and Automation in Design, vol.108, pp 461-468.
• C. Innocenti, DIEM – University of Bologna, Viale Risorgimento, 2-40136 Bologna, Italy, “A Framework for Efficiency Evaluationn of Multi-Degree-of-Freedom Gear Trains”, Transactions of the ASME, 556 / vol. 118, December 1996.
• Deb K, 2005, Optimization for engineering design – algorithms and examples, Eighth Printing, New Delhi, Published by Asoke K. Ghosh, Prentice-Hall of India Private Limited.
• Design Data, Faculty of Mechanical Engineering, PSG College of Technology, Coimbatore-641004.
• Ying Chin Ho, Colin L. Moodie, 1998, “Machine Layout with a linear Single-RowFlow Path in an Automated Manufacturing System”, Journal of Manufacturing Systems, 17 (1), pp 1 – 22.