A Comparison of Sequential Quadratic Programming, Genetic Algorithm, Simulated Annealing, Particle Swarm Optimization and Hybrid Algorithm for the Design and Optimization of Golinski’s Speed Reducer

Yıl 2017, Cilt: 4 Sayı: 2, 34 - 52, 10.08.2017

Cenker Aktemur , Islam Gusseinov

Öz

This article provides information
on different optimization methods such as Sequential Quadratic Programming (SQP),
Genetic Algorithm (GA), Simulated Annealing (SA), Particle Swarm Optimization (PSO)
and Hybrid Algorithm (HA). Optimization is a method of designing a system in
such a manner that it falls into all limitations on design and satisfies all
the design parameters provided. In this particular study, Matlab software is
used to perform these optimization methods. It is very helpful software with
wide range of applications. One of the such applications is optimization
toolbox which is called optimtool. It contains readily
written codes for different optimization tools. After conducting optimization
on Golinski’s speed reducer with five various optimization method, the results
are in kilograms for the weight optimization which are SQP = 2994.355 kg, GA =
2994.914 kg, SA = 2730.74 kg, HA = 2994.355 kg, and PSO = 2905.677.
The figure below, which is thought graphically abstract,
represents a result of all optimizations.

Anahtar Kelimeler

optimization, golinski’s speed reducer, design parameters, limitation

Kaynakça

• [1] Ray, Tapabrata. "Golinski's speed reducer problem revisited." AIAA journal 41.3 (2003): 556-558.
• [2] Datseris, P. "Weight minimization of a speed reducer by heuristic and decomposition techniques." Mechanism and Machine Theory 17.4 (1982): 255-262.
• [3] Deb, Kalyanmoy, et al. "A fast and elitist multiobjective genetic algorithm: NSGA-II." IEEE transactions on evolutionary computation 6.2 (2002): 182-197.
• [4] Kennedy, James. "Particle swarm optimization." Encyclopedia of machine learning. Springer US, 2011. 760-766.
• [5] Dorigo, Marco, and Luca Maria Gambardella. "Ant colony system: a cooperative learning approach to the traveling salesman problem." IEEE Transactions on evolutionary computation 1.1 (1997): 53-66.
• [6] Hassan, Rania, et al. "A comparison of particle swarm optimization and the genetic algorithm." 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. 2005.
• [7] Yang, Chunming, and Dan Simon. "A new particle swarm optimization technique." Systems Engineering, 2005. ICSEng 2005. 18th International Conference on. IEEE, 2005.
• [8] Eberhart, Russell, and James Kennedy. "A new optimizer using particle swarm theory." Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on. IEEE, 1995.
• [9] Eberhart, Russell C., and Yuhui Shi. "Evolving artificial neural networks." Proceedings of the International Conference on Neural Networks and Brain. Vol. 1. No. 998. PRC, 1998..
• [10] Eberhart, Russell C., and Yuhui Shi. "Comparison between genetic algorithms and particle swarm optimization." International Conference on Evolutionary Programming. Springer Berlin Heidelberg, 1998.