Performance Studies on Differential Evolution Algorithm and Shuffled Frog-Leaping Algorithm for Simulated Manufacturing Problems

Abstract:

The real world engineering problems are complex associated with lot of factors. The objective of mathematic models in simulated manufacturing problems are to minimize cost or maximize profits while satisfying the constraints. The purpose of this article was to study two algorithms for testing their efficiency in solving non-linear optimization problems and simulated manufacturing problems. A well-known meta-heuristic approach called Differential Evolution (DE) was compared with Shuffled Frog-leaping Algorithm (SFLA) in term of mean, maximum, minimum, and standard deviation of the solution. SFLA was better than DE in terms of the performance to finding optimal solutions because of the unique process of memeplex, which can increase speed of convergence and find turning parameters.

• Periodical: Applied Mechanics and Materials (Volume 835)
• Pages: 858-863
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.835.858
• Online since: May 2016
• Authors: Lakkana Ruekkasaem*, Pasura Aungkulanon
• Keywords: Differential Evolution Algorithm, Meta-Heuristics Algorithms, Optimization, Shuffled Frog-Leaping Algorithm, Simulated Manufacturing Problems

References

[1] V. Granville, M. Krivanek, J.P. Rasson, Simulated annealing: a Proof of Convergence Pattern Analysis and Machine Intelligence, IEEE Tran. 16 (1994) 652–656.

DOI: 10.1109/34.295910

[2] A. Lokketangen,K. Jornsten, S. Storoy, Tabu Search within a Pivot and Complement Framework, Int. Tran. Operat. Res. 1 (1994) 305-316.

DOI: 10.1111/1475-3995.d01-42

[3] B.A. Garro, R.A. Vázquez, Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms, Comput. Intell. Neurosci. (2015) 1-20.

DOI: 10.1155/2015/369298

[4] F. Cus, J. Balic, Optimisation of Cutting Process by GA Approach, Robot. Com. Integ. Manuf, 19 (2003) 113–121.

[5] M. Dorigo, V. Maniezzo, A. Colorni, Ant System: Optimisation by a Colony of Cooperating Agents, IEEE Tran. Part B. 26 (1996) 29-41.

DOI: 10.1109/3477.484436

[6] R. Storn, K. Price, System Design by Constraint Adaptation and Deferential Evolution, J. Global Optim. 11 (1997) 341–359.

[7] J. Brest, S. Greiner, B. Boscovic, M. Mernik, V. Zumer, Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems, IEEE Tran. on Evo. Com. 10 (2006) 646-657.

DOI: 10.1109/tevc.2006.872133

[8] A. Shamekhi. An Improved Differential Evolution Optimization Algorithm, Int. J. Res. Rev. Appl. Sci. 15 (2013) 132-145.

[9] M. Clerc, J. Kennedy, The Particle Swarm-Explosion Stability and Convergence in a Multidimensional Complex Space, IEEE Tran. on Evo. Com. 6 (2002) 58-73.

DOI: 10.1109/4235.985692

[10] D.T. Pham, M. Castellani, The Bees Algorithm – Modelling Foraging Behaviour to Solve Continuous Optimisation Problems, Proc. Imech E, Part C. 12 (2009) 2919-2938.

DOI: 10.1243/09544062jmes1494

[11] K.S. Lee, Z.W. Geem, A New Meta-heuristic Algorithm for Con. Eng. Optimisation: Harmony Search Theory: Meth. Appl. Mech. Eng. 194 (2004) 3902–3933.

[12] L. Wang, C. Fang, An effective shuffled frog-leaping algorithm for multi-mode resource-constrained project scheduling problem, Inform. Sci. 181 (2011) 4804–4822.

DOI: 10.1016/j.ins.2011.06.014

[13] J.V. Kumar, D.M.V. Kumar, Generation bidding strategy in a pool based electricity market using Shuffled Frog Leaping Algorithm, Appl. Soft Comput. 21 (2014) 407-414.

DOI: 10.1016/j.asoc.2014.03.027

[14] A.S. Bhagade, P.V. Puranik, Artificial Bee Colony (ABC) Algorithm for Vehicle Routing Optimization Problem, Int. J. Soft Comput. Eng. 2 (2012) 329-333.

[15] S. Das, P.N. Suganthan, Differential Evolution: A Survey of the State-of-the-Art, IEEE Tran. on Evo. Com. 15 (2011) 4-31.

[16] M. Iwan, R. Akmeliawatib, T. Faisala, H.M. Assadi, Performance Comparison of Differential Evolution and Particle Swarm Optimization In Constrained Optimization, Proc. Eng. 41 (2012) 1323–1328.

DOI: 10.1016/j.proeng.2012.07.317

[17] M. Eslamian, S.H. Hosseinian, B. Vahidi, Bacterial foraging-based solution to the unitcommitment problem, IEEE Trans. Power Syst. 24(3) (2009) 1478–1488.

DOI: 10.1109/tpwrs.2009.2021216

[18] H. Elbehairy, E. Elbeltagi, T. Hegazy, Comparison of two evolutionary algorithms for optimization of bridge deck repairs, Comput. Aided Civ. Infrastruct. Eng. 21 (2006) 561–572.

DOI: 10.1111/j.1467-8667.2006.00458.x

[19] A.R. Vahed, A.H. Mirzaei, Solving a bi-criteria permutation flow-shop problem using shuffled frog-leaping algorithm , Soft Computing. Springer-Verlag, New York, (2007) 435-452.

DOI: 10.1007/s00500-007-0210-y

[20] Z. Khan, L.B. Prasad, T. Singhl, Machine Condition Optimisation by Genetic Algorithms and Simulated, Comput. Ops Res. 24 (1997) 647-657.

[21] B. Amiri, M. Fathian, A. Maroosi, Application of shuffled frog-leaping algorithm on clustering, Int. J. Ad. Manuf. Tech. 45 (2009) 199-209.

DOI: 10.1007/s00170-009-1958-2

[22] N. Chai-ead, P. Aungkulanon, P. Luangpaiboon, Bees and firefly algorithms for noisy non-linear optimisation problems. Prof. Int. Multiconference of Engineers and Computer Scientists 2011-2 (2011) 1449–1454.

DOI: 10.1142/9789814390019_0005

[23] P. Aungkulanon, N. Chai-ead, P. Luangpaiboon, Simulated manufacturing process improvement via particle swarm optimisation and firefly algorithms, Prof. Int. Multiconference of Engineers and Computer Scientists, 2011-2 (2011) 1123–1128.

DOI: 10.1142/9789814390019_0017