Solving engineering optimization problems with tabu/scatter search

• Autores: Ricardo P. Beausoleil
• Localización: Revista de Matemática: Teoría y Aplicaciones, ISSN 2215-3373, ISSN-e 2215-3373, Vol. 24, Nº. 1, 2017, págs. 157-188
• Idioma: inglés
• DOI: 10.15517/rmta.v24i1.27796
• Títulos paralelos:
  • Resolviendo problemas de optimización en ingeniería con búsqueda tabú/dispersa
• Enlaces
  • Texto completo (pdf)
• Resumen
  • español

    Este artículo introduce una adaptación de una Búsqueda Tabú/ Dispersa multiobjetivo para problemas de ingeniería con restricciones no lineales con valores discretos y enteros-mixtos. El problema es reducido a un problema bi-objetivo, (la función objetivo y la función de violación delas restricciones). Este enfoque elimina el uso de penalidades para manipular las restricciones. El desempeño del algoritmo fue probado con diferentes problemas conocidos de la ingeniería, incluyendo algunas funciones de optimización matemática e ingeniería estructural. Los resultadosmuestran que el método propuesto trabaja bien en términos de eficiencia y robustez.

  • English

    This paper introduces an adaptation of a multiobjective tabu/scatter search to deal with nonlinear discrete, mixed-integer constrained engineering optimization problems. The problem is reduced to a bi-objective problem (the objective function and the constraint violation function). This approach eliminates the use of penalties for constraint handling. Its performance was proved with different standard engineering optimization problems, including mathematical function minimization and structural engineering. The results show that the proposed method performs well in termsof efficiency and robustness.

• Referencias bibliográficas
  • Akhtar, S.; Tai, K.; Ray, T. (2002) “A socio-behavioural simulation model for engineering design optimization”, Eng. Opt. 34: 341–354.
  • Arora, J.S. (1989) Introduction to Optimum Design. McGraw-Hill, New York.
  • Beausoleil, R. (2006) “MOSS Multiobjective scatter search applied to non-linear multiple criteria optimization”, European Journal of Operational...
  • Beausoleil, R. (2008) “MOSS-II Tabu/Scatter Search for nonlinear multi- objective optimization”, in: Z. Michalewicz & P. Siarry (Eds.)...
  • Belegundu, A.D. (1982) “A study of mathematical programming method for structured optimization”, R.I. Dept. of Civil and Environment Engineering...
  • Cagnina, L.C.; Esquivel, S.; Coello, C.A. (2008) “Solving engineering optimization problems with the simple constrained particle swarm optimizer”,...
  • Chen, T.Y.; Cheng, Y.L. (2008) “Global optimization using hybrid approach”, WSEAS Transactions on Mathematics 7: 254–262.
  • Chickermane, Chang, H.; Gea, H. (1996) “Structural Optimization using a New Local approximation”, International Journal for Numerical Method...
  • Ching-Long, S.; Shutan, H. (2008) Latest Trends on Computers (Volume II).
  • De Jong, K. (1975) Analysis of the Behaviour of a Class of Genetic Adaptive Systems. Ph.D. Thesis, University of Michigan, Ann Arbor MI.
  • Deb, K. (1991) “Optimal design of a weld beam via genetic algorithm”, AIAA Journal 29(11): 2013–2015.
  • Ettaouil, M.; Loqman C. (2008) “Constraint satisfaction problems solved by semidefinite relaxations”, WSEAS Transactions on Computers 7: 951–961.
  • Fesanghary, M.; Mahdavi, M.; Minary-Jolandan, M.; Alizadeh, Y. (2008) “Hybridizing harmony search algorithm with sequential quadratic programming...
  • Fogel, L.J.; Owens, A.J.: Walsh, M.J. (1966) Artificial Intelligence Through Simulated Evolution. John Wiley, Chichester UK.
  • Geem, Z.W.; Kim, J.H.; Loganathan, G.V. (2001) “A new heuristic optimization algorithm: harmony search”, Simulation 76(2): 60–68.
  • Glover, F. (1977) “Heuristic for integer programming using surrogate constraints”, Decision Sci. 8(1): 156–166.
  • Glover, F. (1994) “Tabu search for nonlinear and parametric optimization (with links to genetic algorithms”, Discrete Applied Mathematics...
  • Glover, F. (2005) “Adaptive memory projection methods for integer programming”, in: Rego C. & Alidee B. (Eds.) Metaheuristics Optimization...
  • Goldberg, D.E. (1989) “Genetic Algorithms in Search, Optimization and Machine Learning”. Addison Wesley, Boston MA.
  • Golinski, J. (1973) “An adaptive optimization system applied to machine synthesis”, Mech. Mach. Theory 8(4): 419–436.
  • Hernández, S. (1994) “Multi-objective structural optimisation”, in: S. Kodiyalam & M. Saxena (Eds.) Geometry and Optimisation Techniques...
  • Holland, J.H. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor MI.
  • Kaveh, A.; Talatahari, S. (2009) “Engineering optimization with hybrid particle swarm and ant colony optimization”, Asian Journal of Civil...
  • Kirkpatrick, S.; Gelatt, D.; Vecchi, M.P. (1983) “Optimization by simulated annealing”, Science 220: 671–680.
  • Koza, J.R. (1990) “Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems”, Rep. No....
  • Lee, K.S.; Geem, Z.W. (2005) “A new meta-heuristic algorithm for continuous engineering optimization: Harmony search theory and practice”,...
  • Mahdavi, M.; Fesanghary, M.; Damangir, E. (2007) “An Improved Harmony Search Algorithm for Solving Optimization Problems”, Applied Mathematics...
  • Parsopoulos, K.E.; Vrahatis, M.N. (2005) “Particle swarm optimization for solving constrained engineering optimization problems”, in: L. Wang,...
  • Raj, K.H.; Sharma, R.S.; Mishra, G.S.; Dua, A.; Patvardhan, C. (2005) “An evolutionary computational technique for constrained optimisation...
  • Ray, T.; Saini, P. (2001) “Engineering design optimisation using a swarm with intelligent information sharing among individuals”, Engineering...
  • Sahab, M.G.; Toropov, V.V.; Ashour, A.F. (2004) “A hybrid genetic algorithm for structural optimization probems”, Asian Journal of Civil Engineering...
  • Sandgren, E. (1990) “Nonlinear integer and discrete programming in mechanical design optimization”, J. Mech. Des. ASME 112: 223–229.
  • Singiresu, S.; Ying, X. (2005) Journal of Mechanical Design 127(6): 1100–1112.