Constrained particle swarm optimization using a bi-objective formulation

• Barbosa H, Lemonge A (2003) A new adaptive penalty scheme for genetic algorithms. Inf Sci 156(3–4):215–251

  Article  MathSciNet  Google Scholar 

• Bratton D, Kennedy J (2007) Defining a standard for particle swarm optimization. In: Proceedings of the 2007 IEEE swarm intelligence symposium. IEEE, Piscataway, pp 120–127

  Chapter  Google Scholar 

• Coello Coello C (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191(11–12):1245–1287

  Article  MATH  MathSciNet  Google Scholar 

• Deb K, Agarwal S, Pratap A, Meyarivan T (2000) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Proceedings of the parallel problem solving from nature VI conference. Springer, New York, pp 849–858

  Chapter  Google Scholar 

• Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

  Article  Google Scholar 

• Fletcher R, Leyffer S (2002) Nonlinear programming without a penalty function. Math Program 91(2):239–269

  Article  MATH  MathSciNet  Google Scholar 

• Grosset L, LeRiche R, Haftka RT (2005) A double-distributed statistical algorithm for composite laminate optimization. Struct Multidiscipl Optim 31(1):49–59

  Article  Google Scholar 

• Hamida B, Schoenauer M (2000) An adaptive algorithm for constrained optimization problems. In: Proceedings of the 6th conference on parallel problems solving from nature. Springer, New York, pp 529–539

  Chapter  Google Scholar 

• Koziel S, Michalewicz Z (1999) Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization. Evol Comput 7(1):19–44

  Article  Google Scholar 

• Liang J, Runarsson T, Mezura-Montes E, Clerc M, Suganthan P, Coello Coello C, Deb K (2006) Problem definitions and evaluation criteria for the CEC 2006 special session on constrained real-parameter optimization. Technical report, Nanyang Technological University, Singapore

• Liu CA (2007) New multiobjective PSO algorithm for nonlinear constrained programming problems. In: Advances in cognitive neurodynamics ICCN 2007. Springer, New York, pp 955–962

  Google Scholar 

• Poon N, Martins J (2007) An adaptive approach to constraint aggregation using adjoint sensitivity analysis. Struct Multidiscipl Optim 34(1):61–73

  Article  Google Scholar 

• Reyes-Sierra M, Coello Coello C (2005) Improving PSO-based multi-objective optimization using crowding, mutation and ϵ-dominance. In: Third international conference on evolutionary multi-criterion optimization, Guanajuato, 9–11 March 2005, pp 505–519

• Reyes-Sierra M, Coello Coello C (2006) Multi-objective particle swarm optimizers: a survey of the state-of-the-art. Int J Comput Intel Res 2(3):287–308

  MathSciNet  Google Scholar 

• Sienz J, Innocente M (2008) Trends in engineering computational technology, chap. particle swarm optimization: fundamental study and its application to optimization and to Jetty scheduling problems. Saxe-Coburg, Stirling, pp 103–126

• Surry P, Radcliffe N (1997) The COMOGA method: constrained optimisation by multi-objective genetic algorithms. Control Cybern 26(3):391–412

  MathSciNet  Google Scholar 

• Vanderplaats GN (2005) Numerical optimization techniques for engineering design, 4rd edn. Vanderplaats Research and Development, Colorado Springs

• Venkatraman S, Yen G (2005) A generic framework for constrained optimization using genetic algorithms. IEEE Trans Evol Comput 9:424–435

  Article  Google Scholar 

• Venter G, Sobieszczanski-Sobieski J (2003) Particle swarm optimization. AIAA J 41(8):1583–1589

  Article  Google Scholar 

• Venter G, Sobieszczanski-Sobieski J (2004) Multidisciplinary optimization of a transport aircraft wing using particle swarm optimization. Struct Multidiscipl Optim 26(1):121–131

  Article  Google Scholar 

• Venter G, Sobieszczanski-Sobieski J (2006) A parallel particle swarm optimization algorithm accelerated by asynchronous evaluations. J Aerosp Comput Inf Commun 3(3):123–137

  Article  Google Scholar 

• Zhou Y, Li Y, He J, Kang L (2003) Multi-objective and MGG evolutionary algorithm for constrained optimization. In: The 2003 Congress on evolutionary computation, Canberra, 8–12 December 2003, pp 1–5

Download references