Adaptive boundary constraint-handling scheme for constrained optimization

• Alvarez-Benitez JE, Everson RM, Fieldsend JE (2005) A MOPSO algorithm based exclusively on pareto dominance concepts. In: International conference on evolutionary multi-criterion optimization. Springer, Berlin, pp 459–473

• Arabas J, Szczepankiewicz A, Wroniak T (2010) Experimental comparison of methods to handle boundary constraints in differential evolution. In: International conference on parallel problem solving from nature. Springer, Berlin, pp 411–420

• Bäck T, Fogel DB, Michalewicz Z (1997) Handbook of evolutionary computation. IOP Publishing Ltd., Bristol

  Book  MATH  Google Scholar 

• Brest J, Greiner S, Boskovic B, Mernik M, Zumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evol Comput 10(6):646–657

  Article  Google Scholar 

• Chu W, Gao X, Sorooshian S (2011) Handling boundary constraints for particle swarm optimization in high-dimensional search space. Inf Sci 181(20):4569–4581

  Article  Google Scholar 

• Clerc M (2006) Confinements and biases in particle swarm optimisation. Technical Report hal-00122799. https://hal.archives-ouvertes.fr/hal-00122799

• Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73

  Article  Google Scholar 

• Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186(2):311–338

  Article  MATH  Google Scholar 

• Eberhart RC, Kennedy J, et al (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, vol 1. New York, NY, pp 39–43

• Elsayed SM, Sarker RA, Mezura-Montes E (2014) Self-adaptive mix of particle swarm methodologies for constrained optimization. Inf Sci 277:216–233

  Article  MathSciNet  MATH  Google Scholar 

• Gandomi AH, Yang XS (2012) Evolutionary boundary constraint handling scheme. Neural Comput Appl 21(6):1449–1462

  Article  Google Scholar 

• Helwig S, Wanka R (2007) Particle swarm optimization in high-dimensional bounded search spaces. In: 2007 IEEE swarm intelligence symposium. IEEE, pp 198–205

• Helwig S, Branke J, Mostaghim S (2013) Experimental analysis of bound handling techniques in particle swarm optimization. IEEE Trans Evol Comput 17(2):259–271

  Article  Google Scholar 

• Huang T, Mohan A (2005) A hybrid boundary condition for robust particle swarm optimization. IEEE Antennas Wirel Propag Lett 4:112–117. https://doi.org/10.1109/LAWP.2005.846166

  Article  Google Scholar 

• Jia G, Wang Y, Cai Z, Jin Y (2013) An improved (\(\mu + \lambda \))-constrained differential evolution for constrained optimization. Inf Sci 222:302–322

  Article  MathSciNet  MATH  Google Scholar 

• Jordehi AR (2015) A review on constraint handling strategies in particle swarm optimisation. Neural Comput Appl 26(6):1265–1275

  Article  Google Scholar 

• Juárez-Castillo E, Pérez-Castro N, Mezura-Montes E (2015) A novel boundary constraint-handling technique for constrained numerical optimization problems. In: 2015 IEEE congress on evolutionary computation (CEC). IEEE, pp 2034–2041

• Juárez-Castillo E, Acosta-Mesa HG, Mezura-Montes E (2017a) Empirical study of bound constraint-handling methods in particle swarm optimization for constrained search spaces. In: 2017 IEEE congress on evolutionary computation (CEC). IEEE, pp 604–611

• Juárez-Castillo E, Pérez-Castro N, Mezura-Montes E (2017b) An improved centroid-based boundary constraint-handling method in differential evolution for constrained optimization. Int J Pattern Recogn Artif Intell 31:1759023

  Article  MathSciNet  Google Scholar 

• Kennedy J (2006) Swarm intelligence. In: Zomaya AY (ed) Handbook of nature-inspired and innovative computing. Springer, Berlin, pp 187–219

  Chapter  Google Scholar 

• Kukkonen S, Lampinen J (2005) Gde3: the third evolution step of generalized differential evolution. In: 2005 IEEE congress on evolutionary computation, vol 1. IEEE, pp 443–450

• Lampinen J (2002) A constraint handling approach for the differential evolution algorithm. In: Proceedings of the congress on evolutionary computation, vol 2. IEEE Computer Society Washington, DC, pp 1468–1473

• Liang J, Runarsson TP, Mezura-Montes E, Clerc M, Suganthan P, Coello CC, Deb K (2006) Problem definitions and evaluation criteria for the CEC 2006 special session on constrained real-parameter optimization. J Appl Mech 41(8):8–31

  Google Scholar 

• Mallipeddi R, Suganthan PN (2010) Problem definitions and evaluation criteria for the CEC 2010 competition on constrained real-parameter optimization. Nanyang Technological University, Singapore

  Google Scholar 

• Mezura-Montes E, Coello CAC (2011) Constraint-handling in nature-inspired numerical optimization: past, present and future. Swarm Evol Comput 1(4):173–194

  Article  Google Scholar 

• Mezura-Montes E, Miranda-Varela ME, del Carmen Gómez-Ramón R (2010) Differential evolution in constrained numerical optimization: an empirical study. Inf Sci 180(22):4223–4262

  Article  MathSciNet  MATH  Google Scholar 

• Padhye N, Deb K, Mittal P (2013) Boundary handling approaches in particle swarm optimization. In: Proceedings of seventh international conference on bio-inspired computing: theories and applications (BIC-TA 2012). Springer, Berlin, pp 287–298

• Padhye N, Mittal P, Deb K (2015) Feasibility preserving constraint-handling strategies for real parameter evolutionary optimization. Comput Optim Appl 62(3):851–890

  Article  MathSciNet  MATH  Google Scholar 

• Price K, Storn RM, Lampinen JA (2005) Differential evolution: a practical approach to global optimization (natural computing series). Springer, Berlin

  MATH  Google Scholar 

• Purchla M, Malanowski M, Terlecki P, Arabas J (2004) Experimental comparison of repair methods for box constraints. In: Proceedings of 7th national conference on evolutionary computation and global optimisation, pp 135–142

• Robinson J, Rahmat-Samii Y (2004) Particle swarm optimization in electromagnetics. IEEE Trans Antennas Propag 52(2):397–407

  Article  MathSciNet  MATH  Google Scholar 

• Ronkkonen J, Kukkonen S, Price KV (2005) Real-parameter optimization with differential evolution. Proc IEEE CEC 1:506–513

  Google Scholar 

• Runarsson TP, Yao X (2000) Stochastic ranking for constrained evolutionary optimization. IEEE Trans Evol Comput 4(3):284–294

  Article  Google Scholar 

• Shi Y, Cheng S, Qin Q (2011) Experimental study on boundary constraints handling in particle swarm optimization: from population diversity perspective. Int J Swarm Intell Res 2(3):43–69

  Article  Google Scholar 

• Simon D (2013) Evolutionary optimization algorithms. Wiley, New York

  Google Scholar 

• Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

  Article  MathSciNet  MATH  Google Scholar 

• Wang Y, Wang BC, Li HX, Yen GG (2016) Incorporating objective function information into the feasibility rule for constrained evolutionary optimization. IEEE Trans Cybern 46(12):2938–2952

  Article  Google Scholar 

• Wang BC, Li HX, Li JP, Wang Y (2018) Composite differential evolution for constrained evolutionary optimization. IEEE Trans Syst Man Cybern Syst PP:1–14

  Google Scholar 

• Wei J, Jia L (2013) A novel particle swarm optimization algorithm with local search for dynamic constrained multi-objective optimization problems. In: 2013 IEEE congress on evolutionary computation (CEC). IEEE, pp 2436–2443

• Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

  Article  Google Scholar 

• Wu G, Mallipeddi R, Suganthan P (2016) Problem definitions and evaluation criteria for the CEC 2017 competition on constrained real-parameter optimization. National University of Defense Technology, Changsha, Hunan, PR China and Kyungpook National University, Daegu, South Korea and Nanyang Technological University, Singapore, Technical Report

• Xu S, Rahmat-Samii Y (2007) Boundary conditions in particle swarm optimization revisited. IEEE Trans Antennas Propag 55(3):760–765

  Article  Google Scholar 

• Yang XS (2014) Nature-inspired optimization algorithms. Elsevier, Amsterdam

  MATH  Google Scholar 

• Zhang WJ, Xie XF, Bi DC (2004) Handling boundary constraints for numerical optimization by particle swarm flying in periodic search space. In: CEC2004. Congress on evolutionary computation, 2004, vol 2. IEEE, pp 2307–2311

• Zhong J, Hu X, Zhang J, Gu M (2005) Comparison of performance between different selection strategies on simple genetic algorithms. In: Computational intelligence for modelling, control and automation, 2005 and international conference on international conference on intelligent agents, web technologies and internet commerce, vol 2. IEEE, pp 1115–1121

Download references