A Swarm Intelligence Based (SIB) method for optimization in designs of experiments

• Bera R, Roy JS (2013) Thinning of elliptical and concentric elliptical antenna arrays using particle swarm optimization. Microw Rev 19:2–7

  Google Scholar 

• Box KHV, Cox DR (1962) Some systematic supersaturated designs. Technometrics 4:489–495

  Article  MATH  MathSciNet  Google Scholar 

• Boyd S, Vandenverghe L (2004) Convex optimization. Cambridge University Press, Cambridge

  Book  Google Scholar 

• Braendler D, Hendtlass T (2002) The suitability of particle swarm optimization for training neural hardware. In: International conference on industrial and engineering applications of artificial intelligence and expert system, 190–199 (2002)

• Brandstatter B, Baumgartner U (2002) Particle swarm optimization—mass-spring system analogon. IEEE Trans Magn 38:997–1000

  Article  Google Scholar 

• Bulutoglu DA (2007) Cyclicly constructed \(E(s^2)\)optimal supersaturated designs. J Stat Plan Inference 137:2413–2428

  Article  MATH  MathSciNet  Google Scholar 

• Bulutoglu DA, Cheng CS (2004) Construction of \(E(s^2)\)-optimal supersaturated designs. Annal Stat 32:1662–1678

  Article  MATH  MathSciNet  Google Scholar 

• Chakrabarti R, Chattopadhyay PK, Basu M, Panigrahi CK (2006) Particle swarm optimization technique for dynamic economic dispatch. J Inst Eng (India) 87:48–54

  Google Scholar 

• Chen RB, Hsieh DN, Hung Y, Wang W (2013) Optimizing Latin hypercube designs by particle swarm. Stat Comput 23:663–676

  Article  MATH  MathSciNet  Google Scholar 

• Clerc M (2006) Particle swarm optimization. ISTE, London

  Book  MATH  Google Scholar 

• Craigen R, Faucher G, Low R, Wares T (2013) Circulant partial Hadamard matrices. Linear Algebra Appl 439:3307–3317

  Article  MATH  MathSciNet  Google Scholar 

• Dawkins R (1976) The selfish gene. Oxford University Press, London

  Google Scholar 

• Dorigo M (1992) Learning and Natural Algorithms. PhD thesis, Politecnico di Milano, Italy

• Eberhart R, Shi Y (2001) Particle swarm optimization: developments, application and resources. In: IEEE proceedings of the congress on evolutionary computation 1:81–85

• Engelbrecht AP (2005) Fundamentals of computational swarm intelligence. Wiley, Chichester

  Google Scholar 

• Eusuff M, Lansey K, Pasha F (2006) Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng Optim 38:129–154

  Article  MathSciNet  Google Scholar 

• Fraser A (1957) Simulation of genetic systems by automatic digital computers. I. Introduction. Aust J Biol Sci 10:484–491

  Article  Google Scholar 

• Geramita A, Seberry J (1979) Quadratic forms, orthogonal designs, and Hadamard matrices, Lecture notes in pure and applied mathematics series 45, Marcel Dekker Inc., New York

• Gong T, Tuson AL (2008) Particle swarm optimization for quadratic assignment problems—a forma analysis approach. Int J Comput Intell Res 4:177–185

  Article  MathSciNet  Google Scholar 

• Horadam KJ (2007) Hadamard matrices and their applications. Priceton University Press, Princeton

  Book  MATH  Google Scholar 

• Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conferenec on neural networks 4:1942–1948

• Low RM, Stamp M, Craigen R, Faucher G (2005) Unpredictable binary strings. Congr Numer 177:65–75

  MATH  MathSciNet  Google Scholar 

• Morris MD, Mitchell TJ (1995) Exploratory designs for computational experiments. J Stat Plan Inference 43:381–402

  Article  MATH  Google Scholar 

• Orouskhano M, Orouskhani Y, Mansouri M, Teshnehlab M (2013) A novel cat swarm optimization algorithm for unconstrained optimization problems. I J Inf Technol Comput Sci 11:32–41

  Google Scholar 

• Panduro MA, Brizuela CA, Balderas LI, Acosta DA (2009) A comparison of genetic algorithms, particle swarm optimization and the differential evolution method for the design of scannable circular antenna arrays. Prog Electromagn Res B 13:171–186

  Article  Google Scholar 

• Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22:52–67

  Article  Google Scholar 

• Phoa FKH, Chen RB, Wang W, Wong WK (2016) Optimizing two-level supersaturated designs using swarm intelligence techniques. Technometrics 58:43–49

• Plackett RL, Burman JP (1946) The design of optimum multifactorial experiments. Biometrika 33:305–325

  Article  MATH  MathSciNet  Google Scholar 

• Seeley TD (1995) The wisdom of the hive. Harvard University Press, Cambridge

  Google Scholar 

• Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Proceedings of IEEE international conference on evolutionary computation, 69–73

• Xu H, Phoa FKH, Wong WK (2009) Recent developments in nonregular fractional factorial designs. Stat Surv 3:18–46

  Article  MATH  MathSciNet  Google Scholar 

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