Skip to main content
SpringerLink
Log in

A Hybrid Approach Based on Evolutionary Strategies and Interval Arithmetic to Perform Robust Designs

  • Chapter
Evolutionary Computation in Dynamic and Uncertain Environments

Part of the book series: Studies in Computational Intelligence ((SCI,volume 51))

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Armengol J, de la Rosa JL, Travé-Massuyés L (1998) On Modal Inter-val Analysis for Envelope Determination within Ca-En Qualitative Simulator. http://ima.udg.es/SIGLA/X

  2. Averbakh I (2000) Minmax regret solutions for minimax optimization problems with uncertainty. Operations Research Letters, 27: 57-65

    Article  MATH  MathSciNet  Google Scholar 

  3. Averbakh I, Lebedev V (2002) Interval data minmax regret network optimiza- tion problems. Discrete Applied Mathematics 138: 289-301

    Article  MathSciNet  Google Scholar 

  4. Coello C (1999) A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques. Knowl Inf. Syst 1 (3): 129-156

    Google Scholar 

  5. Coello C (2002) Theoretical and Numerical Constraint-Handling Techniques used with Evolutionary Algorithms: A Survey of the State of the Art. Computer Methods in Applied Mech and Engnng 8 (2): 1245-1287

    Article  Google Scholar 

  6. Coello C. http://www.cs.cinvestav.mx/EVOCINV/download/tutorial-moea.pdf

  7. Coit DW, Smith AE (1996) Reliability Optimization of Series-Parallel systems using a Genetic Algorithm. IEEE Trans Reliab 45 (2): 254-260

    Article  Google Scholar 

  8. Coit DW, Tongdan J; Wattanapongsakorn N (2004) System optimization with component reliability estimation uncertainty: a multicriteria approach. IEEE Trans on Reliab 53 (3): 369-380

    Article  Google Scholar 

  9. Constantinides (1994) Basic Reliability. In: Annual Reliability and Maintain-ability Symposium, Anaheim, California, USA

    Google Scholar 

  10. Corne DW, Knowles JD (2000) The Pareto-Envelope based Selection Algorithm for Multiobjective Optimization. In: Proceedings of the Sixth International Conference on Parallel Problem Solving from Nature (PPSN VI). Springer, Berlin: 839-848

    Google Scholar 

  11. Corne DW, Jerram NR, Knowles JD, Oates MJ (2001) PESA-II: Region-based Selection in Evo-lutionary Multiobjective Optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), Morgan Kaufmann Publishers: 283-290.

    Google Scholar 

  12. Davenport AJ, Beck JC (Unpublished manuscript) A Survey of Techniques for Scheduling with Uncertainty. In: http://www.mie.utoronto.ca/staff/profiles/beck/publications.html

  13. Deb K, Gupta H (2005) Searching for Robust Pareto-Optimal Solutions in Multi-objective Optimization. Evolutionary Multi-Criterion Optimization, LNCS 3410. Berlin, Germany: Springer-Verlag: 150-164

    Google Scholar 

  14. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A Fast and Elitist MultiOb- jective Genetic Algorithm: NSGA-II. IEEE Trans Evol. Comput. 6 (2): 182-197

    Article  Google Scholar 

  15. Dhingra A (1992) Optimal Apportionment of Reliability & Redundancy in Series Systems Under Multiple Objectives. IEEE Trans Reliab 41 (4): 576-582

    Article  MATH  Google Scholar 

  16. Elegbede C, Adjallah K (2003) Availability allocation to repairable systems with genetic algorithms: a multi-objective formulation. Reliab Engnng Sys Safety 82 (3): 319-330

    Article  Google Scholar 

  17. Ferson S, Long T. Deconvolution can reduce Uncertainty in Risk Analysis. http://ramas.com

  18. Giuggioli P, Marseguerra M, Zio E (2001) Multiobjective optimization by genetic algorithms: application to safety systems. Reliab Engnng Sys Safety 72: 59-74

    Article  Google Scholar 

  19. Granger M, Henrion M (1993) Uncertainty: A Guide to Dealing with Uncer-tainty in Quantitative Risk and Policy Analysis. Cambridge University Press, UK

    Google Scholar 

  20. Golberg D (1989) Genetics algorithms in search, optimization & machine learn-ing. Addison-Wesley Publishing Company, Inc. USA

    Google Scholar 

  21. Hadjihassan S, Walter E, Pronzato L (1996) Quality Improvement via Optimisa- tion of Tolerance Intervals During the Design Stage. In: Kearfott RB, Kreinovich V (Eds.) Applications of Interval Computations. Kluwer Academic Publishers, Dordrecht, The Netherlands

    Google Scholar 

  22. Hansen E (1992) Global Optimization Using Interval Analysis. Marcel Dekker, Inc., New York

    MATH  Google Scholar 

  23. Hendrix EMT, Mecking CJ, Hendriks ThHB (1996) Finding Robust Solutions for Product Design Problems. EJOR 92: 28-36

    Article  MATH  Google Scholar 

  24. Hikita M, Nakagawa Y, Nakashima K, Narihisa H (1992) Reliability Opti- mization of Systems by a Surrogate-Constraints Algorithm. IEEE Trans Reliab 41: 473-480

    Article  MATH  Google Scholar 

  25. Horn J, Nafpliotis N, (1993) Multiobjective optimization using the Niched Pareto Genetic Algorithm. IlliGAL Report 93005, Illinois Genetic Algorithms Laboratory, University of Illinois, USA

    Google Scholar 

  26. Jin Y, Branke J (2005) Evolutionary Optimization in Uncertain Environments - A survey. IEEE trans Evol Comput. 9 (3): 303-317

    Article  Google Scholar 

  27. Kim JH, Yum BJ (1993) A Heuristic Method for Solving Redundancy Opti- mization Problems in Complex Systems IEEE Trans Reliab 42: 572-578

    MATH  Google Scholar 

  28. Knowles JD Corne DW (2000) M-PAES: A Memetic Algorithm for Multiob-jective Optimization. In: Proc. of the Congress on Evolutionary Computation (CEC00). IEEE Press, Piscataway, NJ: 325-332

    Google Scholar 

  29. Kulturel-Konak, S, Smith AE, Coit DW (2003) Efficiently solving the redun-dancy allocation problem using tabu search. IIE Trans 35 (6): 515-26

    Article  Google Scholar 

  30. Kuo W, Hwang CL, Tillman FA (1978) A note on Heuristic Methods in Optimal System Reliability. IEEE Trans Reliab 27: 320-324

    Article  MATH  Google Scholar 

  31. Kuo W, Lin H, Xu Z, Zhang W (1987). Reliability Optimization with the La-grange multiplier and branch-and-bound technique. IEEE Trans Reliab 36: 1090-1095

    Article  Google Scholar 

  32. Kursawe F (1992) Towards Self-Adapting Evolution Strategies. In: Tzeng G, Yu P (Eds.) Proc. of the Tenth International Conference on Multiple Criteria Decision Making, Taipei

    Google Scholar 

  33. Kursawe F (1993) Evolution Strategies- Simple “Models” of Natural Process?. Revue Internationale de Systemique 7 (5)

    Google Scholar 

  34. Kouvelis P, Yu G, (1997) Robust Discrete Optimization and Its Applications. Non Convex Optimization and Its Applications. Kluwer Academis Publishers.

    Google Scholar 

  35. Lin HH, Kuo W (1987) A Comparison of Heuristic Reliability Optimization Methods. In: Proc. of the World Productivity Forum & 1987 Int’l Industrial Engineering Conf. Institute of Industrial Engineering: 583-589

    Google Scholar 

  36. Martorell S, Carlos S, Villanueva JF, Snchez AI, Galvn B, Salazar D, Cepin M (2006) Use of Multiple Objective Evolutionary Algorithms in Optimizing Surveillance Requirements. Reliab Engnng Sys Safety 91: 1027-1038

    Article  Google Scholar 

  37. Martorell S, Snchez A, Carlos S, Serradell V (2004) Alternatives and challenges in optimizing industrial safety using genetic algorithms. Reliab Engnng Sys Safety 86 (1): 25-38

    Article  Google Scholar 

  38. Medina M, Carrasquero N, Moreno J (1998) Estrategias Evolutivas Celulares para la Optimización de Funciones. In: IBERAMIA’98, 6 Congreso Iberoamer-icano de Inteligencia Artificial. Lisboa, Portugal

    Google Scholar 

  39. Michalewicz Z (1992) Genetic Algorithms + Data Structure = Evolution Pro-grams, Springer-Verlag

    Google Scholar 

  40. Milanese M, Norton J, J. Piet-Lahanier J (Eds.) (1998) Bounding Approaches to System Identification. Plenum Press, New York, USA

    Google Scholar 

  41. Misra KB, Ljubojevic MD (1973) Optimal Reliability Design of a System: A new look. IEEE Trans Reliab 22: 255-258

    Article  Google Scholar 

  42. Moore R (1979) Methods and Applications of Interval Analysis. SIAM Studies in Applied Mathematics. Philadelphia, USA

    Google Scholar 

  43. Nahas N, Nourelfath M (2005) Ant system for reliability optimization of a series system with multiple-choice and budget constraints. Reliab Engnng Sys Safety 87 (1): 1-12

    Article  Google Scholar 

  44. Nakagawa Y, Nakashima K (1997) A heuristic method for determining optimal reliability allocation. IEEE Trans Reliab 26: 156-161

    Article  Google Scholar 

  45. Nakashima K, Yamato K (1997) Optimal Design of a Series-Parallel System with time-dependent reliability. IEEE Trans Reliab 26: 119-120

    Article  Google Scholar 

  46. Neumaier A (1990) Interval Methods for Systems of Equations. Cambridge Uni-versity Press, UK.

    MATH  Google Scholar 

  47. Peng-Sheng Y, Ta-Cheng Ch (2005) An efficient heuristic for series-parallel re-dundant reliability problems. Computers & Operations Research 32 (8): 2117-2127

    Article  MATH  Google Scholar 

  48. Ramirez-Marquez JE, Coit DW, Konak A (2004) A Redundancy allocation for series-parallel systems using a max-min approach. IIE Transactions 36 (9): 891-898

    Article  Google Scholar 

  49. Ramírez-Rosado IJ,Bernal-Agustín JL (2001) Reliability and Costs Optimiza-tion for Distribution Networks Expansion Using an Evolutionary Algorithm. IEEE Trans on Power Systems 16: 111-118

    Article  Google Scholar 

  50. Ratschek H, Rokne J. (1984) Computer Methods for the range of functions. Ellis Horwood Limited, UK

    MATH  Google Scholar 

  51. Ravi V, Murty BSN, Reddy PJ, (1997) Nonequilibrium Simulated Annealing Al-gorithm Applied to Reliability Optimization of Complex Systems. IEEE Trans. Reliab 46: 233-239.

    Article  Google Scholar 

  52. Rocco C (2005) A Hybrid Approach based on Evolutionary Strategies and In-terval Arithmetic to perform Robust Designs. In: Applications of Evolutionary Computing, Lecture Notes in Computer Science LNCS 3449: 623-628, Springer-Verlag

    Google Scholar 

  53. Rocco CM, Miller AJ, Moreno JA, Carrasquero N (2000) Reliability Optimi- sation of Complex Systems. In: The Annual Reliability and Maintainability Symposium. Los Angeles, USA

    Google Scholar 

  54. Rocco C, Moreno JA, Carrasquero N (2003) Robust Design using a Hybrid-Cellular-Evolutionary and Interval-Arithmetic Approach: A Reliability Application. In: Tarantola S, Saltelli A (Eds.) Special Issue: SAMO 2001: Methodological advances and useful applications of sensitivity analysis. Reliab Engnng Sys Safety 79 (2): 149-159

    Google Scholar 

  55. Rosenhead (1989) Rational analysis for a problematic world. Wiley, New York

    Google Scholar 

  56. Roy, B. (1998) A missing link in operational research decision aiding: robustness analysis. Foundations of Computing and Decision Sciences 23 (3): 141-160

    MATH  Google Scholar 

  57. Roy B. (2002) Robustesse de quoi, vis-à-vis de quoi, mais aussi robustesse pourquoi en aide à la décision? In Newsletters of the European Working Group “Multicriteria Aid for Decisions” 6 (3): 1-6

    Google Scholar 

  58. Sakawa M (1978) Multiobjective reliability and redundancy optimization of a series-parallel system by the Surrogate Worth Trade-off method. Microelectron-ics and Reliability 17 (4): 465-467

    Article  MathSciNet  Google Scholar 

  59. Salazar D, Rocco C, Galvàn B (2006) Optimization of Constrained Multiple-Objective Reliability Problems using Evolutionary Algorithms. Reliab Engnng Sys Safety 91: 1057-1070

    Article  Google Scholar 

  60. Salazar DE, Martorell SS, Galvn BJ (2005) Analysis of Representation Alterna-tives for a Multi-ple-Objective Floating Bound Scheduling Problem of a Nuclear Power Plant Safety System. In: Evolutionary and Deterministic Methods for Design, Optimisation and Control with Applications to Industrial and Societal Problems (Eurogen 2005). September 12-14. Munich, Germany

    Google Scholar 

  61. Saltellia, Scott M (1997) Guest editorial: The role of sensitivity analysis in the corroboration of models and its link to model structural and parametric uncertainty. Reliab Enginng Syst Safety 57: 1-4

    Article  Google Scholar 

  62. Schaffer JD (1984) Multiple Optimization with Vector Evaluated Genetic Algo-rithms. Ph. D. Thesis. Vanderbilt University. (Unpublished)

    Google Scholar 

  63. Schwefel HP, Back Th (1995) Evolution Strategies I: Variants and their compu- tational implementation. In: Periaux J, Winter G (Eds.) Genetic Algorithm in Engineering and Computer Science. John Wiley & Sons

    Google Scholar 

  64. Shelokar PS, Jayaraman VK, Kulkarni BD (2002) Ant algorithm for single and multiobjective reliability optimization problems. Quality and Reliability Engi-neering International 18 (6): 497-514

    Article  Google Scholar 

  65. Srense K (2003) A famework for robust and flexible optimisation using meta-heuristics with applications in supply chain design. PhD Thesis. University of Antwerp, Belgium

    Google Scholar 

  66. Sevaux M, Srensen K (2004) Robustness Analysis: Optimisation. In Newsletter of the European Working Group “Multiple Criteria Decision Aiding” 3 (10): 3-5

    Google Scholar 

  67. Srinivas N, Deb K (1994) Multiobjective optimization Using Nondominated Sorting in Genetic Algorithms. Evol Comput 2 (3): 221-248

    Article  Google Scholar 

  68. Tillman FA, Hwang CL, Kuo W (1985) Optimization of System Reliability. Marcel Dekker

    Google Scholar 

  69. Tsutsui S, Gosh A, Fujimoto Y (1996) A robust solution searching scheme in genetic search. In Parallel Problem Solving from Nature. Berlin, Germany: Springer-Verlag: 543-552

    Google Scholar 

  70. Tsutsui S, Gosh A (1997) Genetic algorithms with a robust solutions searching scheme. IEEE Trans. Evol. Comput. 1 (3): 201-208

    Article  Google Scholar 

  71. ı J (1998) Análisi i disseny de controladors robustos. Ph.D. Thesis, Univer-sitat de Girona

    Google Scholar 

  72. Vincke P (2003) About Robustness Analysis. In: Newsletter of the European Working Group “Multicriteria Aid for Decisions” 3 (8): 7-9

    Google Scholar 

  73. Vincke, P (1999) Robust solutions and methods in decision aid. Journal of Mul- ticriteria Decision Analysis 8: 181-187

    Article  MATH  Google Scholar 

  74. Wolfram (1984) Cellular automata as models of complexity. Nature 3H

    Google Scholar 

  75. Wong YY, Jong WK (2004) Multi-level redundancy optimization in series sys- tems. Computers & Industrial Engineering 46 (2): 337-346

    Article  Google Scholar 

  76. Yun-Chia Liang, Smith, AE (2004) An ant colony optimization algorithm for the redundancy allocation problem (RAP). IEEE Trans Reliab 53 (3): 417-423

    Article  Google Scholar 

  77. Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a compara- tive case study and the strength Pareto approach. IEEE Trans Evol Comput 3 (4): 257-271

    Article  Google Scholar 

  78. Zitzler E, Laummans M, Thiele L (2001) SPEA2: Improving the Stregth Pareto Evolutionary Algorithm. TIK Report No. 103. Swiss Federal Institute of Tech-nology (ETH). Computer Engineering and Networks Laboratory (TIK)

    Google Scholar 

  79. Zitzler E, Laumanns M, Bleuler S (2004) A Tutorial on Evolutionary Multiobjec- tive Optimization. In: Workshop on Multiple Objective Metaheuristics (MOMH 2002). Springer-Verlag. Berlin, Germany

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Facultad de Ingeniería, Universidad Central de Venezuela, Caracas 1041A, Apartado Postal, 47937, Los Chaguaramos, Venezuela

    Claudio M. Rocco S.

  2. Instituto de Sistemas Inteligentesy Aplicaciones Numéricas en Ingeniería, Universidad de Las Palmas de Gran Canaria Edif. Central del Parque Científico y Tecnológico, 2 planta, Campus de Tafira Baja, 35017, Las Palmas, Spain

    Daniel E. Salazar A.

Authors
  1. Claudio M. Rocco S.
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel E. Salazar A.
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Leicester, University Road, LE1 7RH, Leiceste, UK

    Shengxiang Yang

  2. School of Computer Engineering, Nanyang Technological University, Blk N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew-Soon Ong

  3. Honda Research Institute Europe, Carl-Legien-Str 30, 63073, Offenbach am Main, Germany

    Yaochu Jin

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Rocco S., C.M., Salazar A., D.E. (2007). A Hybrid Approach Based on Evolutionary Strategies and Interval Arithmetic to Perform Robust Designs. In: Yang, S., Ong, YS., Jin, Y. (eds) Evolutionary Computation in Dynamic and Uncertain Environments. Studies in Computational Intelligence, vol 51. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-49774-5_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-49774-5_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-49772-1

  • Online ISBN: 978-3-540-49774-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation