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Brief History and Overview of Intelligent Optimization Algorithms

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Configurable Intelligent Optimization Algorithm

Part of the book series: Springer Series in Advanced Manufacturing ((SSAM))

Abstract

Up to now, intelligent optimization algorithm has been developed for nearly 40 years. It is one of the main research directions in the field of algorithm and artificial intelligence. No matter for complex continuous problems or discrete NP-hard combinatorial optimizations, people nowadays is more likely to find a feasible solution by using such randomized iterative algorithm within a short period of time instead of traditional deterministic algorithms. In this chapter, the basic principle of algorithms, research classifications, and the development trends of intelligent optimization algorithm are elaborated.

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References

  1. Nocedal J, Wright SJ (2006) Numerical optimization. Springer, Berlin

    Google Scholar 

  2. Bonnans JF, Gilbert JC, Lemarechal C, Sagastizabal CA (2006) Numerical optimization: theoretical and practical aspects. Springer, Berlin

    Google Scholar 

  3. Papadimitriou CH, Steiglitz K (1998) Combinatorial optimization: algorithms and complexity. Dover Publications, Mineola

    Google Scholar 

  4. Schrijver A (2003) Combinatorial optimization. Springer, Berlin

    Google Scholar 

  5. Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. J ACM Comput Surv (CSUR) 35(3)68–308

    Google Scholar 

  6. Garey MR, Johnson DS (1990) Computers and intractability: a guide to the theory of NP-completeness. W. H Freeman and Co, San Francisco

    Google Scholar 

  7. Ullman JD (1975) NP-complete scheduling problems. J Comput Syst Sci 10(3):384–393

    Article  MATH  MathSciNet  Google Scholar 

  8. Gawiejnowics S (2008) Time-dependent scheduling. Springer, Berlin

    Google Scholar 

  9. Karp RM (1986) Combinatorics, complexity, and randomness. Commun ACM 29(2):98–109

    Article  MATH  MathSciNet  Google Scholar 

  10. Kann V (1992) On the approximability of NP-complete optimization problems. Royal Institute of Technology, Sweden

    Google Scholar 

  11. Talbi EG (2009) Metaheuristics: from design to implementation. Wiley, New york

    Google Scholar 

  12. Ribeiro CC, Martins SL, Rosseti I (2007) Metaheuristics for optimization problems in computer communications. Comput Commun 30(4):656–669

    Article  Google Scholar 

  13. Liao TW, Egbelu PJ, Sarker BR, Leu SS (2011) Metaheuristics for project and construction management—a state-of-the-art review. Autom Constr 20(5):491–505

    Article  Google Scholar 

  14. Moscato P (1989) On evolution, Search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech Concurrent Computation Program

    Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

  16. Tao F, Zhang L, Zhang ZH, Nee AYC (2010) A quantum multi-agent evolutionary algorithm for selection of partners in a virtual enterprise. CIRP Ann Manufact Technol 59(1):485–488

    Google Scholar 

  17. Shi Y, Eberhart RC (2001) Fuzzy adaptive particle swarm optimization. In: Proceedings of the 2001 congress on evolutionary computation, vol 1, pp 101–106

    Google Scholar 

  18. Horn J, Nafpliotis N, Goldberg DE (1994) A niched pareto genetic algorithm for multiobjective optimization. In: Proceedings of the 1st IEEE congress on evolutionary computation, vol 1, pp 82–87

    Google Scholar 

  19. Wang DW, Yung KL, Lp WH (2001) A heuristic genetic algorithm for subcontractor selection in a global manufacturing environment. IEEE Trans Syst Man Cybern Part C 31(2):189–198

    Article  Google Scholar 

  20. 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 

  21. March JG (1991) Exploration and exploitation in organizational learning. Organ Sci v2(1):71–87

    Google Scholar 

  22. Tsoulos IG (2008) Modifications of real code genetic algorithm for global optimization. Appl Math Comput 203(2):598–607

    Article  MATH  MathSciNet  Google Scholar 

  23. Zhang G, Gao L, Shi Y (2011) An effective genetic algorithm for the flexible job-shop scheduling problem. Expert Syst Appl 38(4):3563–3573

    Article  Google Scholar 

  24. Zhang G (2011) Quantum-inspired evolutionary algorithms: a survey and empirical study. J Heuristics 17(3):303–351

    Article  MATH  Google Scholar 

  25. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Kluwer Academic Publishers, Boston

    MATH  Google Scholar 

  26. Whitley D (1994) A genetic algorithm tutorial. Stat Comput 4(2):65–85

    Article  Google Scholar 

  27. Schmitt LM (2001) Theory of genetic algorithms. Theoret Comput Sci 259(1–2):1–61

    Article  MATH  MathSciNet  Google Scholar 

  28. Wang L, Pan J, Jiao LC (2000) The immune algorithm. ACTA Electronica Sinica 28(7):74–78

    Google Scholar 

  29. Wang L, Pan J, Jiao LC (2000) The immune programming. Chin J Comput 23(8):806–812

    Google Scholar 

  30. de Castro LN, Von Zuben FJ (2002) Learning and optimization using the clonal selection principle. IEEE Trans Evol Comput 6(3):239–251

    Article  Google Scholar 

  31. Hofmeyr SA, Forrest S (2000) Architecture for an artificial immune system. Evol Comput 8(4):443–473

    Article  Google Scholar 

  32. Noutani Y, Andresen B (1998) A comparison of simulated annealing cooling strategies. J Phys A: Math Gen 41(31):8373–8385

    Google Scholar 

  33. Ali MM, Torn A, Viitanen S (2002) A direct search variant of the simulated annealing algorithm for optimization involving continuous variables. Comput Oper Res 29(1):87–102

    Article  MATH  MathSciNet  Google Scholar 

  34. Varanelli JM (1996) On the acceleration of simulated annealing. University of Virginia, USA

    Google Scholar 

  35. Lourenco HR, Martin O, Stutzle T (2003) Iterated local search. Int Ser Oper Res Manag Sci 57:321–353 (Handbook of Metaheuristics. Kluwer Academic Publishers)

    Google Scholar 

  36. Lourenco HR, Martin O, Stutzle T (2010) Iterated local search: framework and applications. Int Ser Oper Res Manag Sci 146:363–397 (Handbook of Metaheuristics, 2nd edn. Kluwer Academic Publishers)

    Google Scholar 

  37. Fanjul-Peyro L, Ruiz R (2010) Iterated greedy local search methods for unrelated parallel machine scheduling. Eur J Oper Res 207(1):55–69

    Article  MATH  MathSciNet  Google Scholar 

  38. Derbel H, Jarboui B, Hanafi S, Chabchoub H (2012) Genetic algorithm with iterated local search for solving a location-routing problem. Expert Syst Appl 39(3):2865–2871

    Article  Google Scholar 

  39. Dorigo M, Maniezzo V, Colorn A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern 26(1):29–42

    Article  Google Scholar 

  40. Dorigo M, Gambardella M (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66

    Article  Google Scholar 

  41. Stutzle T, Hoos HH (2000) MAX-MIN ant system. Future Gener Comput Syst 16(8):889–914

    Article  Google Scholar 

  42. Birattari M, Pellegrini P, Dorigo M (2007) On the invariance of ant colony optimization. IEEE Trans Evol Comput 11(6):732–742

    Article  Google Scholar 

  43. Martens D, De Backer M, Haesen R, Vanthienen J, Snoeck M, Baesens B (2007) Classification with ant colony optimization. IEEE Trans Evol Comput 11(5):651–665

    Article  Google Scholar 

  44. Chatterjee A, Siarry P (2006) Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Comput Oper Res 33(3):859–871

    Article  MATH  Google Scholar 

  45. Clerc M, Kennedy J (2002) The particle swarm-explosion, stability and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(2):58–73

    Article  Google Scholar 

  46. Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: IEEE 2th proceedings of evolutionary computation, pp 1671–1676

    Google Scholar 

  47. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation

    Google Scholar 

  48. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39(3):459–471

    Article  MATH  MathSciNet  Google Scholar 

  49. Platel MD, Schliebs S, Kasabov N (2009) Quantum-inspired evolutionary algorithm: a multimodel EDA. IEEE Trans Evol Comput 13(6):1218–1232

    Article  Google Scholar 

  50. Lam AYS, Li VOK (2010) Chemical-reaction-inspired metaheuristic for optimization. IEEE Trans Evol Comput 14(3):381–399

    Article  Google Scholar 

  51. Wang C, Cheng HZ (2008) Optimization of network configuration in large distribution systems using plant growth simulation algorithm. IEEE Trans Power Syst 23(1):119–126

    Article  Google Scholar 

  52. Daskin A, Kais S (2011) Group leaders optimization algorithm. Mol Pheys 109(5):761–772

    Article  Google Scholar 

  53. Mehrabian AR, Lucas C (2006) A novel numerical optimization algorithm inspired from weed colonization. Ecol Inform 1(4):355–366

    Article  Google Scholar 

  54. Yang XS(2008) Nature-inspired metaheuristic algorithms. Luniver Press

    Google Scholar 

  55. Muhlenbein H, Schomisch M, Born J (1991) The parallel genetic algorithm as function optimizer[J]. Parallel Comput 17(6–7):619–632

    Article  Google Scholar 

  56. Yang HT, Yang PC, Huang CL (1997) A parallel genetic algorithm approach to solving the unit commitment problem: implementation on the transputer networks. IEEE Trans Power Syst 12(2):661–668

    Article  Google Scholar 

  57. Fukuyama Y, Chiang HD (1996) A parallel genetic algorithm for generation expansion planning. IEEE Trans Power Syst 11(2):955–961

    Article  Google Scholar 

  58. Xu DJ, Daley ML (1995) Design of optimal digital-filter using a parallel genetic algorithm. IEEE Trans Circ Syst 42(10):673–675

    Article  Google Scholar 

  59. Matsumura T, Nakamura M, Okech J, Onaga K (1998) A parallel and distributed genetic algorithm on loosely-coupled multiprocessor system. IEICE Trans Fundam Elect Commun Comput Sci 81(4):540–546

    Google Scholar 

  60. Yeung SH, Chan WS, Ng KT, Man KF (2012) Computational optimization algorithms for antennas and RF/microwave circuit designs: an overview. IEEE Trans Industr Inf 8(2):216–227

    Article  Google Scholar 

  61. Tao F, Zhao DM, Hu YF, Zhou ZD (2008) Resource service composition and its optimal-selection based on particle swarm optimization in manufacturing grid system. IEEE Trans Industr Inf 4(4):315–327

    Article  Google Scholar 

  62. Tang KS, Yin RJ, Kwong S, Ng KT, Man KF (2011) A theoretical development and analysis of jumping gene genetic algorithm. IEEE Trans Industr Inf 7(3):408–418

    Article  Google Scholar 

  63. Lo CH, Fung EHK, Wong YK (2009) Intelligent automatic fault detection for actuator failures in aircraft. IEEE Trans Industr Inf 5(1):50–55

    Article  Google Scholar 

  64. Hur SH, Katebi R, Taylor A (2011) Modeling and control of a plastic film manufacturing web process. IEEE Trans Industr Inf 7(2):171–178

    Article  Google Scholar 

  65. Wolpert DH (1997) W G Macready (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  MathSciNet  Google Scholar 

  66. Holland J (1975) Adaptation in natural and artificial systems. The University of Michigan Press

    Google Scholar 

  67. Glover F (1989) Tabu search. ORSA J Comput 1(3):190–206

    Article  MATH  Google Scholar 

  68. Kirkpatrick S, Gelatt CD, Vechi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    Article  MATH  MathSciNet  Google Scholar 

  69. Farmer JD, Packard NH, Perelson AS (1986) The immune system, adaptation, and machine learning. Physica D 22(1–3):187–204

    Article  MathSciNet  Google Scholar 

  70. Dorigo M (1992) Optimization, learning and natural algorithms. Ph.D. Thesis, Politecnico di Milanno

    Google Scholar 

  71. Adleman LM (1994) Molecular computation of solutions to combinatorial problem. Science 266(5187):1021–1024

    Article  Google Scholar 

  72. Reynolds RG (1994) An introduction to cultural algorithms. In: The 3rd annual conference on evolution programming

    Google Scholar 

  73. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: IEEE international conference on neural networks

    Google Scholar 

  74. Linhares A (1998) State-space search strategies gleaned from animal behavior: a traveling salesman experiment. Biol Cybern 87(3):167–173

    Article  Google Scholar 

  75. Li XL (2003) A new intelligent optimization algorithm—artificial fish school algorithm. Ph.D. Thesis, Zhejiang University, China

    Google Scholar 

  76. Yang XS (2010) A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NICSO 2010), Springer, Berlin, p 65–74

    Google Scholar 

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Tao, F., Laili, Y., Zhang, L. (2015). Brief History and Overview of Intelligent Optimization Algorithms. In: Configurable Intelligent Optimization Algorithm. Springer Series in Advanced Manufacturing. Springer, Cham. https://doi.org/10.1007/978-3-319-08840-2_1

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  • DOI: https://doi.org/10.1007/978-3-319-08840-2_1

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-08840-2

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