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An improved genetic algorithm for numerical function optimization

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Abstract

To avoid problems such as premature convergence and falling into a local optimum, this paper proposes an improved real-coded genetic algorithm (RCGA-rdn) to improve the performance in solving numerical function optimization. These problems are mainly caused by the poor search ability of the algorithm and the loss of population diversity. Therefore, to improve the search ability, the algorithm integrates three specially designed operators: ranking group selection (RGS), direction-based crossover (DBX) and normal mutation (NM). In contrast to the traditional strategy framework, RCGA-rdn introduces a new step called the replacement operation, which periodically performs a local initialization operation on the population to increase the population diversity. In this paper, comparisons with several advanced algorithms were performed on 21 complex constrained optimization problems and 10 high-dimensional unconstrained optimization problems to verify the effectiveness of RCGA-rdn. Based on the results, to further verify the feasibility of the algorithm, it was applied to a series of practical engineering optimization problems. The experimental results show that the proposed operations can effectively improve the performance of the algorithm. Compared with the other algorithms, the improved algorithm (RCGA-rdn) has a better search ability, faster convergence speed and can maintain a certain population diversity.

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References

  1. Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution[C]// National Conference on Emerging Trends & Applications in computer science. Wiley-IEEE Press

  2. Fogel DB (1998) Evolutionary computation: the fossil record[M]. Wiley-IEEE

  3. Lenin K, Reddy B, Kalavathi M (2013) Collective animal behavior (CAB) algorithm for solving optimal reactive power dispatch problem[J]. International Electrical Engineering Journal (IEEJ) 4(4):1147–1158

    Google Scholar 

  4. Ali MZ, Awad NH, Suganthan PN et al (2017) An improved class of real-coded genetic algorithms for numerical optimization[J]. Neurocomputing

  5. Elhoseny M, Tharwat A, Hassanien AE (2018) Bezier curve based path planning in a dynamic field using modified genetic algorithm[J]. J Comput Sci 25:339–350

    Article  Google Scholar 

  6. Hussein HA, Demiroglu I, Johnston RL (2018) Application of a parallel genetic algorithm to the global optimization of medium-sized au–Pd sub-nanometre clusters[J]. Eur Phys J B 91(2):34

    Article  Google Scholar 

  7. Chuang YC, Chen CT, Hwang C (2016) A simple and efficient real-coded genetic algorithm for constrained optimization[M]. Elsevier Science Publishers B V

  8. Bi X, Wang C (2018) A niche-elimination operation based NSGA-III algorithm for many-objective optimization[J]. Appl Intell 48(1):118–141

    Article  MathSciNet  Google Scholar 

  9. Wang JQ, Chen ZW, Zhang PL, et al (2018) Research on improvement of real-coded genetic algorithm for solving constrained optimization problems[J]. Control and Decision

  10. Biesinger B, Hu B, Raidl GR (2018) A genetic algorithm in combination with a solution archive for solving the generalized vehicle routing problem with stochastic demands[J]. Transp Sci 52:673–690

    Article  Google Scholar 

  11. Lin HY, Lin CJ, Huang ML (2016) Optimization of printed circuit board component placement using an efficient hybrid genetic algorithm[J]. Appl Intell 45(3):1–16

    Article  Google Scholar 

  12. Chen WH, Wu PH, Lin YL (2018) Performance optimization of thermoelectric generators designed by multi-objective genetic algorithm[J]. Appl Energy 209:211–223

    Article  Google Scholar 

  13. Pathan MV, Patsias S, Tagarielli VL (2018) A real-coded genetic algorithm for optimizing the damping response of composite laminates[J]. Comput Struct 198:51–60

    Article  Google Scholar 

  14. Metawa N, Hassan MK, Elhoseny M (2017) Genetic algorithm based model for optimizing bank lending decisions[J]. Expert Syst Appl 80:75–82

    Article  Google Scholar 

  15. Elhoseny M, Tharwat A, Farouk A, Hassanien AE (2017) K-coverage model based on genetic algorithm to extend WSN lifetime[J]. IEEE Sensors Letters 1:1–4

    Article  Google Scholar 

  16. Yuan XH, Elhoseny M, El-Minir HK et al (2017) A genetic algorithm-based, dynamic clustering method towards improved WSN longevity[J]. J Netw Syst Manag 25(1):1–26

    Article  Google Scholar 

  17. Elhoseny M, Shehab A, Yuan XH (2017) Optimizing robot path in dynamic environments using genetic algorithm and Bezier curve[J]. J Intell Fuzzy Syst 33(4):2305–2316

    Article  MATH  Google Scholar 

  18. Giassi M, Göteman M (2018) Layout design of wave energy parks by a genetic algorithm[J]. Ocean Eng 154:252–261

    Article  Google Scholar 

  19. Lata S, Yadav SL, Sohal A (2017) Comparative study of different selection techniques in genetic algorithm[J]. Int J Eng Sci

  20. Lozano M, Herrera F, Cano JR (2005) Replacement strategies to preserve useful diversity in steady-state genetic algorithms[J]. Inf Sci 178(23):4421–4433 2018, 91(2):34

    Article  Google Scholar 

  21. Rao A, Chow PC, Gélinas S et al (2013) The role of spin in the kinetic control of recombination in organic photovoltaics.[J]. Nature 500(7463):435–439

    Article  Google Scholar 

  22. Sundar S, Singh A (2017) Two grouping-based metaheuristics for clique partitioning problem[J]. Appl Intell 47(2):430–442

    Article  Google Scholar 

  23. Thammano A, Teekeng W (2015) A modified genetic algorithm with fuzzy roulette wheel selection for job-shop scheduling problems[J]. Int J Gen Syst 44(4):499–518

    Article  MathSciNet  MATH  Google Scholar 

  24. Syswerda G (1989) Uniform crossover in genetic algorithms[C]. International Conference on Genetic Algorithms. Morgan Kaufmann Publishers Inc. 2–9

  25. Jones S, Hinde CJ (2007) s. University of Aberdeen, Aberdeen

    Google Scholar 

  26. Eshelman LJ, Schaffer JD (1993) Real-coded genetic algorithms and interval-schemata[J]. Foundations of Genetic Algorithms 2:187–202

    Google Scholar 

  27. Deb K, Agrawal RB (1994) Simulated binary crossover for continuous search space[J]. Complex Syst 9(3):115–148

    MathSciNet  MATH  Google Scholar 

  28. Ramteke M, Ghune N, Trivedi V (2015) Simulated binary jumping gene: a step towards enhancing the performance of real-coded genetic algorithm[J]. Inf Sci 325:429–454

    Article  Google Scholar 

  29. Rodríguez JAM, Alanís FCM (2016) Binocular self-calibration performed via adaptive genetic algorithm based on laser line imaging[J]. J Mod Opt 63(13):1–14

    Article  Google Scholar 

  30. García-Martínez C, Lozano M, Herrera F, Molina D, Sánchez AM (2008) Global and local real-coded genetic algorithms based on parent-centric crossover operators[J]. Eur J Oper Res 185(3):1088–1113

    Article  MATH  Google Scholar 

  31. Deep K, Thakur M (2007) A new crossover operator for real coded genetic algorithms[J]. Appl Math Comput 188(1):895–911

    MathSciNet  MATH  Google Scholar 

  32. Amjady N, Nasiri-Rad H (2009) Nonconvex economic dispatch with AC constraints by a new real coded genetic algorithm[J]. IEEE Trans Power Syst 24(3):1489–1502

    Article  Google Scholar 

  33. Amjady N, Nasiri-Rad H (2010) Solution of nonconvex and nonsmooth economic dispatch by a new adaptive real coded genetic algorithm[J]. Expert Syst Appl 37(7):5239–5245

    Article  Google Scholar 

  34. Kuo HC, Lin CH (2013) A directed genetic algorithm for global optimization[J]. Appl Math Comput 219(14):7348–7364

    MathSciNet  MATH  Google Scholar 

  35. Miettinen K, Marko M et al (2003) Numerical comparison of some penalty-based constraint handling techniques in genetic algorithms[J]. J Glob Optim 27(4):427–446

    Article  MathSciNet  MATH  Google Scholar 

  36. Haghrah A, Mohammadi-Ivatloo B, Seyedmonir S (2015) Real coded genetic algorithm approach with random transfer vectors-based mutation for short-term hydro–thermal scheduling[J]. Generation Transmission & Distribution Iet 9(1):75–89

    Article  Google Scholar 

  37. Khuat TT, Le MH (2016) A genetic algorithm with multi-parent crossover using quaternion representation for numerical function optimization[J]. Appl Intell:1–17

  38. Ersavas C, Karatepe E (2016) Optimum allocation of FACTS devices under load uncertainty based on penalty functions with genetic algorithm[J]. Electr Eng 99(1):1–12

    Google Scholar 

  39. Si C, Shen J, Zou X, et al (2015) A dynamic penalty function for constrained optimization[M]// advances in swarm and computational intelligence. Springer International Publishing, pp 261–272

  40. Ismkhan H (2018) Black box optimization using evolutionary algorithm with novel selection and replacement strategies based on similarity between solutions[J]. Appl Soft Comput 64:260–271

    Article  Google Scholar 

  41. Xie XF, Zhang WJ, Yang ZL (2002) A parents selection strategy fighting premature convergence in floating genetic algorithms[J]. Control and Decision 17(5):625–628

    Google Scholar 

  42. Koumousis VK, Katsaras CP (2006) A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance[J]. IEEE Trans Evol Comput 10(1):19–28

    Article  Google Scholar 

  43. Kalayci CB, Polat O, Gupta SM (2016) A hybrid genetic algorithm for sequence-dependent disassembly line balancing problem[J]. Ann Oper Res 242(2):321–354

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  45. Lin CH (2013) A rough penalty genetic algorithm for constrained optimization[J]. Inf Sci 241(241):119–137

    Article  Google Scholar 

  46. Mctavish T, Restrepo D (2008) Evolving solutions: the genetic algorithm and evolution strategies for finding optimal parameters[J]. Ann N Y Acad Sci 879(1):75–86

    Google Scholar 

  47. Jadrich RB, Lindquist BA, Bollinger JA, et al (2016) Consequences of minimising pair correlations in fluids for dynamics, thermodynamics and structure[J]. Mol Phys (16–17):1–13

  48. Khezerlou AV, Alizadeh S (2014) A new model for discovering process trees from event logs.[J]. Appl Intell 41(3):725–735

  49. Elsayed SM, Sarker RA, Essam DL (2014) A new genetic algorithm for solving optimization problems[J]. Eng Appl Artif Intell 27(C):57–69

    Article  Google Scholar 

  50. Montemurro M, Vincenti A, Vannucci P (2013) The automatic dynamic penalisation method (ADP) for handling constraints with genetic algorithms[J]. Comput Methods Appl Mech Eng 256(256):70–87

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Northeast Agricultural University, Harbin, China

    Yingying Song, Fulin Wang & Xinxin Chen

Authors
  1. Yingying Song
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  2. Fulin Wang
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  3. Xinxin Chen
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Corresponding author

Correspondence to Fulin Wang.

Appendix: List of test functions

Appendix: List of test functions

Table 15 Basic information about the 21 test functions
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Song, Y., Wang, F. & Chen, X. An improved genetic algorithm for numerical function optimization. Appl Intell 49, 1880–1902 (2019). https://doi.org/10.1007/s10489-018-1370-4

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