Memetic quantum evolution algorithm for global optimization

Abstract

Quantum-inspired heuristic search algorithms have attracted considerable research interest in recent years. However, existing quantum simulation methods are still limited on the basis of particle swarm optimizer. This paper explores the principle of memetic computing to develop a novel memetic quantum evolution algorithm for solving global optimization problem. First, we design a quantum theory-based memetic framework to handle multiple evolutionary operators, in which multiple units of different kinds of algorithmic information are harmoniously combined. Second, we propose the memetic evolutionary operator and the quantum evolutionary operator to complete the balance between the global search and the local search. The memetic evolutionary operator emphasizes meme diffusion by the shuffled process to enhance the global search ability. The quantum evolutionary operator utilizes an adaptive selection mechanism for different potential wells to tackle the local search ability. Furthermore, the Newton’s gravity laws-based gravitational center and geometric center as two important components are introduced to improve the diversity of population. These units can be recombined by means of different evolutionary operators that are based on the synergistic coordination between exploitation and exploration. Through extensive experiments on various optimization problems, we demonstrate that the proposed method consistently outperforms 11 state-of-the-art algorithms.

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References

  1. 1.

    Wang X, Yang J, Teng X, Xia W, Jensen R (2007) Feature selection based on rough sets and particle swarm optimization. Pattern Recognit Lett 28(4):459–471

    Article  Google Scholar 

  2. 2.

    Maitra M, Chatterjee A (2008) A hybrid cooperative–comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding. Expert Syst Appl 34(2):1341–1350

    Article  Google Scholar 

  3. 3.

    Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report TR06, Erciyes University

  4. 4.

    Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: Proceedings of world congress on nature and biologically inspired computing. IEEE Publications, USA, pp 210–214

  5. 5.

    Hatamlou A (2014) Heart: a novel optimization algorithm for cluster analysis. Prog Artif Intell 2(2–3):167–173

    Article  Google Scholar 

  6. 6.

    Tang D, Dong S, Jiang Y, Li H, Huang Y (2015) ITGO: invasive tumor growth optimization algorithm. Appl Soft Comput 36:670–698

    Article  Google Scholar 

  7. 7.

    Qin AK, Suganthan PN (2005) Self-adaptive differential evolution algorithm for numerical optimization. IEEE Cong Evolut Comput 2:1785–1791

    Google Scholar 

  8. 8.

    Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Cong Evolut Comput 13(2):398–417

    Article  Google Scholar 

  9. 9.

    Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Cong Evolut Comput 13(5):945–958

    Article  Google Scholar 

  10. 10.

    Zhou S, Sun Z (2005) A new approach belonging to EDAS: quantum-inspired genetic algorithm with only one chromosome. In: International conference on natural computation, vol 3612, pp 141–150

  11. 11.

    Narayanan A, Moore M (1996) Quantum-inspired genetic algorithms. In: Proceedings of IEEE international conference on evolutionary computation. IEEE Press, NJ, pp 61–66

  12. 12.

    Han KH, Kim JH (2002) Quantum-inspired evolutionary algorithm for a class of Combinatorial optimization. IEEE Trans Evolut Comput 6:580–593

    MathSciNet  Article  Google Scholar 

  13. 13.

    Sun J, Feng B, Xu WB (2004) Particle swam optimization with particles having quantum behavior. IEEE Cong Evolut Comput 1:325–331

    Google Scholar 

  14. 14.

    Sun J, Xu WB, Feng B (2004) A global search strategy of quantum-behaved particle swarm optimization. In: IEEE conference on cybernetics and intelligent systems, vol 1, pp 111–116

  15. 15.

    Xi ML, Sun J, Xu WB (2008) An improved quantum-behaved particle swarm optimization algorithm with weighted mean best position. Appl Math Comput 205:751–759

    MATH  Google Scholar 

  16. 16.

    Sun J, Fang W, Palade V, Wu XJ, Xu WB (2011) Quantum-behaved particle swarm optimization with Gaussian distributed local attractor point. Appl Math Comput 218:3763–3775

    MATH  Google Scholar 

  17. 17.

    Chen DB, Wang JT, Zou F, Houb WB, Zhao CX (2012) An improved group search optimizer with operation of quantum-behaved swarm and its application. Appl Soft Comput 12:712–725

    Article  Google Scholar 

  18. 18.

    Huang L, Xi ML, Zhou YH (2010) An improved quantum-behaved particle swarm optimization with random selection of the optimal individual. In: 2010 WASE international conference on information engineering, (ICIE), vol 4, pp 189–193

  19. 19.

    Tang D, Cai Y, Zhao J, Xue Y (2014) A quantum behaved particle swarm optimization with memetic algorithm and memory for continuous non-linear large scale problems. Inf Sci 289:162–189

    Article  Google Scholar 

  20. 20.

    Tang D, Dong S, Cai X, Zhao J (2016) A two stage quantum-behaved particle swarm optimization with skipping search rule and weight to solve continuous optimization problem. Neural Comput Appl 27:2429–2440

    Article  Google Scholar 

  21. 21.

    Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech Concurrent Computation Program Technical Reports 826

  22. 22.

    Neri F, Moscato P, Cotta C (2002) Handbook of memetic algorithms. Springer, Berlin, Heidelberg, pp 157–167

    Google Scholar 

  23. 23.

    Smith J (2007) Coevolving memetic algorithms: a review and progress report. IEEE Trans Syst Man Cybern B Cybern 37(1):6–17

    MathSciNet  Article  Google Scholar 

  24. 24.

    Jadhav DG, Pattnaik SS, Das S (2014) Memetic algorithm with local search as modified swine influenza model-based optimization and its use in ECG filtering. J Optim 1–22

  25. 25.

    Zhou Z, Ong Y-S, Nair P, Keane A, Lum K-Y (2007) Combining global and local surrogate models to accelerate evolutionary optimization. IEEE Trans Syst Man Cybern C Appl Rev 37(1):66–76

    Article  Google Scholar 

  26. 26.

    Iacca G, Neri F, Mininno E, Ong Y-S, Lim M-H (2012) Ockham’s Razor in memetic computing: three stage optimal memetic exploration. Inf Sci 188:17–43

    MathSciNet  Article  Google Scholar 

  27. 27.

    Caraffini F, Neri F, Iacca G, Mol A (2013) Parallel memetic structures. Inf Sci 227:60–82

    MathSciNet  Article  Google Scholar 

  28. 28.

    Caraffini F, Neri F, Picinali L (2014) An analysis on separability for memetic computing automatic design. Inf Sci 265:1–22

    MathSciNet  Article  Google Scholar 

  29. 29.

    Samma H, Lim CP, Saleh JM (2016) A new reinforcement learning-based memetic particle swarm optimizer. Appl Soft Comput 43:276–297

    Article  Google Scholar 

  30. 30.

    Li Y, Jiao L, Li P, Wu B (2014) A hybrid memetic algorithm for global optimization. Neurocomputing 134:132–139

    Article  Google Scholar 

  31. 31.

    Bambha NK, Bhattacharyya S, Teich J, Zitzler E (2004) Systematic integration of parameterized local search into evolutionary algorithms. IEEE Trans Evol Comput 8(2):137–155

    Article  Google Scholar 

  32. 32.

    Wang H, Moon I, Yang S, Wanga D (2012) A memetic particle swarm optimization algorithm for multimodal optimization problems. Inf Sci 197:38–52

    Article  Google Scholar 

  33. 33.

    Sun J, Garibaldi JM, Krasnogor N, Zhang Q (2013) An intelligent multi-restart memetic algorithm for box constrained global optimisation. Evol Comput 21(1):107–147

    Article  Google Scholar 

  34. 34.

    Zhang G, Xing K (2018) Memetic social spider optimization algorithm for scheduling two-stage assembly flowshop in a distributed environment. Comput Ind Eng 125:423–433

    Article  Google Scholar 

  35. 35.

    Žalik KR, Žalik B (2018) Memetic algorithm using node entropy and partition entropy for community detection in networks. Inf Sci 445–446:38–49

    MathSciNet  Article  Google Scholar 

  36. 36.

    Kóczy LT, Földesi P, Tüű-Szabó B (2018) Enhanced discrete bacterial memetic evolutionary algorithm—an efficacious metaheuristic for the traveling salesman optimization. Inf Sci 460–461:389–400

    MathSciNet  Article  Google Scholar 

  37. 37.

    Soleimanpour-moghadam M, Nezamabadi-pour H, Farsangi M (2014) A quantum inspired gravitational search algorithm for numerical function optimization. Inf Sci 267:83–100

    MathSciNet  MATH  Article  Google Scholar 

  38. 38.

    Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 79:2232–2248

    MATH  Article  Google Scholar 

  39. 39.

    Eusuff MM, Lansey KE (2003) Optimization of water distribution network design using the shuffled frog leaping algorithm. J Water Resour Plan Manag 129(3):210–225

    Article  Google Scholar 

  40. 40.

    Liao T, Aydın D, Stützle T (2013) Artificial bee colonies for continuous optimization: experimental analysis and improvements. Swarm Intell 7(4):327–356

    Article  Google Scholar 

  41. 41.

    Veček N, Mernik M, Črepinšek M (2014) A chess rating system for evolutionary algorithms: a new method for the comparison and ranking of evolutionary algorithms. Inf Sci 277:656–679

    MathSciNet  Article  Google Scholar 

  42. 42.

    Tang D, Yang J, Dong S, Liu Z (2016) A Lévy flight-based shuffled frog-leaping algorithm and its applications for continuous optimization problems. Appl Soft Comput 49:641–662

    Article  Google Scholar 

  43. 43.

    Chow CK, Yuen SY (2011) An evolutionary algorithm that makes decision based on the entire previous search history. IEEE Trans Evol Comput 15(6):741–769

    Article  Google Scholar 

  44. 44.

    Chen D, Zou F, Lu R, Wang P (2017) Learning backtracking search optimisation algorithm and its application. Inf Sci 376:71–94

    Article  Google Scholar 

  45. 45.

    Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut Comput 1:3–18

    Article  Google Scholar 

  46. 46.

    Vapnik V (1995) The nature of statistical learning theory. Springer, New York

    Google Scholar 

  47. 47.

    Tanabe R, Fukunaga AS (2014) Improving the search performance of SHADE using linear population size reduction. In: 2014 IEEE congress on evolutionary computation (CEC), pp 1658–1665

  48. 48.

    Awad NH, Ali MZ, Suganthan PN, Reynolds RG (2016) An ensemble sinusoidal parameter adaptation incorporated with L-SHADE for solving CEC2014 benchmark problems. In: IEEE CEC, pp 2958–2965

  49. 49.

    Jiawei Han MK (2006) Data mining: concepts and techniques. Elsevier, New York

    Google Scholar 

  50. 50.

    Das P, Das DK, Dey S (2016) An improved krill herd algorithm with global exploration capability for solving numerical function optimization problems and its application to data clustering. Appl Soft Comput 46:230–245

    Article  Google Scholar 

  51. 51.

    Tang D, Dong S, He L, Jiang Y (2016) Intrusive tumor growth inspired optimization algorithm for data clustering. Neural Comput Appl 27:349–374

    Article  Google Scholar 

  52. 52.

    Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222:175–184

    MathSciNet  Article  Google Scholar 

  53. 53.

    Blake C L, Merz C J. (1998) UCI repository of machine learning databases. University of California, Irvine, Department of Information and Computer Sciences. http://www.ics.uci.edu/mlearn/MLRepository.html

  54. 54.

    Das P, Das DK, Dey S (2018) A modified bee colony optimization (MBCO) and its hybridization with k-means for an application to data clustering. Appl Soft Comput 70:590–603

    Article  Google Scholar 

  55. 55.

    Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  56. 56.

    Liu K, Ng JK, Lee V, Son SH, Stojmenovic I (2016) Cooperative data scheduling in hybrid vehicular ad hoc networks: VANET as a software defined network. IEEE/ACM Trans Netw (TON) 24(3):1759–1773

    Article  Google Scholar 

  57. 57.

    Milner S, Davis C, Zhang H, Llorca J (2012) Nature-inspired self-organization, control, and optimization in heterogeneous wireless networks. IEEE Trans Mob Comput 11(7):1207–1222

    Article  Google Scholar 

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Acknowledgements

This work is supported by the Guangdong Provincial Natural Fund Project (2016A030310300); Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research Center;Guangdong Science and Technology Key Project (2015B010131009); National Natural Science Foundation of China (71401045); the Ministry of Education in China Project of Humanities and Social Sciences (18YJAZH137); the Guangdong Provincial Natural Fund Project (2017A030313394); major scientific research projects of Guangdong (2017WTSCX021); the planning project of the 13th Five-Year in Philosophy and Social Sciences of Guangzhou (2018GZGJ48); and Ministry of Education Science and Technology Development Center (2017A11001).

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Correspondence to Deyu Tang.

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Tang, D., Liu, Z., Zhao, J. et al. Memetic quantum evolution algorithm for global optimization. Neural Comput & Applic 32, 9299–9329 (2020). https://doi.org/10.1007/s00521-019-04439-8

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Keywords

  • Quantum evolution
  • Memetic algorithm
  • Evolutionary computation
  • Gravitational search algorithm