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Using differential evolution strategies in chemical reaction optimization for global numerical optimization

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Abstract

In this paper we propose a new hybrid metaheuristic approach which combines Chemical Reaction Optimization and Differential Evolution to solve global numerical optimization problems. Chemical Reaction Optimization is widely used in several optimization problems. However, due to its random behavior in searching the optimal solution, it may converge slowly. Differential Evolution is another efficient method based on differentiation operation which can be achieved by several, more or less selective, research strategies. The aim of this paper is to propose new hybrid algorithms that use Differential Evolution strategies inside Chemical Reaction Optimization process in order to overcome its limits by increasing optimal quality and accelerating convergence. We propose in this paper two new hybrid algorithms. Both of them use the Differential Evolution Best Strategy as a local search operator to improve the exploitation process and the Differential Evolution Random Strategy as a global search operator to maintain the diversity of the population and improve the exploration process. However, the two proposed algorithms slightly differ on the used local search operators. Based on 23 benchmark functions classified in 3 categories, experimental studies start by showing that our second proposed algorithm is better than the first one. Then, this second algorithm is compared with numerous other existing algorithms. First, the experimental results of comparison with the original algorithms show that our algorithm attains very good performance for (1) the quality of the obtained solutions, where it outperforms the other algorithms by achieving the first average and overall rank for two over the three categories; (2) for the robustness where it obtains the best average number of successful runs (21.47 over 25 runs) as well as for (3) convergence speed where our proposed algorithm converges faster comparing with other algorithms in nine over the twenty three functions and finds better solution for functions where other algorithms converge faster. In addition, the proposed algorithm has also been compared with other hybrid chemical reaction and differential evolution based algorithms, the experimental results show that globally the proposed algorithm also outperforms the other hybrid algorithms except for some limited cases.

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Notes

  1. B u f f e r, I n i t i a l K E and K E L o s s R a t e are used in H-CRO-DE1 but not in H-CRO-DE2.

  2. http://ist.csu.edu.cn/YongWang.htm.

References

  1. Alatas B, Akin E, Karci A (2008) MODENAR: Multi-Objective differential evolution algorithm for mining numeric association rules. Appl Soft Comput 8(1):646–656

    Article  Google Scholar 

  2. Babu BV, Angira R (2006) Modified differential evolution (MDE) for optimization of non-linear chemical processes. Comput Chem Eng 30(6):989–1002

    Article  MATH  Google Scholar 

  3. Babu BV, Munawar SA (2007) Differential evolution strategies for optimal design of shell-and-tube heat exchangers. Chem Eng Sci 62(14):3720–3739

    Article  Google Scholar 

  4. Brest J, Greiner S, Boskovic B, Mernik M, Zumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evol Comput 10 (6):646–657

    Article  Google Scholar 

  5. Das S, Konar A, Chakraborty UK (2005) Two improved differential evolution schemes for faster global search. In: Proceedings of the 7th annual conference on genetic and evolutionary computation, pp 991–998

    Google Scholar 

  6. Das S, Konar A, Chakraborty UK (2007) Annealed differential evolution. In: Proceedings of 2007 IEEE congress on evolutionary computation, pp 1926–1933

    Chapter  Google Scholar 

  7. Das S, Abraham A, Chakraborty UK, Konar A (2009) Differential evolution using a neighborhood-based mutation operator. IEEE Trans Evol Comput 13(3):526–553

    Article  Google Scholar 

  8. Dorigo M (1992) Optimization, learning and natural algorithms. PhD Thesis, Politecnico di Milano, Italy

  9. Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the 6th international symposium on micro machine and human science, pp 39–43

    Chapter  Google Scholar 

  10. Epitropakis MG, Tasoulis DK, Pavlidis NG, Plagianakos VP, Vrahatis MN (2011) Enhancing differential evolution utilizing proximity-based mutation operators. IEEE Trans Evol Comput 15(1):99–119

    Article  Google Scholar 

  11. Fan HY, Lampinen J (2003) A trigonometric mutation operation to differential evolution. J Glob Optim 27(1):105–129

    Article  MathSciNet  MATH  Google Scholar 

  12. Gao W, Liu S, Huang L (2012) A global best artificial bee colony algorithm for global optimization. J Comput Appl Math 236(11):2741–2753

    Article  MathSciNet  MATH  Google Scholar 

  13. Gao W, Liu S (2012) A modified artificial bee colony algorithm. Comput Oper Res 39(3):687–697

    Article  MATH  Google Scholar 

  14. Goldberg DE (1989) Genetic algorithms in search optimization and machine learning. Addison-Wesley

  15. James JQ, Lam AY, Li VO (2011) Evolutionary artificial neural network based on chemical reaction optimization. In: Proceedings of 2011 IEEE congress on evolutionary computation (CEC), pp 2083–2090

    Google Scholar 

  16. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department

  17. Kim HJ, Lam HS, Kang S (2011) Chemical reaction optimization for task scheduling in grid computing. IEEE Trans Parallel Dist Syst 22(10):1624–1631

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  19. Lam AY, Li VO (2010) Chemical reaction optimization for cognitive radio spectrum allocation. In: Proceedings of global telecommunications conference (GLOBECOM 2010), pp 1–5

    Google Scholar 

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

    Article  Google Scholar 

  21. Lam AY, Xu J, Li VO (2010) Chemical reaction optimization for population transition in peer-to-peer live streaming. In: Proceedings of 2010 IEEE congress on evolutionary computation (CEC), pp 1–8

    Google Scholar 

  22. Lam AY, Li VO, Yu JJ (2012) Real-coded chemical reaction optimization. IEEE Trans Evol Comput 16(3):339–353

    Article  Google Scholar 

  23. Lam AY, Li VO, Xu J (2013) On the convergence of chemical reaction optimization for combinatorial optimization. IEEE Trans Evol Comput 17(5):605–620

    Article  Google Scholar 

  24. Li JQ, Pan QK (2012) Chemical-reaction optimization for flexible job-shop scheduling problems with maintenance activity. Appl Soft Comput 12(9):2896–2912

    Article  Google Scholar 

  25. Li ZY, Li Z, Nguyen TT, Chen SM (2015) Orthogonal chemical reaction optimization algorithm for global numerical optimization problems. Expert Syst Appl 42(6):3242–3252

    Article  Google Scholar 

  26. Li ZY, Nguyen TT, Chen SM, Truong TK (2015) A hybrid algorithm based on particle swarm and chemical reaction optimization for multi-object problems. Appl Soft Comput 35:525–540

    Article  Google Scholar 

  27. Lin C, Qing A, Feng Q (2011) A new differential mutation base generator for differential evolution. J Glob Optim 49(1):69–90

    Article  MathSciNet  MATH  Google Scholar 

  28. Mallipeddi R, Suganthan PN, Pan QK, Tasgetiren MF (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput 11(2):1679–1696

    Article  Google Scholar 

  29. Ngambusabongsopa R, Li Z, Eldesouky E (2015) A Hybrid mutation chemical reaction optimization algorithm for global numerical optimization. Math Probl Eng 2015

  30. Nguyen TT, Li ZY, Zhang SW, Truong TK (2014) A hybrid algorithm based on particle swarm and chemical reaction optimization. Expert Syst Appl 41(5):2134–2143

    Article  Google Scholar 

  31. Pan B, Lam AY, Li VO (2011) Network coding optimization based on chemical reaction optimization. In: Proceedings of global telecommunications conference (GLOBECOM 2011), pp 1–5

    Google Scholar 

  32. Price K, Storn RM, Lampinen JA (2006) Differential evolution: a practical approach to global optimization. Springer

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

    Article  Google Scholar 

  34. Rahmat N, Musirin I (2012) Differential evolution ant colony optimization (DEACO) technique in solving economic load dispatch problem. In: Proceedings of power engineering and optimization conference (PEDCO) Melaka, Malaysia, pp 263–268

    Google Scholar 

  35. Rahnamayan S, Tizhoosh HR, Salama M (2008) Opposition-based differential evolution. IEEE Trans Evol Comput 12(1):64–79

    Article  Google Scholar 

  36. Sahu K, Panigrahi S, Behera H (2013) A novel chemical reaction optimization algorithm for higher order neural network training. J Theor Appl Info Technol 53(3):402–409

    Google Scholar 

  37. Sarkar S, Das S (2010) A hybrid particle swarm with differential evolution operator approach (DEPSO) for linear array synthesis. In: Proceedings of the 1st international conference of swarm, evolutionary, and memetic computing, pp 416–423

    Chapter  Google Scholar 

  38. Storn R, Price K (1995) Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. ICSI Berkeley

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

    Article  MathSciNet  MATH  Google Scholar 

  40. Truong TK, Li K, Xu Y (2013) Chemical reaction optimization with greedy strategy for the 0-1 knapsack problem. Appl Soft Comput 13(4):1774–1780

    Article  Google Scholar 

  41. Wang Y, Cai Z, Zhang Q (2011) Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evol Comput 15(1):55–66

    Article  Google Scholar 

  42. Zhang J, Sanderson AC (2009) JADE: Adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958

    Article  Google Scholar 

  43. Zhang J, Avasarala V, Subbu R (2010) Evolutionary optimization of transition probability matrices for credit decision-making. Eur J Oper Res 200(2):557–567

    Article  MATH  Google Scholar 

Download references

Acknowledgments

The authors would like to notice that the work reported in this paper was supported by the National Natural Science Foundation of China (No. 61672215), the Special Project on the Integration of Industry, Education and Research of Guangdong Province, China (No. 2012A090300003) and the Science and Technology Planning Project of Guangdong Province, China (No. 2013B090700003).

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Authors and Affiliations

  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China

    Mourad Nouioua & Zhiyong Li

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  1. Mourad Nouioua
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  2. Zhiyong Li
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Correspondence to Zhiyong Li.

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Nouioua, M., Li, Z. Using differential evolution strategies in chemical reaction optimization for global numerical optimization. Appl Intell 47, 935–961 (2017). https://doi.org/10.1007/s10489-017-0921-4

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