Soft Computing

, Volume 21, Issue 16, pp 4735–4754 | Cite as

Adaptive multi-context cooperatively coevolving particle swarm optimization for large-scale problems

Methodologies and Application
First Online:

Abstract

A novel adaptive multi-context cooperatively coevolving particle swarm optimization (AM-CCPSO) algorithm is proposed in an attempt to improve the performance on solving large-scale optimization problems (LSOP). Due to the curse of dimensionality, most optimization algorithms show their weaknesses on LSOP, and the cooperative co-evolution (CC) is often utilized to overcome such weaknesses. The basic CC framework employs one context vector for cooperatively, but greedily coevolving different subcomponents, which sometimes fails to find global optimum, especially on some complex non-separable LSOP. In the AM-CCPSO, more than one context vectors are employed to provide robust and effective co-evolution. These vectors are selected with respect to each particle of each subcomponent according to their own adaptive probabilities. In the AM-CCPSO, a new PSO updating rule is also proposed to exploit “four best positions” via Gaussian sampling. On a comprehensive set of benchmarks (up to 1000 real-valued variables), as well as on a real world application, the performance of AM-CCPSO can rival several state-of-the-art evolutionary algorithms. Experimental results indicate that the novel adaptive multi-context CC framework is effective to improve the performance of PSO on solving LSOP and can be generally extended in other evolutionary algorithms.

Keywords

Cooperative co-evolution Large-scale optimization  Evolutionary algorithm Particle swarm optimization 
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Notes

Acknowledgments

This work was supported by the NNSF of China under Grants 61201168, the Fundamental Research Fund of Central Universities under Grant 121031.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Ali MM et al (2005) A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems. J Glob Optim 31(4):635–672MathSciNetCrossRefMATHGoogle Scholar
  2. Beheshti Z, Shamsuddin SMH (2014) CAPSO: centripetal accelerated particle swarm optimization. Inform Sci 258:54–79MathSciNetCrossRefGoogle Scholar
  3. Beheshti Z, Shamsuddin SMH, Hasan S (2013) MPSO: median-oriented particle swarm optimization. Appl Math Comput 219(11):5817–5836MathSciNetMATHGoogle Scholar
  4. Benyoucef AS et al (2015) Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions. Appl Soft Comput 32:38–48CrossRefGoogle Scholar
  5. Brest J et al (2007) Performance comparison of self-adaptive and adaptive differential evolution algorithms. Soft Comput 11(7):617–629CrossRefMATHGoogle Scholar
  6. Campos M, Krohling RA, Enriquez I (2014) Bare bones particle swarm optimization with scale matrix adaptation. IEEE Trans Cybern 44(9):1567–1578CrossRefGoogle Scholar
  7. Chuang YC, Chen CT, Hwang C (2015) A real-coded genetic algorithm with a direction-based crossover operator. Inform Sci 305:320–348CrossRefGoogle Scholar
  8. Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73CrossRefGoogle Scholar
  9. Eberhart RC, Shi Y(2000) Comparing inertia weights and constriction factors in particle swarm optimization. Proc 2000 Congr Evol Comput, pp 84–89Google Scholar
  10. Epitropakis MG et al (2011) Enhancing differential evolution utilizing proximity-based mutation operators. IEEE Trans Evol Comput 15(1):99–119CrossRefGoogle Scholar
  11. Fu W et al (2011) Research on engineering analytical model of solar cell. Trans China Electrotech Soc 26(10):211–216Google Scholar
  12. Gagneur J et al (2004) Modular decomposition of protein–protein interaction networks. Genome Biol 5(8):R57.1–R57.12Google Scholar
  13. Ganapathy K et al (2014) Hierarchical particle optimization with ortho-cyclic circles. Expert Syst Appl 41(7):3460–3476CrossRefGoogle Scholar
  14. Ghosh S et al (2012) On convergence of differential evolution over a class of continuous functions with unique global optimum. IEEE Trans Syst Man Cybern Part B Cybern 42(1):107–124CrossRefGoogle Scholar
  15. Guo SM, Yang CC (2015) Enhancing differential evolution utilizing eigenvector-based crossover operator. IEEE Trans Evol Comput 19(1):31–49MathSciNetCrossRefGoogle Scholar
  16. Kennedy J (2003) Bare bones particle swarm. Proc IEEE Swarm Intelligence Symposium, pp 80–87Google Scholar
  17. Kundu R et al (2014) An improved particle swarm optimizer with difference mean based perturbation. Neurocomputing 129:315–333CrossRefGoogle Scholar
  18. Kuo HC, Lin CH (2013) A directed genetic algorithm for global optimization. Appl Math Comput 219(2):7348–7364MathSciNetMATHGoogle Scholar
  19. Liang J et al (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295CrossRefGoogle Scholar
  20. Liu H, Ding GY, Wang B (2014) Bare-bones particle swarm optimization with disruption operator. Appl Math Comput 238:106–122MathSciNetMATHGoogle Scholar
  21. Li XD, Yao X (2009) Tackling high dimensional nonseparable optimization problems by cooperatively coevolving particle swarms. Proc IEEE Congr Evol Comput, pp 1546–1553Google Scholar
  22. Li XD, Yao X (2012) Cooperatively coevolving particle swarms for large-scale optimization. IEEE Trans Evol Comput 16(2):210–224MathSciNetCrossRefGoogle Scholar
  23. Noman N, Iba H (2008) Accelerating differential evolution using an adaptive local search. IEEE Trans Evol Comput 12(1):107–125CrossRefGoogle Scholar
  24. Potter M, Jong KD (1994) A cooperative coevolutionary approach to function optimization. Proc 3rd Conf. Parallel Problem Solving Nat, pp 249–257Google Scholar
  25. Qin AK, Suganthan PN (2005) Self-adaptive differential evolution algorithm for numerical optimization. Proc IEEE Congr Evol Comput, pp 1785–1791Google Scholar
  26. Shi Y, Eberhert R (1999) Empirical study of particle swarm optimization. Proc 1999 IEEE Congr Evol Comput, vol 3, pp 1945–1950Google Scholar
  27. Tang K et al (2007) Benchmark functions for the CEC’2008 special session and competition on large-scale global optimization. Nature Inspired Computat. Applicat. Lab., Univ. Sci. Technol. China, Hefei, China, Tech. Rep. [Online]. Available: http://nical.ustc.edu.cn/cec08ss.php
  28. Tang PH, Tseng MH (2013) Adaptive directed mutation for real-coded genetic algorithms. Appl Soft Comput 13(1):600–614CrossRefGoogle Scholar
  29. Tang RL, Fang YJ (2015) Modification of particle swarm optimization with human simulated property. Neurocomputing 153:319–331CrossRefGoogle Scholar
  30. Van den Bergh F (2002) An analysis of particle swarm optimizers. Ph.D. dissertation, Dept. Comput. Sci., Univ. Pretoria, South AfricaGoogle Scholar
  31. Van den Bergh F, Engelbrecht AP (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evolut Comput 8(3):225–239CrossRefGoogle Scholar
  32. Wang Y, Cai Z, Zhang Q (2011) Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evol Comput 15(1):55–66CrossRefGoogle Scholar
  33. Wang Y, Hu RJ (2014) MPPT algorithm based on particle swarm optimization with hill climing method. Acta Energiae Solaris Sinica 35(1):149–153Google Scholar
  34. Wu Z, Chow T (2013) Neighborhood field for cooperative optimization. Soft Comput 17(5):819–834CrossRefGoogle Scholar
  35. Wu Z, Xia X, Wang B (2015) Improving building energy efficiency by multiobjective neighborhood field optimization. Energy Build 87:45–56CrossRefGoogle Scholar
  36. Yang ZY, Tang K, Yao X (2008a) Multilevel cooperative coevolution for large-scale optimization. Proc IEEE Congr Evol Comput, pp 1663–1670Google Scholar
  37. Yang ZY, Tang K, Yao X (2008b) Large-scale evolutionary optimization using cooperative coevolution. Inform Sci 178(3):2985–2999Google Scholar
  38. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102CrossRefGoogle Scholar
  39. Zhang JQ, Sanderson AC (2009) JADE: Adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958CrossRefGoogle Scholar
  40. Zhang ZH et al (2011) Orthogonal learning particle swarm optimization. IEEE Trans Evol Comput 15(6):832–847CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of AutomationChongqing UniversityChongqingChina
  2. 2.Department of AutomationWuhan UniversityWuhanChina

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