Skip to main content
SpringerLink
Log in

An Inverse Power Generation Mechanism Based Fruit Fly Algorithm for Function Optimization

  • Published:
Journal of Systems Science and Complexity Aims and scope Submit manuscript

Abstract

As a novel population-based optimization algorithm, fruit fly optimization (FFO) algorithm is inspired by the foraging behavior of fruit flies and possesses the advantages of simple search operations and easy implementation. Just like most population-based evolutionary algorithms, the basic FFO also suffers from being trapped in local optima for function optimization due to premature convergence. In this paper, an improved FFO, named IPGS-FFO, is proposed in which two novel strategies are incorporated into the conventional FFO. Specifically, a smell sensitivity parameter together with an inverse power generation mechanism (IPGS) is introduced to enhance local exploitation. Moreover, a dynamic shrinking search radius strategy is incorporated so as to enhance the global exploration over search space by adaptively adjusting the searching area in the problem domain. The statistical performance of FFO, the proposed IPGS-FFO, three state-of-the-art FFO variants, and six metaheuristics are tested on twenty-six well-known unimodal and multimodal benchmark functions with dimension 30, respectively. Experimental results and comparisons show that the proposed IPGS-FFO achieves better performance than three FFO variants and competitive performance against six other meta-heuristics in terms of the solution accuracy and convergence rate.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Liu B, Wang L, Liu Y, et al., A unified framework for population-based metaheuristics, Annals of Operations Research, 2011, 186(1): 231–262.

    Article  MATH  Google Scholar 

  2. Bertsekas D, Nonlinear programming, Journal of the Operational Research Society, 1997, 48(3): 334–334.

    Article  MathSciNet  Google Scholar 

  3. Goldberg D E and Holland J H, Genetic algorithms and machine learning, Machine Learning, 1988, 3(2): 95–99.

    Article  Google Scholar 

  4. Holland J H, Adaptation in Natural and Artificial Systems, MIT Press, New York, 1992.

    Book  Google Scholar 

  5. Eberhart R and Kennedy J, A new optimizer using particle swarm theory, International Symposium on MICRO Machine and Human Science IEEE, Nagoya, 2002, 39–43.

    Google Scholar 

  6. Moscato P and Cotta C, Handbook of Metaheuristics: A Gentle Introduction to Memetic Algorithms, Springer US, USA, 2010, 105–144.

    Google Scholar 

  7. Ong Y S and Keane A J, Meta-lamarckian learning in memetic algorithms, IEEE Transactions on Evolutionary Computation, 2004, 8(2): 99–110.

    Article  Google Scholar 

  8. Chen X, Ong Y S, and Lim M H, A multi-facet survey on memetic computation, IEEE Transactions on Evolutionary Computation, 2011, 15(5): 591–607.

    Article  Google Scholar 

  9. Kennedy J, The particle swarm: Social adaptation of knowledge, IEEE International Conference on Evolutionary Computation, Indianapolis, 2002, 303–308.

    Google Scholar 

  10. Ingber L, Simulated annealing: Practice versus theory, Mathematical and Computer Modelling, 1993, 18(11): 29–57.

    Article  MathSciNet  MATH  Google Scholar 

  11. Storn R and Price K, Differential evolution: A simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization, 1997, 11(4): 341–359.

    Article  MathSciNet  MATH  Google Scholar 

  12. Omran M G H and Mahdavi M, Global-best harmony search, Applied Mathematics & Computation, 2008, 198(2): 643–656.

    Article  MathSciNet  MATH  Google Scholar 

  13. Mirjalili S, Mirjalili S M, and Hatamlou A, Multi-verse optimizer: A nature-inspired algorithm for global optimization, Neural Computing and Applications, 2016, 27(2): 495–513.

    Article  Google Scholar 

  14. Zheng Y J, Water wave optimization: A new nature-inspired metaheuristic, Computers & Operations Research, 2015, 55: 1–11.

    Article  MathSciNet  MATH  Google Scholar 

  15. Pan W T, A new fruit fly optimization algorithm: Taking the financial distress model as an example, Knowledge-Based Systems, 2012, 26: 69–74.

    Google Scholar 

  16. Pan Q K, Sang H Y, Duan J H, et al., An improved fruit fly optimization algorithm for continuous function optimization problems, Knowledge-Based Systems, 2014, 62: 69–83.

    Article  Google Scholar 

  17. Wang L, Zheng X L, and Wang S Y, A novel binary fruit fly optimization algorithm for solving the multidimensional knapsack problem, Knowledge-Based Systems, 2013, 48: 17–23.

    Article  Google Scholar 

  18. Meng T and Pan Q K, An improved fruit fly optimization algorithm for solving the multidimensional knapsack problem, Applied Soft Computing, 2017, 50: 79–93.

    Article  Google Scholar 

  19. Pan W T, Mixed modified fruit fly optimization algorithm with general regression neural network to build oil and gold prices forecasting model, Kybernetes, 2014, 43(7): 1053–1063.

    Article  Google Scholar 

  20. Yuan X, Liu Y, and Xiang Y, Parameter identification of bipt system using chaotic-enhanced fruit fly optimization algorithm, Applied Mathematics and Computation, 2015, 268: 1267–1281.

    Article  MathSciNet  MATH  Google Scholar 

  21. Zheng X L, Wang L, and Wang S Y, A novel fruit fly optimization algorithm for the semiconductor final testing scheduling problem, Knowledge-Based Systems, 2014, 57: 95–103.

    Article  MathSciNet  Google Scholar 

  22. Wang L, Shi Y L, and Liu S, An improved fruit fly optimization algorithm and its application to joint replenishment problems, Expert Systems with Applications, 2015, 42(9): 4310–4323.

    Article  Google Scholar 

  23. Mousavi S M, Alikar N, and Niaki S T A, Optimizing a location allocation-inventory problem in a two-echelon supply chain network: A modified fruit fly optimization algorithm, Computers & Industrial Engineering, 2015, 87: 543–560.

    Article  Google Scholar 

  24. Niu J, Zhong W, and Liang Y, Fruit fly optimization algorithm based on differential evolution and its application on gasification process operation optimization, Knowledge-Based Systems, 2015, 88: 253–263.

    Article  Google Scholar 

  25. Wang W C and Liu X G, Melt index prediction by least squares support vector machines with an adaptive mutation fruit fly optimization algorithm, Chemometrics and Intelligent Laboratory Systems, 2015, 141: 79–87.

    Article  Google Scholar 

  26. Zheng X L and Wang L, A pareto based fruit fly optimization algorithm for task scheduling and resource allocation in cloud computing environment, 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, 2016: 3393–3400.

    Chapter  Google Scholar 

  27. Zheng X L and Wang L, A knowledge-guided fruit fly optimization algorithm for dual resource constrained flexible job-shop scheduling problem, International Journal of Production Research, 2016, 18: 1–13.

    Google Scholar 

  28. Zheng X L and Wang L, A two-stage adaptive fruit fly optimization algorithm for unrelated parallel machine scheduling problem with additional resource constraints, Expert Systems with Applications, 2016, 65: 28–39.

    Article  Google Scholar 

  29. Zheng X L and Wang L, A collaborative multiobjective fruit fly optimization algorithm for the resource constrained unrelated parallel machine green scheduling problem, IEEE Transactions on Systems Man & Cybernetics Systems, 2016, 99: 1–11.

    Google Scholar 

  30. Zhang H, Li B, and Zhang J, Parameter estimation of nonlinear chaotic system by improved TLBO strategy, Soft Computing, 2016, 20(12): 4965–4980.

    Article  Google Scholar 

  31. Pan W T, Using modified fruit fly optimisation algorithm to perform the function test and case studies, Connection Science, 2013, 25(3): 151–160.

    Article  Google Scholar 

  32. Shan D, Cao G H, and Dong H J, LGMS-FOA: An improved fruit fly optimization algorithm for solving optimization problems, Mathematical Problems in Engineering, 2013, 7: 1256–1271.

    MATH  Google Scholar 

  33. Dai H, Zhao G, and Lu J, Comment and improvement on “a new fruit fly optimization algorithm: Taking the financial distress model as an example”, Knowledge-Based Systems, 2014, 59: 159–160.

    Article  Google Scholar 

  34. Yuan X, Dai X, and Zhao J, On a novel multi-swarm fruit fly optimization algorithm and its application, Applied Mathematics and Computation, 2014, 233(3): 260–271.

    Article  MathSciNet  MATH  Google Scholar 

  35. Lin W Y, A novel 3d fruit fly optimization algorithm and its applications in economics, Neural Computing and Applications, 2016, 27(5): 1391–1413.

    Article  Google Scholar 

  36. Xu F Q and Tao Y T, The improvement of fruit fly optimization algorithm: Using bivariable function as example, Advanced Materials Research, 2013, 756(3): 2952–2957.

    Article  Google Scholar 

  37. Mitic M, Najdan V, and Petrovic M, Chaotic fruit fly optimization algorithm, Knowledge-Based Systems, 2105, 89: 446–458.

    Article  Google Scholar 

  38. Wu L H, Zuo C L, and Zhang H Q, A cloud model based fruit fly optimization algorithm, Knowledge-Based Systems, 2015, 89: 603–617.

    Article  Google Scholar 

  39. Babalik A, Iscan H, and Ismail Babaoglu, An improvement in fruit fly optimization algorithm by using sign parameters, Soft Computing, 2017, 2: 1–17.

    Google Scholar 

  40. Wang L and Zheng X L, Advances in fruit fly optimization algorithms, Control Theory and Applications, 2017, 34(5): 557–563 (in Chinese).

    MathSciNet  MATH  Google Scholar 

  41. Li H Z, Guo S, and Li C J, A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm, Knowledge-Based Systems, 2103, 37(2): 378–387.

    Google Scholar 

  42. Yang X S, Firefly algorithms for multimodal optimization, Proceedings of the 5th International Conference on Stochastic Algorithms: Foundations and Applications, Berlin, Springer-Verlag, 2009, 169–178.

    Google Scholar 

  43. Igel C, Hansen N, and Roth S, Covariance matrix adaptation for multi-objective optimization, Evolutionary Computation, 2007, 15(1): 1–28.

    Article  Google Scholar 

  44. Liang J J, Qin A K, and Suganthan P N, Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE Transactions on Evolutionary Computation, 2006, 10(3): 281–295.

    Article  Google Scholar 

  45. Qin A K and Suganthan P N, Self-adaptive differential evolution algorithm for numerical optimization, 2005 IEEE Congress on Evolutionary Computation, IEEE, 2005, 1785–1791.

    Google Scholar 

  46. Deng J and Wang L. A competitive memetic algorithm for multi-objective distributed permutation flow shop scheduling problem, Swarm and Evolutionary Computation, 2017, 32: 121–131.

    Article  Google Scholar 

  47. Zhu Z, Xiao J, He S, et al., A multi-objective memetic algorithm based on locality-sensitive hashing for one-to-many-to-one dynamic pickup-and-delivery problem, Information Sciences, 2016, 329: 73–89.

    Article  Google Scholar 

  48. Gupta A, Ong Y S, and Liang F, Multiobjective multifactorial optimization in evolutionary multitasking, IEEE Transactions on Cybernetics, 2016, 47(7): 1652–1665.

    Article  Google Scholar 

  49. Huang Y, Ding Y, and Hao K, A multi-objective approach to robust optimization over time considering switching cost, Information Sciences, 2017, 394: 183–197.

    Article  Google Scholar 

  50. Zhang Y, Cui G, and Wu J, A novel multi-scale cooperative mutation fruit fly optimization algorithm, Knowledge-Based Systems, 2016, 114: 24–35.

    Article  Google Scholar 

  51. Iscan H and Gunduz M, An application of fruit fly optimization algorithm for traveling salesman problem, Procedia Computer Science, 2017, 111: 58–63.

    Article  Google Scholar 

  52. Chen S F, Qian B, Hu R, et al., An enhanced estimation of distribution algorithm for no-wait job shop scheduling problem with makespan criterion, The 2014 International Conference on Intelligent Computing, Taiyuan, 2014, 675–685.

    Google Scholar 

  53. Zhang Z Q, Qian B, Hu R, et al., Hybrid estimation of distribution algorithm for no-wait flow-shop scheduling problem with sequence-dependent setup times and release dates, The 2016 International Conference on Intelligent Computing, Lanzhou, 2016, 505–516.

    Google Scholar 

  54. Zhao J X, Qian B, Hu R, et al., An improved quantum-inspired evolution algorithm for nowait flow shop scheduling problem to minimize makespan, The 2016 International Conference on Intelligent Computing, Lanzhou, 2016, 536–547.

    Google Scholar 

  55. Chen S F, Qian B, Liu B, et al., A Bayesian statistical inference-based estimation of distribution algorithm for the re-entrant job-shop scheduling problem with sequence-dependent setup times, The 2014 International Conference on Intelligent Computing, Taiyuan, 2014, 686–696.

    Google Scholar 

  56. Qian B, Li Z C, and Hu R, A copula-based hybrid estimation of distribution algorithm for mmachine reentrant permutation flow-shop scheduling problem, Applied Soft Computing, 2017, 61: 921–934.

    Article  Google Scholar 

  57. Qian B, Zhang Q L, Hu R, et al., An effective soft computing technology based on belief-rulebase and particle swarm optimization for tipping paper permeability measurement, Journal of Ambient Intelligence and Humanized Computing, 2017, 15: 1–10.

    Google Scholar 

  58. Pan W T, Zhu W Z, and Ma F X, Modified fruit fly optimization algorithm of logistics storage selection, International Journal of Advanced Manufacturing Technology, 2017, 6: 1–12.

    Google Scholar 

  59. Ismail Babaoglu, Solving 2D strip packing problem using fruit fly optimization algorithm, Procedia Computer Science, 2017, 111: 52–57.

    Article  Google Scholar 

  60. Kanarachos S, Griffin J, and Fitzpatrick M E, Efficient truss optimization using the contrastbased fruit fly optimization algorithm, Computers & Structures, 2017, 182(1): 137–148.

    Article  Google Scholar 

  61. Wang L and Zheng X L, A knowledge-guided multi-objective fruit fly optimization algorithm for the multi-skill resource constrained project scheduling problem, Swarm & Evolutionary Computation, 2017, 38: 54–63.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Management, Wuhan University of Science and Technology, Wuhan, 430065, China

    Ao Liu, Xudong Deng & Liang Ren

  2. Center for Service Science and Engineering, Wuhan University of Science and Technology, Wuhan, 430065, China

    Ao Liu, Xudong Deng & Liang Ren

  3. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, 430065, China

    Ao Liu, Xudong Deng & Liang Ren

  4. School of Economics and Management, Beihang University, Beijing, 100191, China

    Ying Liu

  5. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    Bo Liu

Authors
  1. Ao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xudong Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liang Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ying Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bo Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bo Liu.

Additional information

This research was supported by the National Natural Science Foundation of China under Grant Nos. 71701156, 71390331 and 71690242, the Natural Science Foundation of Hubei Province of China under Grant No. 2017CFB427, Key Research Program of Frontier Sciences for Chinese Academy of Sciences under Grant No. QYZDB-SSW-SYS020, Humanity and Social Science Youth Foundation of Ministry of Education of China under Grant No. 16YJCZH056, Hubei Province Department of Education Humanities and Social Sciences Research Project under Grant No. 17Q034, Open Funding of Center for Service Science and Engineering, Wuhan University of Science and Technology under Grant No. CSSE2017KA01, Open Funding of Intelligent Information Processing and Real-time Industrial System under Grant No. 2016znss18B, Young Incubation Program of Wuhan University of Science and Technology under Grant No. 2016xz017 and 2017xz031.

This paper was recommended for publication by WANG Shouyang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, A., Deng, X., Ren, L. et al. An Inverse Power Generation Mechanism Based Fruit Fly Algorithm for Function Optimization. J Syst Sci Complex 32, 634–656 (2019). https://doi.org/10.1007/s11424-018-7250-5

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11424-018-7250-5

Keywords

Search

Navigation