Skip to main content
Springer Nature Link
Log in

A novel collaborative optimization algorithm in solving complex optimization problems

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

To overcome the deficiencies of weak local search ability in genetic algorithms (GA) and slow global convergence speed in ant colony optimization (ACO) algorithm in solving complex optimization problems, the chaotic optimization method, multi-population collaborative strategy and adaptive control parameters are introduced into the GA and ACO algorithm to propose a genetic and ant colony adaptive collaborative optimization (MGACACO) algorithm for solving complex optimization problems. The proposed MGACACO algorithm makes use of the exploration capability of GA and stochastic capability of ACO algorithm. In the proposed MGACACO algorithm, the multi-population strategy is used to realize the information exchange and cooperation among the various populations. The chaotic optimization method is used to overcome long search time, avoid falling into the local extremum and improve the search accuracy. The adaptive control parameters is used to make relatively uniform pheromone distribution, effectively solve the contradiction between expanding search and finding optimal solution. The collaborative strategy is used to dynamically balance the global ability and local search ability, and improve the convergence speed. Finally, various scale TSP are selected to verify the effectiveness of the proposed MGACACO algorithm. The experiment results show that the proposed MGACACO algorithm can avoid falling into the local extremum, and takes on better search precision and faster convergence speed.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Akpinar S, Mirac Bayhan G, Baykasoglu A (2013) Hybridizing ant colony optimization via genetic algorithm for mixed-model assembly line balancing problem with sequence dependent setup times between tasks. Appl Soft Comput J 13(1):574–589

    Article  Google Scholar 

  • Ali RN, Mohammad HF, Niaki STA (2015) A hybrid genetic and imperialist competitive algorithm for green vendor managed inventory of multi-item multi-constraint EOQ model under shortage. Appl Soft Comput J 30(5):353–364

  • Blum C, Puchinger J, Raidl GR, Roli A (2011) Hybrid metaheuristics in combinatorial optimization: a survey. Appl Soft Comput 11(6):4135–4151

    Article  MATH  Google Scholar 

  • Chen SM, Chien CY (2011) Parallelized genetic ant colony systems for solving the traveling salesman problem. Expert Syst Appl 38(4):3873–3883

    Article  Google Scholar 

  • Corchado E, Abraham A (2010) Hybrid intelligent algorithms and applications. Inf Sci 180(14):2633–2634

    Article  MathSciNet  Google Scholar 

  • Deng W, Chen R, He B, Liu YQ, Yin LF, Guo JH (2012) A novel two-stage hybrid swarm intelligence optimization algorithm and application. Soft Comput 16(10):1707–1722

    Article  Google Scholar 

  • Dorigo M, Gambardella LM (1997) Ant colonies for the travelling salesman problem. Biosystems 43(2):73–81

    Article  Google Scholar 

  • Duarte A, Laguna M, Marti R (2011) Tabu search for the linear ordering problem with cumulative costs. Comput Optim Appl 48(3):697–715

    Article  MathSciNet  Google Scholar 

  • Escario JB, Jimenez JF, Giron-Sierra JM (2015) Ant colony extended: experiments on the travelling salesman problem. Expert Syst Appl 42(1):390–410

    Article  Google Scholar 

  • Fister I, Perc M, Kamal SM, Fister I (2015) A review of chaos-based firefly algorithms: perspectives and research challenges. Appl Math Comput 252:155–165

  • Garnier S, Gautrais J, Theraulaz G (2007) The biological principles of swarm intelligence. Swarm Intell 1(1):3–31

    Article  Google Scholar 

  • Ghanbari A, Kazemi SMR, Mehmanpazir F, Nakhostin MM (2013) A cooperative ant colony optimization-genetic algorithm approach for construction of energy demand forecasting knowledge-based expert systems. Knowl-Based Syst 39(1):194–206

    Article  Google Scholar 

  • Hamzadayi A, Yildiz G (2013) A simulated annealing algorithm based approach for balancing and sequencing of mixed-model U-lines. Comput Ind Eng 66(4):1070–1084

    Article  Google Scholar 

  • Holland JH (1977) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  • Jing SY (2014) A hybrid genetic algorithm for feature subset selection in rough set theory. Soft Comput 18(7):1373–1382

    Article  Google Scholar 

  • Jovanovic R, Tuba M (2011) An ant colony optimization algorithm with improved pheromone correction strategy for the minimum weight vertex cover problem. Appl Soft Comput 11(8):5360–5366

    Article  Google Scholar 

  • Kromer P, Zelinka I, Snasel V (2014) Behaviour of pseudo-random and chaotic sources of stochasticity in nature-inspired optimization methods. Soft Comput 18(4):619–629

    Article  Google Scholar 

  • Lee ZJ, Su SF, Huang CC, Liu KH (2008) Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment. Appl Soft Comput J 8(1):55–78

    Article  Google Scholar 

  • Li WD, Ong SK, Nee AYC (2002) Hybrid genetic algorithm and simulated annealing approach for the optimization of process plans for prismatic parts. Int J Prod Res 40(8):1899–1922

    Article  MATH  Google Scholar 

  • Li BB, Wang L (2007) A hybrid quantum-inspired genetic algorithm for multiobjective flow shop scheduling. IEEE Trans Syst Man Cybern Part B Cybern 37(3):576–591

    Article  Google Scholar 

  • Mahi M, Baykan ÖK, Kodaz H (2015) A new hybrid method based on particle swarm optimization, ant colony optimization and 3-opt algorithms for travelling salesman problem. Appl Soft Comput J 30(5):484–490

    Article  Google Scholar 

  • Marinakisa Y, Marinaki M (2010) A hybrid multi-swarm particle swarm optimization algorithm for the probabilistic traveling salesman problem. Comput Oper Res 37(3):432–442

    Article  MathSciNet  MATH  Google Scholar 

  • Metaxiotis K, Liagkouras K (2012) Multiobjective evolutionary algorithms for portfolio management: a comprehensive literature review. Expert Syst Appl 39(14):11685–11698

    Article  Google Scholar 

  • Okamoto T, Hirata H (2013) Global optimization using a multipoint type quasi-chaotic optimization method. Appl Soft Comput 13(2):1247–1264

    Article  Google Scholar 

  • Otero FEB, Freitas AA, Johnson CG (2013) A new sequential covering strategy for inducing classification rules with ant colony algorithms. IEEE Trans Evol Comput 17(1):64–76

    Article  Google Scholar 

  • Pan ZQ, Zhang Y, Kwong S (2015) Efficient motion and disparity estimation optimization for low complexity multiview video coding. IEEE Trans Broadcast. doi:10.1109/TBC.2015.2419824

  • Pasti R, Castro LND (2006) A neuro-immune network for solving the travelling salesman problem. In Proceedings of 2006 international joint conference on neural networks, Vancouver, pp 3760–3766

  • Rizk-Allah RM, Zaki EM, El-Sawy AA (2013) Hybridizing ant colony optimization with firefly algorithm for unconstrained optimization problems. Appl Math Comput 224:473–483

  • Robertson BL, Price CJ, Reale M (2013) CARTopt: a random search method for nonsmooth unconstrained optimization. Comput Optim Appl 56(2):291–315

  • Sheikhan M, Mohammadi N (2012) Neural-based electricity load forecasting using hybrid of GA and ACO for feature selection. Neural Comput Appl 21(8):1961–1970

  • Sioud A, Gravel M, Gagné C (2012) A hybrid genetic algorithm for the single machine scheduling problem with sequence-dependent setup times. Comput Oper Res 39(10):2415–2424

  • Tseng LY, Lin YT (2009) A hybrid genetic local search algorithm for the permutation flowshop scheduling problem. Eur J Oper Res 198(1):84–92

    Article  MATH  Google Scholar 

  • Wang XH, Duan HB (2014) A hybrid biogeography-based optimization algorithm for job shop scheduling problem. Comput Ind Eng 73(1):96–114

    Article  Google Scholar 

  • Wei LY, Zhao M (2005) A niche hybrid genetic algorithm for global optimization of continuous multimodal functions. Appl Math Comput (N Y) 160(3):649–661

    Article  MathSciNet  MATH  Google Scholar 

  • Wen Y, Xu H, Yang JD (2011) A heuristic-based hybrid genetic-variable neighborhood search algorithm for task scheduling in heterogeneous multiprocessor system. Inf Sci 181(3):567–581

    Article  Google Scholar 

  • Wen XZ, Shao L, Xue Y, Fang W (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295(1):395–406

    Article  Google Scholar 

  • Xing LN, Rohlfshagen P, Chen YW, Yao X (2011) A hybrid ant colony optimization algorithm for the extended capacitated arc routing problem. IEEE Trans Syst Man Cybern Part B Cybern 41(4):1110–1123

    Article  Google Scholar 

  • Yanass HH (2013) A review of three decades of research on some combinatorial optimization problems. Pesqui Oper 33(1):11–36

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank all the reviewers for their constructive comments. This research was supported by the National Natural Science Foundation of China (U1433124, 51475065), Open Project Program of State Key Laboratory of Mechanical Transmissions (Chongqing University) (SKLMT-KFKT-201416, SKLMT-KFKT-201513), the Natural Science Foundation of Liaoning Province (2015020013), Open Fund of Key Laboratory of Guangxi High Schools for Complex System & Computational Intelligence (15CI06Y), Open Project Program of Guangxi Key laboratory of hybrid computation and IC design analysis (HCIC201507, HCIC201402), Open Project Program of the Traction Power State Key Laboratory of Southwest Jiaotong University (TPL1403), the PAPD fund. The program for the initialization, study, training, and simulation of the proposed algorithm in this article was written with the tool-box of MATLAB 2010b produced by the Math-Works, Inc.

Author information

Authors and Affiliations

  1. Software Institute, Dalian Jiaotong University, Dalian, 116028, China

    Wu Deng, Huimin Zhao, Li Zou, Guangyu Li & Xinhua Yang

  2. Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi University for Nationalities, Nanning, 530006, China

    Wu Deng & Huimin Zhao

  3. The State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, 400044, China

    Wu Deng, Li Zou & Guangyu Li

  4. Traction Power State Key Laboratory of Southwest Jiaotong University, Chengdu, 610031, China

    Wu Deng & Li Zou

  5. Key Laboratory of Guangxi High Schools for Complex System & Computational Intelligence, Guangxi University for Nationalities, Nanning, 530006, China

    Wu Deng & Huimin Zhao

  6. Department of Computer Science and Technology, University of South China, Hengyang, 421001, China

    Daqing Wu

  7. Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Daqing Wu

Authors
  1. Wu Deng
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Huimin Zhao
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Li Zou
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Guangyu Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Xinhua Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Daqing Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Huimin Zhao.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deng, W., Zhao, H., Zou, L. et al. A novel collaborative optimization algorithm in solving complex optimization problems. Soft Comput 21, 4387–4398 (2017). https://doi.org/10.1007/s00500-016-2071-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-016-2071-8

Keywords