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Soft Computing

, Volume 21, Issue 15, pp 4387–4398 | Cite as

A novel collaborative optimization algorithm in solving complex optimization problems

  • Wu Deng
  • Huimin Zhao
  • Li Zou
  • Guangyu Li
  • Xinhua Yang
  • Daqing Wu
Methodologies and Application

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.

Keywords

Genetic algorithm Ant colony optimization algorithm  Chaotic optimization method Multi-strategy Collaborative optimization Complex optimization problem 

Notes

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Wu Deng
    • 1
    • 2
    • 3
    • 4
    • 5
  • Huimin Zhao
    • 1
    • 2
    • 5
  • Li Zou
    • 1
    • 3
    • 4
  • Guangyu Li
    • 1
    • 3
  • Xinhua Yang
    • 1
  • Daqing Wu
    • 6
    • 7
  1. 1.Software InstituteDalian Jiaotong UniversityDalianChina
  2. 2.Guangxi Key Laboratory of Hybrid Computation and IC Design AnalysisGuangxi University for NationalitiesNanningChina
  3. 3.The State Key Laboratory of Mechanical TransmissionsChongqing UniversityChongqingChina
  4. 4.Traction Power State Key Laboratory of Southwest Jiaotong UniversityChengduChina
  5. 5.Key Laboratory of Guangxi High Schools for Complex System & Computational IntelligenceGuangxi University for NationalitiesNanningChina
  6. 6.Department of Computer Science and TechnologyUniversity of South ChinaHengyangChina
  7. 7.Nanjing University of Information Science and TechnologyNanjingChina

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