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A novel two-stage hybrid swarm intelligence optimization algorithm and application

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Abstract

This paper presents a novel two-stage hybrid swarm intelligence optimization algorithm called GA–PSO–ACO algorithm that combines the evolution ideas of the genetic algorithms, particle swarm optimization and ant colony optimization based on the compensation for solving the traveling salesman problem. In the proposed hybrid algorithm, the whole process is divided into two stages. In the first stage, we make use of the randomicity, rapidity and wholeness of the genetic algorithms and particle swarm optimization to obtain a series of sub-optimal solutions (rough searching) to adjust the initial allocation of pheromone in the ACO. In the second stage, we make use of these advantages of the parallel, positive feedback and high accuracy of solution to implement solving of whole problem (detailed searching). To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems from TSPLIB are tested to demonstrate the potential of the proposed two-stage hybrid swarm intelligence optimization algorithm. The simulation examples demonstrate that the GA–PSO–ACO algorithm can greatly improve the computing efficiency for solving the TSP and outperforms the Tabu Search, genetic algorithms, particle swarm optimization, ant colony optimization, PS–ACO and other methods in solution quality. And the experimental results demonstrate that convergence is faster and better when the scale of TSP increases.

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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 (61175056, 51175054, 60870009), the Visiting Scholarship of State Key Laboratory of Power Transmission Equipment and System Security and New Technology (Chongqing University) (2007DA10512711406), the Open Project Program of Artificial Intelligence Key Laboratory of Sichuan Province (Sichuan University of Science and Engineering), China (No. 2010RZ004, 2011RYJ03), the National High Technology Research and Development Program of China (863 Program) (2012AA040912), the Open Project Program of Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, (Anhui University), China, the Open Project Program of Key Laboratory of advanced Design and Intelligent Computing (Dalian University), Ministry of Education, China (No. ADIC2010008), and the Open Project Program of Key Laboratory of Numerical Simulation in the Sichuan Provincial College (Neijiang Normal University) China (No. 2011SZFZ001), Young Key Teachers Foundation Projects of Dalian Maritime University (2011QN028, 2012QN031). The program for the initialization, study, training, and simulation of the proposed algorithm for the TSP in this article was written with the tool-box of MATLAB 2009 produced by the Math-Works, Inc.

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Authors and Affiliations

  1. Informational Science and Technology Institute, Dalian Maritime University, 116026, Dalian, China

    Wu Deng, Rong Chen, Yaqing Liu & Jinghuan Guo

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

    Wu Deng & Lifeng Yin

  3. Foreign Language Institute, Dalian Jiaotong University, 116028, Dalian, China

    Bing He

  4. Key Laboratory of Intelligent Computing and Signal Processing, Anhui University, Ministry of Education, 230039, Hefei, China

    Wu Deng

  5. State Key Laboratory of Power Transmission Equipment and System Security and New Technology, College of Electrical Engineering, Chongqing University, 400044, Chongqing, China

    Wu Deng

  6. Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, 643000, Zigong, China

    Wu Deng & Yaqing Liu

  7. Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, Ministry of Education, 116622, Dalian, China

    Wu Deng

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  1. Wu Deng
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Correspondence to Wu Deng.

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Deng, W., Chen, R., He, B. et al. A novel two-stage hybrid swarm intelligence optimization algorithm and application. Soft Comput 16, 1707–1722 (2012). https://doi.org/10.1007/s00500-012-0855-z

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