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A cooperative ant colony system and genetic algorithm for TSPs

หน่วยงาน Central Queensland University, Australia

รายละเอียด

ชื่อเรื่อง : A cooperative ant colony system and genetic algorithm for TSPs
นักวิจัย : Dong, Gaifang. , Guo, Wanwu.
คำค้น : LIBRARY OF CONGRESS NEEDED , Applied research. , 890205 Information Processing Services (incl. Data Entry and Capture) , 080108 Neural, Evolutionary and Fuzzy Computation. , 080205 Numerical Computation. , 080201 Analysis of Algorithms and Complexity. , Ant colony optimization -- Ant colony system -- Genetic algorithm -- Traveling salesman problem -- Convergence -- Consistency
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2553
อ้างอิง : http://hdl.cqu.edu.au/10018/55307
ที่มา : Dong, G & Guo, W 2010, 'A cooperative ant colony system and genetic algorithm for TSPs' in Tan, Y., Shi, Y. and Tan K.C. (eds.) Advances in Swarm Intelligence, International Conference on Swarm Intelligence (ICSI 2010), 12-15 June 2010, Beijing, China, Springer-Verlag, Germany, pp. 597-604.
ความเชี่ยวชาญ : -
ความสัมพันธ์ : Advances in Swarm Intelligence, Part I, International Conference on Swarm Intelligence (ICSI 2010), 12-15 June 2010, Beijing, China / Y. Tan, Y. Shi, and K.C. Tan (eds.). Germany : Springer- Verlag, 2010. p. 597-604 8 pages Refereed 0302-9743 9783642134944 , ACQUIRE [electronic resource] : Central Queensland University Institutional Repository.
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

The travelling salesman problem (TSP) is a classic problem of combinatorial optimization and is unlikely to find an efficient algorithm for solving TSPs directly. In the last two decades, ant colony optimization (ACO) has been successfully used to solve TSPs and their associated applicable problems. Despite the success, ACO algorithms have been facing constantly challenges for improving the slow convergence and avoiding stagnation at the local optima. In this paper, we propose a new hybrid algorithm, cooperative ant colony system and genetic algorithm (CoACSGA) to deal with these problems. Unlike the previous studies that regarded GA as a sequential part of the whole searching process and only used the result from GA as the input to the subsequent ACO iteration, this new approach combines both GA and ACS together in a cooperative and concurrent fashion to improve the performance of ACO for solving TSPs. The mutual information exchange between ACS and GA at the end of each iteration ensures the selection of the best solution for the next round, which accelerates the convergence. The cooperative approach also creates a better chance for reaching the global optimal solution because the independent running of GA will maintain a high level of diversity in producing next generation of solutions. Compared with the results of other algorithms, our simulation demonstrates that CoACSGA is superior to other ACO related algorithms in terms of convergence, quality of solution, and consistency of achieving the global optimal solution, particularly for small-size TSPs.

บรรณานุกรม :
Dong, Gaifang. , Guo, Wanwu. . (2553). A cooperative ant colony system and genetic algorithm for TSPs.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Dong, Gaifang. , Guo, Wanwu. . 2553. "A cooperative ant colony system and genetic algorithm for TSPs".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Dong, Gaifang. , Guo, Wanwu. . "A cooperative ant colony system and genetic algorithm for TSPs."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2553. Print.
Dong, Gaifang. , Guo, Wanwu. . A cooperative ant colony system and genetic algorithm for TSPs. กรุงเทพมหานคร : Central Queensland University, Australia; 2553.