A Novel Heuristic Communication Heterogeneous Dual Population Ant Colony Optimization Algorithm | Zendy

Mingle Xu | Zendy; Xiaoming You | Zendy; Sheng Liu | Zendy

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A Novel Heuristic Communication Heterogeneous Dual Population Ant Colony Optimization Algorithm

Author(s) -

Mingle Xu,

Xiaoming You,

Sheng Liu

Publication year - 2017

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2746569

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

To balance the convergence speed and the solution's diversity in the large-scale travel salesman problem (TSP), this paper proposes a new heuristic communication heterogeneous dual population ant colony optimization (HHACO). First, the main characteristics of HHACO are the heuristic communication and the two heterogeneous ant colonies. Heuristic communication, an indirect communication strategy, helps improve the deviation of solution. Heterogeneous ant colonies are beneficial to balance the convergence speed and the diversity of solution, in which one ant colony is in charge of solution's diversity and the another one in charge of convergence speed inspiring from nature evolution with self-adaptive ability. Besides, this paper takes advantage of orthogonal test to discuss the parameters in HHACO algorithm and a better parameters' set is obtained. Then, HHACO algorithm is applied to solve TSP, and meanwhile characteristics of different ant colonies in HHACO are discussed. Finally, HHACO is compared with the other dual colonies algorithms and several classic ant colony optimization algorithms, and results suggest that the HHACO has a better performance in the large-scale problem.

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