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A universal optimization strategy for ant colony optimization algorithms based on the Physarum-inspired mathematical model.

journal contribution
posted on 2014-09-01, 00:00 authored by Zili ZhangZili Zhang, C Gao, Y Liu, T Qian
Ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP.

History

Journal

Bioinspiration and Biomimetics

Volume

9

Article number

036006

Pagination

1-14

Location

Bristol, United Kingdom

ISSN

1748-3190

eISSN

1748-3190

Language

eng

Publication classification

C Journal article, C1 Refereed article in a scholarly journal

Copyright notice

2014, Institute of Physics Publishing Ltd.

Issue

3

Publisher

Institute of Physics Publishing Ltd.