Simple ant colony algorithm for combinatorial optimization problems
- Resource Type
- Conference
- Authors
- Zhang, Zhaojun; Zou, Kuansheng
- Source
- 2017 36th Chinese Control Conference (CCC) Control Conference (CCC), 2017 36th Chinese. :9835-9840 Jul, 2017
- Subject
- Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Algorithm design and analysis
Ant colony optimization
Optimization
Urban areas
Couplings
Traveling salesman problems
Upper bound
Max-Min ant system
Pheromone model
Parameter setting
- Language
- ISSN
- 1934-1768
Ant colony optimization (ACO) as a kind of distributed intelligent bionic optimization algorithm has been widely used to solve a variety of optimization problems, especially combinatorial optimization problems. The model and management mechanism of pheromone are very important to the performance of ACO. The Max-Min ant system (MMAS) is a classical ACO algorithm and has unique characteristics in terms of pheromones management. In this paper, we analyze the advantages and disadvantages of MMAS. Then we propose a novel ACO algorithm called simple ant colony optimization (SACO). In SACO, constant pheromone bounds are used and the update amount and initialization of pheromone are also set a constant. One of benefit is can reduce the coupling of parameters. Then we study the parameters setting about the initial value of pheromone and evaporation rate. The effect of parameters on the algorithm performance is also studied by experimental method based on traveling salesman problems. Finally, the performance of SACO is compared with other novel algorithms based on traveling salesman problems to show the feasibility and effectiveness of improvements.