An Ant-Based Algorithm to Solve Distributed Constraint Optimization Problems
Authors: Ziyu Chen, Tengfei Wu, Yanchen Deng, Cheng Zhang
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluation indicates that ACO DCOP is able to find solutions of equal or significantly higher quality than state-of-the-art DCOP algorithms. We empirically evaluate our proposed method with peer algorithms including DSA (p = 0.8), MGM2, DSAN, DGibbs, GDBA and the ant solver for DCSP (ACO DCSP for short) on two type of problems: random DCOPs and weighted graph coloring problems. |
| Researcher Affiliation | Academia | Ziyu Chen,1 Tengfei Wu,2 Yanchen Deng,3 Cheng Zhang1 Chongqing University 1 {chenziyu,bootan}@cqu.edu.cn, 2 wutengfei0404@163.com, 3 dyc941126@126.com |
| Pseudocode | Yes | Algorithm 1: Ant Colony Optimization |
| Open Source Code | No | The paper does not provide any specific link or explicit statement regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | For random DCOPs, we set agent number to 70, domain size to 10 and uniformly select cost from [1, 100]. Each pair of agents is constrained independently with a probability p1 = 0.1 (for the sparse configuration) or p1 = 0.6 (for the dense configuration). We consider weighted graph coloring problems with 120 agents, 3 available colors for each agent and p1 = 0.05. The cost for each violation is uniformly selected from range 1 to 100. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, counts, or predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or processor types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | For random DCOPs, we set agent number to 70, domain size to 10 and uniformly select cost from [1, 100]. Each pair of agents is constrained independently with a probability p1 = 0.1 (for the sparse configuration) or p1 = 0.6 (for the dense configuration). For ant-based algorithms, we consider the number of ants K = 13 for the sparse random DCOPs, K = 20 for the dense random DCOPs and weighted graph coloring problems, set τ0 = 3 for ACO DCOP and τ0 = 10 for ACO DCSP. Moreover, we implement ACO DCSP as a pipelining version and set α = 2, β = 8, ρ = 0.02. |