Title: | An Ant Colony Optimization Based on Information Entropy for Constraint Satisfaction Problems |
Address: | "School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China. School of Information Science and Technology, North China University of Technology, Beijing 100144, China" |
ISSN/ISBN: | 1099-4300 (Electronic) 1099-4300 (Linking) |
Abstract: | "Solving the constraint satisfaction problem (CSP) is to find an assignment of values to variables that satisfies a set of constraints. Ant colony optimization (ACO) is an efficient algorithm for solving CSPs. However, the existing ACO-based algorithms suffer from the constructed assignment with high cost. To improve the solution quality of ACO for solving CSPs, an ant colony optimization based on information entropy (ACOE) is proposed in this paper. The proposed algorithm can automatically call a crossover-based local search according to real-time information entropy. We first describe ACOE for solving CSPs and show how it constructs assignments. Then, we use a ranking-based strategy to update the pheromone, which weights the pheromone according to the rank of these ants. Furthermore, we introduce the crossover-based local search that uses a crossover operation to optimize the current best assignment. Finally, we compare ACOE with seven algorithms on binary CSPs. The experimental results revealed that our method outperformed the other compared algorithms in terms of the cost comparison, data distribution, convergence performance, and hypothesis test" |
Keywords: | ant colony optimization constraint satisfaction problem information entropy local search; |
Notes: | "PubMed-not-MEDLINEGuan, Boxin Zhao, Yuhai Li, Yuan eng 61772124/National Natural Science Foundation Program of China/ 61702381/National Natural Science Foundation Program of China/ 61332014/State Key Program of National Natural Science of China/ 150402002/Fundamental Research Funds for the Central Universities/ 150404008/Fundamental Research Funds for the Central Universities/ Switzerland 2019/08/06 Entropy (Basel). 2019 Aug 6; 21(8):766. doi: 10.3390/e21080766" |