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An Ant Colony Optimization Based on Information Entropy for Constraint Satisfaction Problems

1
School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
2
School of Information Science and Technology, North China University of Technology, Beijing 100144, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(8), 766; https://doi.org/10.3390/e21080766
Received: 5 July 2019 / Revised: 28 July 2019 / Accepted: 3 August 2019 / Published: 6 August 2019
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PDF [1124 KB, uploaded 6 August 2019]
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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. View Full-Text
Keywords: ant colony optimization; constraint satisfaction problem; information entropy; local search ant colony optimization; constraint satisfaction problem; information entropy; local search
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Guan, B.; Zhao, Y.; Li, Y. An Ant Colony Optimization Based on Information Entropy for Constraint Satisfaction Problems. Entropy 2019, 21, 766.

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