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

Boxin Guan,Yuhai Zhao,Yuan Li

doi:10.3390/e21080766

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.

Highlights

The constraint satisfaction problem (CSP) is an assignment that consists of a set of variables that satisfy some constraints [1,2,3,4]
The generated test cases were represented by four components < n, m, p1, p2 >, where n is the number of variables, m is the domain for each variable, p1 is the connectivity of the constraint graph, and p2 is the tightness of the constraints
The ant colony optimization based on information entropy (ACOE) algorithm was proposed to deal with the problem

Summary

Introduction

The constraint satisfaction problem (CSP) is an assignment that consists of a set of variables that satisfy some constraints [1,2,3,4]. CSP can be solved by assigning specific values to variables in accordance with the constraint conditions [5,6,7,8]. To solve the CSP, complete methods based on the backtracking mechanism [17,18] explore all possible solutions until they find a feasible solution or prove the non-existence of any solution at all. These complete methods are often integrated with filtering technologies, which are effective in the reduction of the domains. The completeness appears to be an ideal property, it is difficult to solve high complex CSPs

Methods

Results

Conclusion