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

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.

Magnetotelluric Signal-Noise Separation Using IE-LZC and MP

“…Information entropy is a term describing the degree of uncertainty of the system [ 23 , 24 ], and therefore, it can be used to measure nonlinear and nonstationary MT signals. Lower entropy means less uncertainty of the information, that is, the MT signal with substantial interference in this paper.…”

Parallel m-dimensional relative ant colony optimization (mDRACO) for the Costas-array problem

“…The main deficiency of ant colony algorithms in general is the tendency to prematurely converge on local minima that are globally suboptimal (Deng et al 2019;Guan et al 2019); many successful implementations of ACO achieve performance improvements by adding countermeasures against early convergence. To this end, many ACO implementations are augmented with an additional non-ACO optimization step, such as Local Search (LS) (Guan et al 2021(Guan et al , 2019Thiruvady et al 2016).…”

“…The main deficiency of ant colony algorithms in general is the tendency to prematurely converge on local minima that are globally suboptimal (Deng et al 2019;Guan et al 2019); many successful implementations of ACO achieve performance improvements by adding countermeasures against early convergence. To this end, many ACO implementations are augmented with an additional non-ACO optimization step, such as Local Search (LS) (Guan et al 2021(Guan et al , 2019Thiruvady et al 2016). Performing LS on the solutions found by an ant colony can both improve convergence time by examining promising regions of the space more systematically and prevent early stagnation by sufficiently perturbing solutions that have reached a local minimum.…”

Parallel m-Dimensional Relative Ant Colony Optimization (mDRACO) For the Costas-Array Problem

“…The main deficiency of ant colony algorithms in general is the tendency to prematurely converge on local minima that are globally suboptimal [7,13]; many successful implementations of ACO achieve performance improvements by adding countermeasures against early convergence. To this end, many ACO implementations are augmented with an additional non-ACO optimization step, such as Local Search (LS) [13,14,20]. Performing LS on the solutions found by an ant colony can both improve convergence time by examining promising regions of the space more systematically and prevent early stagnation by sufficiently perturbing solutions that have reached a local minimum.…”