Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous AbstractEmission Factors of CO(2) and Airborne Pollutants and Toxicological Potency of Biofuels for Airplane Transport: A Preliminary Assessment    Next AbstractFreshness Identification of Oysters Based on Colorimetric Sensor Array Combined with Image Processing and Visible Near-Infrared Spectroscopy »

Entropy (Basel)


Title:An Ant Colony Optimization Based on Information Entropy for Constraint Satisfaction Problems
Author(s):Guan B; Zhao Y; Li Y;
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"
Journal Title:Entropy (Basel)
Year:2019
Volume:20190806
Issue:8
Page Number: -
DOI: 10.3390/e21080766
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"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 26-12-2024