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A new physarum-based hybrid optimization algorithm for solving 0/1 knapsack problem
As a typical NP-complete problem, 0/1 Knapsack Problem (KP), has been widely applied in many domains for solving practical problems. Although ant colony optimization (ACO) algorithms can obtain approximate solutions to 0/1 KP, there exist some shortcomings such as the low convergence rate, premature convergence and weak robustness. In order to get rid of the above-mentioned shortcomings, this paper proposes a new kind of Physarum-based hybrid optimization algorithm, denoted as PM-ACO, based on the critical paths reserved by Physarum-inspired mathematical (PM) model. By releasing additional pheromone to items that are on the important pipelines of PM model, PM-ACO algorithms can enhance item pheromone matrix and realize a positive feedback process of updating item pheromone. The experimental results in two different datasets show that PM-ACO algorithms have a stronger robustness and a higher convergence rate compared with traditional ACO algorithms.
History
Title of book
Advances in Swarm and Computational IntelligenceVolume
9141Series
Lecture notes in computer scienceChapter number
26Pagination
238 - 246Publisher
SpringerPlace of publication
Berlin, GermanyPublisher DOI
ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319204710Publication classification
B Book chapter; B1 Book chapterCopyright notice
2015, SpringerExtent
52Editor/Contributor(s)
D Hutchinson, T Kanade, J Kittler, J Kleinberg, F Mattern, J Mitchell, M Naor, C Rangan, B Steffen, D Terozopoulos, D Tygar, G WeikumUsage metrics
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