A new physarum-based hybrid optimization algorithm for solving 0/1 knapsack problem

Chen, Shi, Gao, Chao and Zhang, Zili 2015, A new physarum-based hybrid optimization algorithm for solving 0/1 knapsack problem. In Hutchinson, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Rangan, C. Pandu, Steffen, Bernhard, Terozopoulos, Demetri, Tygar, Doug and Weikum, Gerhard (ed), Advances in swarm and computational intelligence, Springer, Berlin, Germany, pp.238-246, doi: 10.1007/978-3-319-20472-7_26.

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Title A new physarum-based hybrid optimization algorithm for solving 0/1 knapsack problem
Author(s) Chen, Shi
Gao, Chao
Zhang, ZiliORCID iD for Zhang, Zili orcid.org/0000-0002-8721-9333
Title of book Advances in swarm and computational intelligence
Editor(s) Hutchinson, David
Kanade, Takeo
Kittler, Josef
Kleinberg, Jon M.
Mattern, Friedemann
Mitchell, John C.
Naor, Moni
Rangan, C. Pandu
Steffen, Bernhard
Terozopoulos, Demetri
Tygar, Doug
Weikum, Gerhard
Publication date 2015
Series Lecture notes in computer science 9141
Chapter number 26
Total chapters 52
Start page 238
End page 246
Total pages 9
Publisher Springer
Place of Publication Berlin, Germany
Keyword(s) 0/1 Knapsack
Physarum-inspired model
Summary 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.
ISBN 9783319204710
ISSN 0302-9743
Language eng
DOI 10.1007/978-3-319-20472-7_26
Field of Research 08 Information And Computing Sciences
080503 Networking and Communications
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2015, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082178

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