Title: Solving large-scale 0-1 knapsack problem by the social-spider optimisation algorithm

Authors: Guo Zhou; Ruixin Zhao; Yongquan Zhou

Addresses: Department of Science and Technology Teaching, China University of Political Science and Law, 100088, Beijing, China ' College of Information Science and Engineering, Guangxi University for Nationalities, 530006, Nanning, China ' College of Information Science and Engineering, Guangxi University for Nationalities, 530006, Nanning, China

Abstract: This paper uses the social-spider optimisation (SSO) algorithm to solve large-scale 0-1 knapsack problems. The SSO algorithm is based on the simulation of cooperative behaviour of social-spiders. In SSO algorithm, individuals emulate a group of spiders which interact to each other based on the biological laws of the cooperative colony. The algorithm considers two different search agents (spiders): males and females. Depending on gender, each individual is conducted by a set of different evolutionary operators which mimic different cooperative behaviour which are typically found in the colony. The experiment results show that the social-spider optimisation algorithm can be an efficient alternative for large-scale 0-1 knapsack problems.

Keywords: large-scale 0-1 knapsack problems; cooperative behaviour; social-spider optimisation algorithm.

DOI: 10.1504/IJCSM.2018.10016491

International Journal of Computing Science and Mathematics, 2018 Vol.9 No.5, pp.433 - 441

Accepted: 18 Jun 2017
Published online: 28 Sep 2018 *

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