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Artificial Life and Robotics

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01-03-2016 | Original Article

Set-based particle swarm optimization with status memory for knapsack problem

Authors: Takahiro Hino, Sota Ito, Tao Liu, Michiharu Maeda

Published in: Artificial Life and Robotics | Issue 1/2016

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Abstract

Set-based particle swarm optimization (S-PSO) operates on discrete space. S-PSO can solve combinatorial optimization problem with high quality and is successful to apply to the large-scale problem. In S-PSO, a velocity is a set with possibility and a position is a candidate solution. In this paper, we present a novel algorithm of set-based particle swarm optimization with status memory (S-PSOSM) to decide the position based on the previous position for solving knapsack problem. Some operators are redefined for S-PSOSM. S-PSOSM is a simple algorithm because the state of probability reduces. In addition, the weight of S-PSOSM is discussed. S-PSOSM shows high qualities in experimental results.

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Title: Set-based particle swarm optimization with status memory for knapsack problem
Authors: Takahiro Hino
Sota Ito
Tao Liu
Michiharu Maeda
Publication date: 01-03-2016
Publisher: Springer Japan
Published in: Artificial Life and Robotics / Issue 1/2016
Print ISSN: 1433-5298
Electronic ISSN: 1614-7456
DOI: https://doi.org/10.1007/s10015-015-0253-6

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