ISSN : 1796-217X
Volume : 3    Issue : 9    Date : December 2008

A Novel Probability Binary Particle Swarm Optimization Algorithm and Its Application
Ling Wang, Xiuting Wang, Jingqi Fu, and Lanlan Zhen
Page(s): 28-35
Full Text:
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Particle swarm optimization (PSO), an intelligent optimization algorithm inspired by the flocking
behavior of birds, has been shown to perform well and widely used to solve the continuous
problem. But the traditional PSO and most of its variants are developed for optimization problems in
continuous space, which are not able to solve the binary combinational optimization problem. To
tackle this problem, Kennedy extended the PSO and proposed a discrete binary PSO. But its
performance is not ideal and just few further works were conducted based on it. In this paper, we
propose a novel probability binary particle swarm optimization (PBPSO) algorithm for discrete binary
optimization problems. In PBPSO, a novel updating strategy is adopted to update the swarm and
search the global solution, which further simplify the computations and improve the optimization
ability. To investigate the performance of the proposed PBPSO, the multidimensional knapsack
problems are used as the test benchmarks. The experimental results demonstrate that PBPSO has
a better performance for solving the multidimensional knapsack problem in terms of convergent
speed and global search ability.

Index Terms
particle swarm optimization, probability optimization algorithm, knapsack problem