Improved Euclidean Particle Swarm and Application
Authors: Hongbing Zhu, Chengdong Pu, Jinguang Gu
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Particle swarm optimization (PSO) is a swarm intelligence algorithm, has been successfully applied to
many engineering optimization problems and shown its high search speed in these applications. However, as the
dimension and the number of local optima of optimization problems increase, PSO and most existing improved PSO
algorithms such as, the standard particle swarm optimization (SPSO) and the Gaussian particle swarm optimization
(GPSO), are easily trapped in local optima. In this paper we proposed a novel algorithm based on SPSO called
Euclidean particle swarm optimization (EPSO) which has greatly improved the ability of escaping from local optima.
To confirm the effectiveness of EPSO, we have employed five benchmark functions to examine it, and compared it with
SPSO and GPSO. The experiments results showed that EPSO is significantly better than SPSO and GPSO, especially
obvious in higher-dimension problems. As one of tis application, we applied EPSO to the structure prediction of toy
model both on artificial and real protein sequences. Predicting the structure of protein through its sequence of amino
acids is a complex and challenging problem in computational biology. Though toy model is one of the simplest and
effective models, it is still extremely difficult to predict its structure as the increase of amino acids. The experimental
results demonstrated that EPSO was efficient in protein structure prediction problem in toy model.