Grammatical Differential Evolution Adaptable Particle Swarm Optimization Algorithm

Tapas Si

Particle Swarm Optimization (PSO) algorithm is population based global optimization algorithm having stochastic nature. PSO has faster convergence but it often gets stuck in local optima due to lacks in diversity. In this work, grammatical differential evolution adaptable PSO algorithm is devised. The main feature of this algorithm is that each particle in PSO uses different velocity update equations evolved by grammatical differential evolution to update its position in the swarm space in order to create diversity in the population. The proposed method is termed as GDE-APSO. GDE-APSO is applied on 8 well-known benchmark unconstrained functions. The obtained results are compared with that of Comprehensive Learning PSO (CLPSO) algorithm. The experimental results show that GDE-APSO algorithm performed better with statistical significance than CLPSO.

Select Volume / Issues:

• Volume 3 Issue 6, Nov. 2012

Year:

2012

Type of Publication:

Article

Keywords:

Grammatical Differential Evolution; Particle Swarm Optimization; CLPSO; Function Optimization

Journal:

IJECCE

Volume:

3

Number:

6

Pages:

1526-1531

Month:

November