Probabilistic opposition-based particle swarm optimization with velocity clamping
| Autor: | Naveed Kazim Khan, Farrukh Shahzad, Sohail Masood |
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| Rok vydání: | 2013 |
| Předmět: |
Mathematical optimization
education.field_of_study Optimization problem Population MathematicsofComputing_NUMERICALANALYSIS Probabilistic logic Swarm behaviour Particle swarm optimization Swarm intelligence Human-Computer Interaction Artificial Intelligence Hardware and Architecture Multi-swarm optimization education Software Information Systems Mathematics Premature convergence |
| Zdroj: | Knowledge and Information Systems. 39:703-737 |
| ISSN: | 0219-3116 0219-1377 |
| DOI: | 10.1007/s10115-013-0624-z |
| Popis: | A probabilistic opposition-based Particle Swarm Optimization algorithm with Velocity Clamping and inertia weights (OvcPSO) is designed for function optimization--to accelerate the convergence speed and to optimize solution's accuracy on standard benchmark functions. In this work, probabilistic opposition-based learning for particles is incorporated with PSO to enhance the convergence rate--it uses velocity clamping and inertia weights to control the position, speed and direction of particles to avoid premature convergence. A comprehensive set of 58 complex benchmark functions including a wide range of dimensions have been used for experimental verification. It is evident from the results that OvcPSO can deal with complex optimization problems effectively and efficiently. A series of experiments have been performed to investigate the influence of population size and dimensions upon the performance of different PSO variants. It also outperforms FDR-PSO, CLPSO, FIPS, CPSO-H and GOPSO on various benchmark functions. Last but not the least, OvcPSO has also been compared with opposition-based differential evolution (ODE); it outperforms ODE on lower swarm population and higher-dimensional functions. |
| Databáze: | OpenAIRE |
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