Local Optima Avoidable Particle Swarm Optimization
S. M. A. Salehizadeh, Peyman Yadmellat, Mohammad Bagher Menhaj · 2009
This paper proposes a local optima avoidable particle swarm optimization (LOAPSO) which remarkably outperforms the standard PSO in the sense that it can avoid entrapment in local optimum. Three benchmark functions are used to validate the proposed algorithm and compare its performance with that of the other algorithms known as hybrid PSOs and six functions reported in SIS2005 are used to better verification of the proposed algorithm. Numerical results indicate that LOAPSO is considerably competitive due to its ability to avoid being trapped in local optima and to find the functions' global optimum as well as better convergence performance.