# A Flexible Particle Swarm Optimization based on Global Best and Global Worst Information

### Emre Çomak

#### Abstract

A reverse direction supported particle swarm optimization (RDS-PSO) method was proposed in this paper. The main idea to create such a method relies that on benefiting from global worst particle in reverse direction. It offers avoiding from local optimal solutions and providing diversity thanks to its flexible velocity update equation. Various experimental studies have been done in order to evaluate the effect of variable inertia weight parameter on RDS-PSO by using of Rosenbrock, Rastrigin, Griewangk and Ackley test functions. Experimental results showed that RDS-PSO, executed with increasing inertia weight, offered relatively better performance than RDS-PSO with decreasing one. RDS-PSO executed with increasing inertia weight produced remarkable improvements except on Rastrigin function when it is compared with original PSO.

#### References

- Kennedy, J., Eberhart, R.C., 1995. Particle swarm optimization. In Proceedings of the IEEE Conference on Neural Networks. Australia, p. 1942-1948.
- Du, W.L., Li, B., 2008. Multi-strategy ensemble particle swarm optimization for dynamic optimization. In Information Sciences 178(15), p. 3096-3109.
- Tang, K., Yao, X., 2008. Special Issue on nature inspired problem solving. In Information Sciences 178(15), p. 2983-2984.
- Angeline, P., 1998. Evolutionary optimization versus particle swarm optimization: philosophy and performance difference. In Proceedings of Evolutionary Programming Conference, San Diago USA.
- Suganthan, P.N., 1999. Particle swarm optimizer with neighbourhood operator. In Proceedings of the 1999 Congress of Evolutionary Computation. IEEE Press, volume 3, p. 1958-1962.
- Chen, D.B., Zhao, C.X., 2009. Particle swarm optimization with adaptive population size and its application. In Applied Soft Computing volume 9, p.39-48. Science Direct Press.
- Kennedy, J., Mendes, R., 2002. Population structure and particle swarm performance. In Proceeding Congress of Evolutionary Computation (CEC 2002) volume 2, p.1671-1676.
- Alatas, B., Akin, E., Ozer B., 2009. Chaos embedded particle swarm optimization algorithms. In Chaos, Solitons and Fractals volume 40, p.1715-1734. Science Direct Press.
- Coelho, LdS., 2008. A quantum particle swarm optimizer with chaotic mutation operator. In Chaos, Solitons and Fractals volume 37(5), p.1409-1418. Science Direct Press.
- Shi, Y., Eberhart, R.C., 2001. Fuzzy adaptive particle swarm optimization. In Proceedings of the 2001 Congress on Evolutionary Computation, volume 1, p.101-106.
- Griewangk, A.O., 1981. Generalized descent of global optimization. In Journal of Optimization Theory and Applications, volume 34, p.11-39.
- Rastrigin, L.A., 1974. External control systems. In Theoretical Foundations of Engineering Cybernetics Series, Moscow, Russian, Nauka.
- De Jong, K., 1975. An analysis of the behaviour of a class of genetic adaptive systems. PhD thesis in University of Michigan.
- D. H. Ackley., 1987. A connectionist machine for genetic hillclimbing. Boston: Kluwer Academic Publishers.

#### Paper Citation

#### in Harvard Style

Çomak E. (2013). **A Flexible Particle Swarm Optimization based on Global Best and Global Worst Information** . In *Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,* ISBN 978-989-8565-41-9, pages 255-262. DOI: 10.5220/0004187602550262

#### in Bibtex Style

@conference{icpram13,

author={Emre Çomak},

title={A Flexible Particle Swarm Optimization based on Global Best and Global Worst Information},

booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},

year={2013},

pages={255-262},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0004187602550262},

isbn={978-989-8565-41-9},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,

TI - A Flexible Particle Swarm Optimization based on Global Best and Global Worst Information

SN - 978-989-8565-41-9

AU - Çomak E.

PY - 2013

SP - 255

EP - 262

DO - 10.5220/0004187602550262