A New Approach to Improve Particle Swarm Optimization
Particle swarm optimization (PSO) is a new evolutionary computation technique. Although PSO algorithm possesses many attractive properties, the methods of selecting inertia weight need to be further investigated. Under this consideration, the inertia weight employing random number uniformly distributed in [0,1] was introduced to improve the performance of PSO algorithm in this work. Three benchmark functions were used to test the new method. The results were presented to show that the new method is effective.
KeywordsParticle Swarm Optimization Particle Swarm Optimization Algorithm Inertia Weight Benchmark Function Global Exploration
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- 1.J. Kennedy and R. C. Eberhart. Particle swarm optimization. Proc. IEEE Int. Conf. on Neural Networks (1995) 1942–1948Google Scholar
- 2.R. C. Eberhart and J. Kennedy. A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science. Nagoya, Japan (1995) 39–43Google Scholar
- 3.R. C. Eberhart, Simpson, P. K., and Dobbins, R. W. Computational Intelligence PC Tools. Boston, MA: Academic Press Professional (1996)Google Scholar
- 4.M. M. Millonas. Swarm, phase transition, and collective intelligence. In C.G. Langton, Eds., Artificial life III. Addison Wesley, MA (1994)Google Scholar
- 6.Y. Shi and R. Eberhart. A modified particle swarm optimizer. IEEE Int. Conf. on Evolutionary Computation (1997) 303–308Google Scholar
- 7.M. Clerc. The swarm and queen: towards a deterministic and adaptive particle swarm optimization. Proc. Congress on Evolutionary Computation, Washington, DC,. Piscataway, NJ: IEEE Service Center (1999) 1951–1957Google Scholar
- 8.R. C. Eberhart and Y. Shi. Comparing Inertia weight and constriction factors in particle swarm optimization. In Proc. 2000 Congr. Evolutionary Computation, San Diego, CA (2000) 84–88Google Scholar
- 9.H. Yoshida, K. Kawata, Y. Fukuyama, and Y. Nakanishi. A particle swarm optimization for reactive power and voltage control considering voltage stability. In G. L. Torres and A. P. Alves da Silva, Eds., Proc. Int. Conf. on Intelligent System Application to Power Systems, Rio de Janeiro, Brazil (1999) 117–121Google Scholar
- 11.Y. Shi and R. Eberhart. Parameter selection in particle swarm optimization. Proc. 7th Annual Conf. on Evolutionary Programming (1998) 591–600Google Scholar
- 12.Y. Shi, and Eberhart, R. Experimental study of particle swarm optimization. Proc. SCI2000 Conference, Orlando, FL (2000)Google Scholar
- 16.P. J. Angeline. Using selection to improve particle swarm optimization. IEEE International Conference on Evolutionary Computation, Anchor age, Alaska, May (1998) 4–9Google Scholar
- 17.J. Kennedy, R.C. Eberhart, and Y. Shi. Swarm Intelligence, San Francisco: Morgan Kaufmann Publishers (2001)Google Scholar