Adaptive Inertia Weight Particle Swarm Optimization

  • Zheng Qin
  • Fan Yu
  • Zhewen Shi
  • Yu Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)


Adaptive inertia weight is proposed to rationally balance the global exploration and local exploitation abilities for particle swarm optimization. The resulting algorithm is called adaptive inertia weight particle swarm optimization algorithm (AIW-PSO) where a simple and effective measure, individual search ability (ISA), is defined to indicate whether each particle lacks global exploration or local exploitation abilities in each dimension. A transform function is employed to dynamically calculate the values of inertia weight according to ISA. In each iteration during the run, every particle can choose appropriate inertia weight along every dimension of search space according to its own situation. By this fine strategy of dynamically adjusting inertia weight, the performance of PSO algorithm could be improved. In order to demonstrate the effectiveness of AIW-PSO, comprehensive experiments were conducted on three well-known benchmark functions with 10, 20, and 30 dimensions. AIW-PSO was compared with linearly decreasing inertia weight PSO, fuzzy adaptive inertia weight PSO and random number inertia weight PSO. Experimental results show that AIW-PSO achieves good performance and outperforms other algorithms.


Particle Swarm Optimization Particle Swarm Optimization Algorithm Inertia Weight Benchmark Function Global Exploration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceeding of IEEE International Conference on Neural Networks (ICNN 1995), Australia, vol. 4, pp. 1942–1947. IEEE Computer Society Press, Los Alamitos (1995)Google Scholar
  2. 2.
    van den Bergh, F., Engelbrecht, A.P.: A cooperative approach to particle swarm optimization. IEEE Transactions On Evolutionary Computation 8, 225–239 (2004)CrossRefGoogle Scholar
  3. 3.
    Peram, T., Veeramachaneni, K., Mohan, C.K.: Fitness-distance-ratio based particle swarm optimization. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium, pp. 174–181 (2003)Google Scholar
  4. 4.
    Silva, A., Neves, A., Costa, E.: An empirical comparison of particle swarm and predator prey optimisation. In: O’Neill, M., Sutcliffe, R.F.E., Ryan, C., Eaton, M., Griffith, N.J.L. (eds.) AICS 2002. LNCS (LNAI), vol. 2464, pp. 103–110. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proceedings of the IEEE Conference on Evolutionary Computation, ICEC, Singapore, pp. 69–73 (1998)Google Scholar
  6. 6.
    Zwe-Lee, G.: Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Transactions on Power Systems 18, 1187–1195 (2003)CrossRefGoogle Scholar
  7. 7.
    Zwe-Lee, G.: A particle swarm optimization approach for optimum design of pid controller in avr system. IEEE Transactions on Energy Conversion 19, 384–391 (2004)CrossRefGoogle Scholar
  8. 8.
    Jinho, P., Kiyong, C., Allstot, D.: Parasitic-aware rf circuit design and optimization. IEEE Transactions on Circuits and Systems 51, 1953–1965 (2004)CrossRefGoogle Scholar
  9. 9.
    Baskar, S., Zheng, R.T., Alphones, A., Ngo, N.Q., Suganthan, P.N.: Particle swarm optimization for the design of low-dispersion fiber bragg gratings. IEEE Photonics Technology Letters 17, 615–617 (2005)CrossRefGoogle Scholar
  10. 10.
    Abido, M.A.: Optimal design of power-system stabilizers using particle swarm optimization. IEEE Transactions on Energy Conversion 17, 406–413 (2002)CrossRefGoogle Scholar
  11. 11.
    Angeline, P.J.: Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 601–610. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  12. 12.
    Shi, Y., Eberhart, R.C.: Fuzzy adaptive particle swarm optimization. In: Proceedings of the 2001 Congress on Evolutionary Computation CEC2001, COEX, World Trade Center, 159, Samseong-dong, Gangnam-gu, Seoul, Korea, pp. 101–106. IEEE Press, Los Alamitos (2001)Google Scholar
  13. 13.
    Zhang, L., Yu, H., Hu, S.: A new approach to improve particle swarm optimization. In: Genetic and Evolutionary Computation, pp. 134–139 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zheng Qin
    • 1
    • 2
  • Fan Yu
    • 1
  • Zhewen Shi
    • 1
  • Yu Wang
    • 2
  1. 1.Department of Computer Science and TechnologyXian JiaoTong UniversityXianP.R. China
  2. 2.Department of Computer Science and TechnologyTsinghua UniversityBeijing

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