An Effective Hybrid Optimization Algorithm Based on Self-Adaptive Particle Swarm Optimization Algorithm and Artificial Immune Clone Algorithm

  • Authors:

  • Ai-ling Chen;Qiang Guo

  • Affiliations:

  • -;-

  • Venue:
  • ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 07
  • Year:
  • 2008

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Abstract

To balance the exploration and exploitation abilities of particle swarm optimization (PSO), self-adaptive inertia weight factor is introduced in PSO. To improve the ability of each algorithm to escape from a local optimum, a hybrid optimization algorithm (PAHA) based on self-adaptive PSO and artificial immune clone algorithm (AICA) is developed. Simulation results have shown that PAHA is effective and efficient for the optimization problems.