An improved group search optimizer with operation of quantum-behaved swarm and its application

  • Authors:

  • Debao Chen;Jiangtao Wang;Feng Zou;Weibo Hou;Chunxia Zhao

  • Affiliations:

  • The School of Physics and Electronic Information, Huai Bei Normal University, Huaibei 235000, China;The School of Physics and Electronic Information, Huai Bei Normal University, Huaibei 235000, China;The School of Physics and Electronic Information, Huai Bei Normal University, Huaibei 235000, China;The School of Mathematical Sciences, Huai Bei Normal University, Huaibei 235000, China;Computer Institute of NanJing University of Science and Technology, Nanjing 210094, China

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2012

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Abstract

Group search optimizer (GSO) is a novel swarm intelligent (SI) algorithm for continuous optimization problem. The framework of the algorithm is mainly based on the producer-scrounger (PS) model. Comparing with ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms, GSO emphasizes more on imitating searching behavior of animals. In standard GSO algorithm, more than 80% individuals are chosen as scroungers, and the producer is the one and only destination of them. When the producer cannot found a better position than the old one in some successive iterations, the scroungers will almost move to the same place, the group might be trapped into local optima though a small quantity of rangers are used to improve the diversity of it. To improve the convergence performance of GSO, an improved GSO optimizer with quantum-behaved operator for scroungers according to a certain probability is presented in the paper. In the method, the scroungers are divided into two parts, the scroungers in the first part update their positions with the operators of QPSO, and the remainders keep searching for opportunities to join the resources found by the producer. The operators of QPSO are utilized to improve the diversity of population for GSO. The improved GSO algorithm (IGSO) is tested on several benchmark functions and applied to train single multiplicative neuron model. The results of the experiments indicate that IGSO is competitive to some other EAs.