Smart Structures and Systems

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A new swarm intelligent optimization algorithm: Pigeon Colony Algorithm (PCA)

  • Yi, Ting-Hua (School of Civil Engineering, Dalian University of Technology) ;
  • Wen, Kai-Fang (School of Civil Engineering, Dalian University of Technology) ;
  • Li, Hong-Nan (School of Civil Engineering, Dalian University of Technology)
  • Received : 2015.10.15
  • Accepted : 2016.03.05
  • Published : 2016.09.25
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Abstract

In this paper, a new Pigeon Colony Algorithm (PCA) based on the features of a pigeon colony flying is proposed for solving global numerical optimization problems. The algorithm mainly consists of the take-off process, flying process and homing process, in which the take-off process is employed to homogenize the initial values and look for the direction of the optimal solution; the flying process is designed to search for the local and global optimum and improve the global worst solution; and the homing process aims to avoid having the algorithm fall into a local optimum. The impact of parameters on the PCA solution quality is investigated in detail. There are low-dimensional functions, high-dimensional functions and systems of nonlinear equations that are used to test the global optimization ability of the PCA. Finally, comparative experiments between the PCA, standard genetic algorithm and particle swarm optimization were performed. The results showed that PCA has the best global convergence, smallest cycle indexes, and strongest stability when solving high-dimensional, multi-peak and complicated problems.

Keywords

Acknowledgement

Supported by : National Natural Science Foundation of China

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