Research Area: Metaheuristic Computing
The chicken swarm optimization algorithm is a new biology optimization algorithm, but its high-dimensional operation usually causes deviation and the iteration time of optimizing is a little long. An improved chicken swarm optimization algorithm is proposed. In the improved algorithm, initial positions are arranged according to chaotic sequence; therefore, the uniformity and ergodicity of population are enhanced. Adaptive inertia weight is introduced to update the rule of hens; thus, the speed of global search and the ability of local search are enhanced. The following coefficient of chicks is changed into random quantity, so the risk of falling into local extremum is avoided. These improvements enhance the search ability in the early stage and the track ability in the late stage of the algorithm. The improved algorithm is applied in the maximum power point tracking control of the photovoltaic system and is compared with other algorithms.
Keywords:
chicken swarm
optimization algorithm
biology
uniformity
ergodicity
population
photovoltaic system
Author(s) Name: Zhongqiang Wu, Danqi Yu, Xiaohua Kang
Journal name: Optimal Control Appications and Methods
Conferrence name:
Publisher name: Wiley
DOI: 10.1002/oca.2394
Volume Information: 39 (2) 1029–1042
