%0 Journal Article
%A LIU Jusheng1
%A HE Jianjia1
%A 2
%A LI Pengfei1
%T Improved particle swarm algorithm based on theory of complex adaptive system
%D 2017
%R 10.3778/j.issn.1002-8331.1608-0018
%J Computer Engineering and Applications
%P 57-63
%V 53
%N 5
%X In order to solve the shortcomings that particle swarm optimization algorithm is easy to fall into local optimum and form early-maturing, this paper proposes a new Dual Adaptive PSO algorithm（DAPSO）which based on the theory of complex adaptive system by introducing the concept of chaos and adaptivity. Firstly, it uses the Logistic equation to create chaotic sequence in the beginning of initializing population. Secondly, it uses nonlinear dynamic adjustment strategy to adjust the particle’s individual learning factor and social learning factor. Thirdly, it uses （0, 1） random uniform distribution to instead of decreasing inertia weight to adjust inertia weight w. Finally, it uses six high-dimensional single mode and multi-modal Benchmark test function to do a simulation and it makes a comparison with PSO, 2PSO and KPSO. The result shows that DAPSO algorithm is more effective than the original particle swarm optimization algorithm in solving the global optimal and it has a better performance on the accuracy and the efficiency than other algorithms.
%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1608-0018