Traditional optimization algorithm is widely used solving nonlinear equations numerical solution problem ,it is not only slow convergence speed but also easy to fall into local optimal solution and solution low precision. Adaptive membrane computing optimization algorithm is important achievement performance improvement, Firstly, the high-dimensional space split,each subspace is a basic membrane, evolutionary strategy algorithm based on basic membrane area is used to improve the local search ability and convergence speed. Basic membrane area will be local optimum timing is transmitted to the surface membrane. Particle swarm optimization (PSO) has global search ability is used surface membrane area. through simulation the paper can comparatively analyze the performance of different algorithms.
[1]
Gheorghe Paun,et al.
Computing with Membranes
,
2000,
J. Comput. Syst. Sci..
[2]
Zhu Xiao Lin.
Numerical Analysis
,
2014
.
[3]
Lisa M. Brown,et al.
A survey of image registration techniques
,
1992,
CSUR.
[4]
J. Douglas Faires,et al.
Numerical Analysis
,
1981
.
[5]
Nadia Busi,et al.
Using well-structured transition systems to decide divergence for catalytic P systems
,
2007,
Theor. Comput. Sci..
[6]
Zbigniew Michalewicz,et al.
Evolutionary algorithms
,
1997,
Emerging Evolutionary Algorithms for Antennas and Wireless Communications.
[7]
Gheorghe Paun.
Computing with Membranes (P Systems): A Variant
,
2000,
Int. J. Found. Comput. Sci..