Collaborative evolution algorithm based on physiological bi-regulation mechanism
Bokai Xia · 2011
In order to improve the searching effect for the optimization of multi-parameters,a networked collaborative evolution algorithm(NCEA) was presented based on the bi-regulation mechanism of physiological system.Its structure was designed according to the relative physiological system,which included supper monitor level(SML),collaborative modulation level(CML),and searching population level(SPL).The SPL sends collaborative command to CML,according to the value of individuals fitness and distribution density fed back from searching population level.According to the collaborative command,the CML adjusts the crossover and variation probability,and the individuals exchange probability and uniformity of SPL based on the change of performance index and the corresponding law of physiological modulation.SPL is composed of main searching population and supplement searching population.The supplement population can supply excellent individuals for main population to avoid the searching falling into some local peak.In the experiments,two typical nonlinear functions were firstly selected to examine the searching precision and convergence rate of NECA,and then it was applied to the optimizing process of a novel nonlinear optimization intelligent controller.The experimental results show that the NCEA has better convergence rate and searching precision than normal GA and CGA(an improved GA).