The Research And Application Of Cellular Genetic Algorithms

with the development of technology, people have to face the more and morecomplicated optimization problems constantly arising in science and engineering, to wichcellular genetic algorithm provides an effective solution. It is an algorithms model thatcombins celluar automata with genetic algorithm. In this algorithm, the genetic operate of acertain individuals is restricted within neighborhood, so it slows down the diffusion speed ofthe good individual. On the one hand, the cellular genetic algorithm can offer us an overallexploitation in sloving problems struck into local optimum, thus increase the globalconvergence, and shows great superiority in coping complexe problem. On the other hand, itsexploiation is poor; it has the poor speed of convering due to running genetic operate withinneighborhood. Apart from this, on the CGA study, great attention of current researchers hasbeen paid to the struct of neighborhood of cellular automata, yet the research of simulatingnatural phenomenon is lacking. This paper gives an overall and systematical research andanalysis on cellular genetic algorithms; several improved algorithms have been presented insolving the new engineering problems. The main content and innovation are as follows:（1）Based on the current cellular genetic algorithm, by mean of selection pressure,qualitative analysis is conducted. Addopting a number of function typical optium, CGA iscompared with SGA in the aspects of evaluation process and computation performance. CGAcan matains population diversity better than SGA throughout evolutionary process. A severalconclution is achived on neighborhood structure to the influence of the algorithmperformance.（2）In different way of migration on cellular genetic algorithm with disater, effect ofselection pressure, relevant to the size and period of disasters, is researched. According to theelastic migration strategy, different excellent individual are placed in disater region toimprove population diversity. The experiment results shows that the cellular geneticalgorithms with new migration strategy can improve the optimization accuracy andconvergence rate as well as harbors superiority of exploration and exploitation.（3）According to different mechanisms of evoluational behavior, two improved celluargentic algorithms are presented. One is cellular genetic algorithms with evlutionary rules,which moniative nature.the other one is cellular genetic algorithms with predator and preymechanism, which mimic the predator-prey model from natural ecology, the evolution rule of cellular genetic algorithm is replaced by predator and prey mechanism. These two algorithmscan be improved from exploitation and exploiration resepectly.（4）Two hybrid cellular genetic algorithms are present. They are the hybrid cellulargenetic algorithms with PSO and the hybrid cellular genetic algorithms with polycentric cityand PSO. They include comunication that is adopted by PSO, so except the result is moresatisfactory, convergence speed is also increased. Polycentric stagey can play a role inmaintain population diversity.（5）Considering local fixel deformation, after workpcece position error is determined,optimal model of clamping forces is built. And then, aiming at minimizing the workpceceposition error, a cellular genetic algorithm with disasters is investigated to solve the proposedmodel so that the global optimal clamping forces can be efficiently obtained. This result ofstudy is better than the result of nolinear programming.