Parameter estimation of cutting tool temperature nonlinear model using a novel simplified E. Coli foraging optimization algorithm

In cutting tool temperature experiment, a large number of related data could be available. In order to define the relationship among the experiment data, the nonlinear regressive curve of cutting tool temperature must be constructed based on the data. In this paper, a simplified E. Coli foraging optimization algorithm is proposed with three main operators, which include a tumbling operator, a swimming operator and a tracing operator that at the same time records the optimal position of individual E. Coli and the location of all E. Coli swarm in order to update the locations of swarm. This paper proposes the simplified optimization algorithm for estimating the parameters such a curve to testing the effectiveness of the optimization algorithm. Comparison of simplified E. Coli foraging optimization algorithm results with those of GA and LS methods showed that the improved optimization algorithm is more effective for estimating the parameters of above curve.