The algorithm development based on the immune search for solving unclear problems to detect the optical flow with minimal cost

The main aim of the research is the development of effective methods and algorithms based on the hybrid principles functioning of the immune system and evolutionary search to determine a global optimal solution to optimisation problems. Artificial immune algorithms are characterised as diverse ones, extremely reliable and implicitly parallel. The integration of modified evolutionary algorithms and immune algorithms is proposed to be used for the solution of above problem. There is no exact method for the efficient solving unclear optimisation problems within the polynomial time. However, by determining close to optimal solutions within the reasonable time, the hybrid immune algorithm (HIA) is capable to offer multiple solutions, which provide compromise between several goals. Quite few researches have been focused on the optimisation of more than one goal and even fewer used to have distinctly considered diversity of solutions that plays fundamental role in good performance of any evolutionary calculation method.

Process parameters optima in quick-point grinding ceramics based on the intelligent algorithm

This article studied the relationship between surface roughness and surface micro hardness of the hard-brittle materials and the process parameters in quick-point grinding, and then established the prediction model for the surface micro hardness and surface roughness by the back propagation network which was an improved genetic algorithm. Through the experiments of the quick-point grinding of ceramics, surface roughness and surface micro hardness were tested, and reliability of the model was validated thereby. Based on the least square fitting of the experiment value and prediction value, the one-dimensional analytic model for surface roughness and surface micro hardness had been, respectively, developed in terms of grinding speed, grinder work-table feed speed, grinding depth, incline angle, and deflection angle as process parameters. Both the correlation test and experiment verification indicated that the model exhibited a high level of accuracy. The multivariate model of surface roughness and surface micro hardness can be constructed by means of immune algorithms and orthogonal experiment data. With the optimum objective of the minimum surface roughness and maximum surface micro hardness, a set of optimized process parameters was obtained using immune algorithms, and experiment verification proved that the error value was less than 10%.