The paper attempts to determine an optimum structure of a directional measurement and control network intended for investigating horizontal displacements. For this purpose it uses the notion of entropy as a logarithmical measure of probability of the state of a particular observation system. An optimum number of observations results from the difference of the entropy of the vector of parameters X X ˆ H ' corresponding to one extra observation. An increment of entropy interpreted as an increment of the amount of information about the state of the system determines the adoption or rejection of another extra observation to be carried out.
For the optimal location of an additional surplus measurements in the design of redundant measurements system, from data reconciliation point of view, of thermal processes, an information entropy has been applied. The relative entropy - Kullback-Leibler divergence, has been used. As a criterion of the optimal location of an additional surplus measurements in a system of measurements data, the minimum of the entropy information of reconciled measurements data has been assumed. Hence, the objective function in the described optimization task is maximum of the relative entropy - Kullback-Leibler divergence concerning sets of raw and reconciled measurements data. Simulation calculation with application of data reconciliation algorithm and Monte Carlo method concerning the influence of installation of the additional surplus measurements on decrease of entropy information of measurements after data validation have been carried out. The example calculations concerned the cross high-pressure heat regeneration system with cascade flow of condensate installed in 153 MW power unit equipped with cooler of steam are presented. Calculations for all variants of configurations of an additional surplus measurements in the analyzed thermal system have been done. Usefulness of the proposed Kullback-Leibler divergence as a objective function has been demonstrated.
In order to overcome the shortcomings of the dolphin algorithm, which is prone to falling into local optimum and premature convergence, an improved dolphin swarm algorithm, based on the standard dolphin algorithm, was proposed. As a measure of uncertainty, information entropy was used to measure the search stage in the dolphin swarm algorithm. Adaptive step size parameters and dynamic balance factors were introduced to correlate the search step size with the number of iterations and fitness, and to perform adaptive adjustment of the algorithm. Simulation experiments show that, comparing with the basic algorithm and other algorithms, the improved dolphin swarm algorithm is feasible and effective.