Automated Search for Locations of Detention Groups to Reduce Security Activity Risk

Property security management is a crucial problem in keeping tangible and intangible assets safe. Its solution using technical means and mobile detention groups ensures assets preservation and reduces security activities risk. Classical definition of a risk, which relates the probability of damage to the protected object to the amount of damage, is proposed to use. For determining the probability, an assumption, where only arrival time of the detention group at secured facility is recorded by the alarm signal, is introduced. In order to minimize the total risk of security activities the task of finding the location of the detention group on the ground with dispersed objects is formulated. As a solution to the formulated problem, a search for the location of the detention groups which takes into account the magnitude of the damage and the current coordinates of the object on the plane is proposed. Examples of calculating locations of detention groups on the plane are presented. An objective function Ф of the effectiveness of security activities implementation, linking the financial and economic indicators of security organization with the value of security activities risk is introduced and substantiated. The analysis of function Φ behavior with a change in the number of detention groups is shown, the presence of function extremum is shown, the interval of finding the extremum is determined. It is proposed to calculate the distances between objects on a map and to use it to determine the coordinates in a new auxiliary plane. Coordinates are calculated using Gram matrices. A computational example is presented. A step-by-step algorithm for secured facility allocation between detention groups with minimization of the total risk of security activities is developed; an example of its use is presented. A search procedure for the location of the detention group on the ground by the determined coordinates on the plane is defined; a search process is illustrated. A general location search algorithm is proposed and the results are presented.

Research of Bio-Inspired Geomagnetic Navigation for AUV Based on Evolutionary Gradient Search

At present, bio-inspired geomagnetic navigation is mostly based on evolutionary strategy, which requires long navigation time and low efficiency. To solve this problem, a bio-inspired geomagnetic navigation method for AUV based on evolutionary gradient search is proposed. Combining the bionic evolutionary search algorithm with the classical gradient algorithm to search the function extremum can not only ensure the global optimization of the search, but also have fast convergence, which can improve the efficiency of bio-inspired geomagnetic navigation. The simulation results show that this method does not need prior geomagnetic information and can complete navigation tasks according to the geomagnetic trend. Comparing with the evolutionary search strategy, the effectiveness and superiority of the evolutionary gradient search strategy are verified.

A Novel Quantum Genetic Algorithm for Continuous Function Optimization

In this paper, a novel quantum genetic algorithm is proposed. This algorithm compares the probability expectation of the quantum chromosome with the best binary solution to determine rotation angle of rotation gate. Different individual in population evolve with different rate to complete local search and global search simultaneously. Hε gate is used to prevent the algorithm from premature convergence. After analyzing the algorithm and its global convergence, applying this approach to the optimization of function extremum, and comparing with the simple genetic algorithm and the quantum genetic algorithm, the simulation result illustrates that the algorithm has the characteristic of quick convergence speed and high solution precision.

A Kind of Adaptive Quantum Particle Swarm Algorithm Based on Phase Encoding

To improve the optimization performance of particle swarm, an adaptive quantum particle swarm optimization algorithm is proposed. In the algorithm, the spatial position of particles is described by the phase of quantum bits, and the position mutation of particles is achieved by Pauli-Z gates. An adaptive determination method of the global-factors is proposed by studying the relationship among inertia factors, self-factors and global-factors. The experimental results demonstrate that the proposed algorithm is much better than the standard particle swarm algorithm by solving the function extremum optimization problems.