Application of the Improved Chaotic Self-Adapting Monkey Algorithm Into Radar Systems of Internet of Things

With the new applications of Internet of Things in recent years, radar sensors have become an important design unit in Internet of Things and embedded design. Considering that the dimensions of the radar sensors deployment problem increase with the increasing number of radars deployed and that the strength of the monkey algorithm is that it avoids the “dimension disaster,”this algorithm is introduced to solve the optimization problem. Some improvements are made based on the shortcomings of the traditional monkey algorithm. Adaptive climbing steps are used in the climbing process to enhance its local search capabilities, a tent function is used to balance the search accuracy and search time in the overlooking process and the jumping process, and a semi-group execution strategy is adopted for the above two processes. To improve the search accuracy, the learning factor and the Euclidean distance are introduced into the looping process, which improves the optimization ability and avoids the individual homoplasy. Therefore, the improved chaotic self-adapting monkey algorithm (ICSAMA) is proposed and abbreviated to ICSAMA. The simulation results show that the improved chaotic adaptive monkey algorithm is better than the monkey algorithm regarding the convergence precision and convergence rate. Finally, a mathematical model of radar deployment is established based on the volume of airspace coverage. Three simulation experiments are designed by using different conditions and scenarios, such as air defense, maritime combat, and trajectory planning, and an emphasis is placed on describing the applications of ICSAMA. The results show that ICSAMA can effectively solve the problem of radar deployment and provide technical support for the site selection of new observation and communication posts, deployment of maneuverable radar stations, and track planning of fleets.