An improved genetic algorithm with dynamic topology

The genetic algorithm （GA） is a nature-inspired evolutionary algorithm to find optima in search space via the interac- tion of individuals. Recently, researchers demonstrated that the interaction topology plays an important role in information exchange among individuals of evolutionary algorithm. In this paper, we investigate the effect of different network topolo- gies adopted to represent the interaction structures. It is found that GA with a high-density topology ends up more likely with an unsatisfactory solution, contrarily, a low-density topology can impede convergence. Consequently, we propose an improved GA with dynamic topology, named DT-GA, in which the topology structure varies dynamically along with the fitness evolution. Several experiments executed with 15 well-known test functions have illustrated that DT-GA outperforms other test GAs for making a balance of convergence speed and optimum quality. Our work may have implications in the combination of complex networks and computational intelligence.

Three-Dimensional Discrete Observability Analysis for Air-to-Air Missile Target Tracking

The interception information of infrared（ IR）-guided air-to-air missiles（ AAM） is mainly estimated only using the basic bearing measurements. In order to intercept highly maneuverable targets,it is essential to study the system observability to improve the target tracking system performance.The uniqueness of this paper is that the observability analysis is derived based on a discrete three-dimensional （3D） system model. During the maneuvering scenario,the system is approximated by a segment-by-segment system. The relationship between missile-target motion and observability is given by direct and dual approaches. Meanwhile sufficient observability conditions are derived. Moreover,a numerical simulation is conducted and an alternate method is provided to reinforce the proposed observability analysis results.

A survey of safety separation management and collision avoidance approaches of civil UAS operating in integration national airspace system

Recent years have witnessed a booming of the industry of civil Unmanned Aircraft System(UAS).As an emerging industry,the UAS industry has been attracting great attention from governments of all countries and the aviation industry.UAS are highly digitalized,informationized,and intelligent;therefore,their integration into the national airspace system has become an important trend in the development of civil aviation.However,the complexity of UAS operation poses great challenges to the traditional aviation regulatory system and technical means.How to prevent collisions between UASs and between UAS and manned aircraft to achieve safe and efficient operation in the integrated operating airspace has become a common challenge for industry and academia around the world.In recent years,the international community has carried out a great amount of work and experiments in the air traffic management of UAS and some of the key technologies.This paper attempts to make a review of the UAS separation management and key technologies in collision avoidance in the integrated airspace,mainly focusing on the current situation of UAS Traffic Management(UTM),safety separation standards,detection system,collision risk prediction,collision avoidance,safety risk assessment,etc.,as well as an analysis of the bottlenecks that the current researches encountered and their development trends,so as to provide some insights and references for further research in this regard.Finally,this paper makes a further summary of some of the research highlights and challenges.

Optimized deployment of a radar network based on an improved firefly algorithm

The threats and challenges of unmanned aerial vehicle(UAV) invasion defense due to rapid UAV development have attracted increased attention recently. One of the important UAV invasion defense methods is radar network detection. To form a tight and reliable radar surveillance network with limited resources, it is essential to investigate optimized radar network deployment. This optimization problem is difficult to solve due to its nonlinear features and strong coupling of multiple constraints. To address these issues, we propose an improved firefly algorithm that employs a neighborhood learning strategy with a feedback mechanism and chaotic local search by elite fireflies to obtain a trade-off between exploration and exploitation abilities. Moreover, a chaotic sequence is used to generate initial firefly positions to improve population diversity. Experiments have been conducted on 12 famous benchmark functions and in a classical radar deployment scenario. Results indicate that our approach achieves much better performance than the classical firefly algorithm(FA) and four recently proposed FA variants.