Multisensor Hybrid Dynamic Alliance Formation Problem Using Sensitive Particle-Based Dynamic Discrete PSO

In recent years, with the wide application and popularization of artificial intelligence algorithm in the field of multisensor information processing, it has been a research hotspot to solve the problem of sensor alliance formation in the battlefield environment by using multisensor cross-cueing technology. Based on the establishment of the multisensor hybrid dynamic alliance model and objective function, a multisensor cross-cueing algorithm based on dynamic discrete particle swarm optimization (DDPSO) with sensitive particles is proposed and a mechanism of “predict re-predict” is proposed in the process of sensor handover. Simulations have verified the good convergence effect and small detection error of multisensor cross-cueing technology in solving alliance formation problems. Meanwhile, compared with “measurement and then update” and “predict and update” mechanisms, the proposed mechanism is more suitable to the changing combat environment. At the same time, to some extent, it also shows that the artificial intelligence algorithm is more suitable for multisensor information processing.

Application Analysis on PSO Algorithm in the Discrete Optimization Problems

Particle Swarm Optimization (PSO) is kind of algorithm that can be used to solve optimization problems. In practice, many optimization problems are discrete but PSO algorithm was initially designed to meet the requirements of continuous problems. A lot of researches had made efforts to handle this case and varieties of discrete PSO algorithms were proposed. However, these algorithms just focus on the specific problem, and the performance of it significantly degrades when extending the algorithm to other problems. For now, there is no reasonable unified principle or method for analyzing the application of PSO algorithm in discrete optimization problem, which limits the development of discrete PSO algorithm. To address the challenge, we first give an investigation of PSO algorithm from the perspective of spatial search, then, try to give a novel analysis of the key feature changes when PSO algorithm is applied to discrete optimization, and propose a classification method to summary existing discrete PSO algorithms.