Hawk and pigeon's intelligence for UAV swarm dynamic combat game via competitive learning pigeon-inspired optimization

For unmanned aerial vehicle(UAV)swarm dynamic combat,swarm antagonistic motion control and attack target allocation are extremely challenging sub-tasks.In this paper,the competitive learning pigeon-inspired optimization(CLPIO)algorithm is proposed to handle the cooperative dynamic combat problem,which integrates the distributed swarm antagonistic motion and centralized attack target allocation.Moreover,the threshold trigger strategy is presented to switch two sub-tasks.To seek a feasible and optimal combat scheme,a dynamic game approach combined with hawk grouping mechanism and situation assessment between sub-groups is designed to guide the solution of the optimal attack scheme,and the model of swarm antagonistic motion imitating pigeon’s intelligence is proposed to form a confrontation situation.The analysis of the CLPIO algorithm shows its convergence in theory and the comparison with the other four metaheuristic algorithms shows its superiority in solving the mixed Nash equilibrium problem.Finally,numerical simulation verifis that the proposed methods can provide an effective combat scheme in the set scenario.

Study on group air to ground attack-defends hierarchical dynamic decision-making

As to oppositional, multi-objective and hierarchical characteristic of air formation to ground attackdefends campaign, and using dynamic space state model of military campaign, this article establishes a principal and subordinate hierarchical interactive decision-making way, the Nash-Stackelberg-Nash model, to solve the problems in military operation, and find out the associated best strategy in hierarchical dynamic decision-making. The simulating result indicate that when applying the model to air formation to ground attack-defends decision-making system, it can solve the problems of two hierarchies, dynamic oppositional decision-making favorably, and reach preferable effect in battle. It proves that the model can provide an effective way for analyzing a battle,

Game theoretic analysis of environmental impact assessment system in China

Environmental impact assessment(EIA)system has been established in China since 1973.In present EIA cases,there are four participants in general:governments,enterprises,EIA organizations and the public.The public has held responsible for both social costs and social duties.The public supervises social costs produced by enterprises discharging pollutant in EIA.However public participation is mostly deputized by governments,which severely weaken the independence of the public as one participant in EIA.In this paper,EIA refers to the different attitudes of the par-ticipants whose optional strategies may be described by a proper game model.According to disfigurements in EIA,three sides(governments,enterprises,and EIA organizations)dynamic iterative game theory of many phases is established referring to iterative game theory,dynamic game theory of incomplete information,and perfect Bayesian equilibrium theory to analyze the reciprocity relation among governments,EIA organizations and enterprises.The results show that in a short period,economic benefit is preponderant over social benefit.Governments and enterprises both do not want to take EIA to reveal social costs.EIA organizations’income comes from enterprises and the collusions are built between them to vindicate economic benefit.In a long run,social benefit loss caused by environmental pollution must be recuperated sooner or later and environmental deterioration will influence the achievements of economic benefit,so both governments and enterprises are certain to pursue high social benefit and willing to take EIA,helpful to increase private benefit.EIA organizations will make fair assessment when their economic benefit are ensured.At present,the public as silent victims can not take actual part in EIA.The EIA system must be improved to break the present equilibrium of three sides,bringing the public to the equilibrium to exert public supervision.