Target distribution in cooperative combat based on Bayesian optimization algorithm

Target distribution in cooperative combat is a difficult and emphases. We build up the optimization model according to the rule of fire distribution. We have researched on the optimization model with BOA. The BOA can estimate the joint probability distribution of the variables with Bayesian network, and the new candidate solutions also can be generated by the joint distribution. The simulation example verified that the method could be used to solve the complex question, the operation was quickly and the solution was best.

Air Combat Assignment Problem Based on Bayesian Optimization Algorithm

In order to adapt to the changing battlefield situation and improve the combat effectiveness of air combat,the problem of air battle allocation based on Bayesian optimization algorithm(BOA)is studied.First,we discuss the number of fighters on both sides,and apply cluster analysis to divide our fighter into the same number of groups as the enemy.On this basis,we sort each of our fighters'different advantages to the enemy fighters,and obtain a series of target allocation schemes for enemy attacks by first in first serviced criteria.Finally,the maximum advantage function is used as the target,and the BOA is used to optimize the model.The simulation results show that the established model has certain decision-making ability,and the BOA can converge to the global optimal solution at a faster speed,which can effectively solve the air combat task assignment problem.

Exploiting Bivariate Dependencies to Speedup Structure Learning in Bayesian Optimization Algorithm

Bayesian optimization algorithm （BOA） is one of the successful and widely used estimation of distribution algorithms （EDAs） which have been employed to solve different optimization problems. In EDAs, a model is learned from the selected population that encodes interactions among problem variables. New individuals are generated by sampling the model and incorporated into the population. Different probabilistic models have been used in EDAs to learn interactions. Bayesian network （BN） is a well-known graphical model which is used in BOA. Learning a propel model in EDAs and particularly in BOA is distinguished as a computationally expensive task. Different methods have been proposed in the literature to improve the complexity of model building in EDAs. This paper employs bivariate dependencies to learn accurate BNs in BOA efficiently. The proposed approach extracts the bivariate dependencies using an appropriate pairwise interaction-detection metric. Due to the static structure of the underlying problems, these dependencies are used in each generation of BOA to learn an accurate network. By using this approach, the computational cost of model building is reduced dramatically. Various optimization problems are selected to be solved by the algorithm. The experimental results show that the proposed approach successfully finds the optimum in problems with different types of interactions efficiently. Significant speedups are observed in the model building procedure as well.

Coordinated Bayesian optimal approach for the integrated decision between electronic countermeasure and firepower attack

The coordinated Bayesian optimization algorithm（CBOA） is proposed according to the characteristics of the function independence,conformity and supplementary between the electronic countermeasure（ECM） and the firepower attack systems.The selection criteria are combinations of probabilities of individual fitness and coordinated degree and can select choiceness individual to construct Bayesian network that manifest population evolution by producing the new chromosome.Thus the CBOA cannot only guarantee the effective pattern coordinated decision-making mechanism between the populations,but also maintain the population multiplicity,and enhance the algorithm performance.The simulation result confirms the algorithm validity.

Multi-objective optimization of environmental tax for mitigating air pollution and greenhouse gas

Government macro-control through various policies is an important way to mitigate air pollution and greenhouse gases.Therefore,environmental tax is used worldwide as an important measure.However,few studies have considered the interaction between carbon and environmental protection taxes.Additionally,different sectors differ in their energy structure,pollution emission intensity,and economic status,and previous studies rarely proposed differentiated environmental tax rates based at the sectoral level.A model framework combining the computable general equilibrium(CGE)model and Bayesian optimization(BO)algorithm is proposed to maximize GDP,meet environmental planning objectives,and explore the optimal environmental taxation scheme to realize the multi-objective optimization of the economy and environment.Meanwhile,this study compares the different impact mechanisms of environmental protection tax and carbon tax.It discusses the impacts of differentiated environmental tax rates in different sectors on the environment and economy.For example,the results show that the coordinated implementation of environmental protection and carbon tax policies and the sectoral differentiated environmental tax rates in China could better balance economic development and environmental governance.Additionally,the optimal taxation scheme could mitigate air pollution and greenhouse gases,promote economic growth,and realize sustainable economic and environmental development.Furthermore,the optimized taxation scheme positively affects the energy and industrial structures.