In this paper, a static weapon target assignment（WTA）problem is studied. As a critical problem in cooperative air combat,outcome of WTA directly influences the battle. Along with the cost of weapons rising rapidly, ...In this paper, a static weapon target assignment（WTA）problem is studied. As a critical problem in cooperative air combat,outcome of WTA directly influences the battle. Along with the cost of weapons rising rapidly, it is indispensable to design a target assignment model that can ensure minimizing targets survivability and weapons consumption simultaneously. Afterwards an algorithm named as improved artificial fish swarm algorithm-improved harmony search algorithm（IAFSA-IHS） is proposed to solve the problem. The effect of the proposed algorithm is demonstrated in numerical simulations, and results show that it performs positively in searching the optimal solution and solving the WTA problem.展开更多
Purpose–The two main purposes of this paper are:first,the development of a new optimization algorithm called GHSACO by incorporating the global-best harmony search(GHS)which is a stochastic optimization algorithm rec...Purpose–The two main purposes of this paper are:first,the development of a new optimization algorithm called GHSACO by incorporating the global-best harmony search(GHS)which is a stochastic optimization algorithm recently developed,with the ant colony optimization(ACO)algorithm.Second,design of a new indirect adaptive recurrent fuzzy-neural controller(IARFNNC)for uncertain nonlinear systems using the developed optimization method(GHSACO)and the concept of the supervisory controller.Design/methodology/approach–The novel optimization method introduces a novel improvization process,which is different from that of the GHS in the following aspects:a modified harmony memory representation and conception.The use of a global random switching mechanism to monitor the choice between the ACO and GHS.An additional memory consideration selection rule using the ACO random proportional transition rule with a pheromone trail update mechanism.The developed optimization method is applied for parametric optimization of all recurrent fuzzy neural networks adaptive controller parameters.In addition,in order to guarantee that the system states are confined to the safe region,a supervisory controller is incorporated into the IARFNNC global structure.Findings–First,to analyze the performance of GHSACO method and shows its effectiveness,some benchmark functions with different dimensions are used.Simulation results demonstrate that it can find significantly better solutions when compared with the Harmony Search(HS),GHS,improved HS(IHS)and conventional ACO algorithm.In addition,simulation results obtained using an example of nonlinear system shows clearly the feasibility and the applicability of the proposed control method and the superiority of the GHSACO method compared to the HS,its variants,particle swarm optimization,and genetic algorithms applied to the same problem.Originality/value–The proposed new GHS algorithm is more efficient than the original HS method and its most known variants IHS and GHS.The proposed control method展开更多
Van Genuchten方程是最常用的土壤水分特征曲线方程,运用该方程的关键是4个参数的取值精度。为了精确地求解这些参数,引入和声搜索(HS)算法进行求解,提出一种基于全局信息的和声搜索优化计算方法——IGHS。IGHS算法具有如下特点:利用当...Van Genuchten方程是最常用的土壤水分特征曲线方程,运用该方程的关键是4个参数的取值精度。为了精确地求解这些参数,引入和声搜索(HS)算法进行求解,提出一种基于全局信息的和声搜索优化计算方法——IGHS。IGHS算法具有如下特点:利用当前和声记忆库中的全局最优解产生新解,改变了和声搜索算法新解的产生方式;通过对和声记忆库中当前最优解的扰动避免算法早熟,增强算法的全局搜索能力;IGHS算法结构简单,容易实现。实验结果表明IGHS算法求解Van Genuchten方程参数的精度与随机微粒群结果相似,但其收敛速快、计算量小,因此可以作为计算Van Genuchten方程参数的新方法。展开更多
基金supported by the National Natural Science Foundation of China(61472441)
文摘In this paper, a static weapon target assignment（WTA）problem is studied. As a critical problem in cooperative air combat,outcome of WTA directly influences the battle. Along with the cost of weapons rising rapidly, it is indispensable to design a target assignment model that can ensure minimizing targets survivability and weapons consumption simultaneously. Afterwards an algorithm named as improved artificial fish swarm algorithm-improved harmony search algorithm（IAFSA-IHS） is proposed to solve the problem. The effect of the proposed algorithm is demonstrated in numerical simulations, and results show that it performs positively in searching the optimal solution and solving the WTA problem.
文摘Purpose–The two main purposes of this paper are:first,the development of a new optimization algorithm called GHSACO by incorporating the global-best harmony search(GHS)which is a stochastic optimization algorithm recently developed,with the ant colony optimization(ACO)algorithm.Second,design of a new indirect adaptive recurrent fuzzy-neural controller(IARFNNC)for uncertain nonlinear systems using the developed optimization method(GHSACO)and the concept of the supervisory controller.Design/methodology/approach–The novel optimization method introduces a novel improvization process,which is different from that of the GHS in the following aspects:a modified harmony memory representation and conception.The use of a global random switching mechanism to monitor the choice between the ACO and GHS.An additional memory consideration selection rule using the ACO random proportional transition rule with a pheromone trail update mechanism.The developed optimization method is applied for parametric optimization of all recurrent fuzzy neural networks adaptive controller parameters.In addition,in order to guarantee that the system states are confined to the safe region,a supervisory controller is incorporated into the IARFNNC global structure.Findings–First,to analyze the performance of GHSACO method and shows its effectiveness,some benchmark functions with different dimensions are used.Simulation results demonstrate that it can find significantly better solutions when compared with the Harmony Search(HS),GHS,improved HS(IHS)and conventional ACO algorithm.In addition,simulation results obtained using an example of nonlinear system shows clearly the feasibility and the applicability of the proposed control method and the superiority of the GHSACO method compared to the HS,its variants,particle swarm optimization,and genetic algorithms applied to the same problem.Originality/value–The proposed new GHS algorithm is more efficient than the original HS method and its most known variants IHS and GHS.The proposed control method
