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
针对不同个性化需求的燃料电池测试台(fuel cell test bench,FCTB)难以评价和量化评估的问题,提出一种基于改进和声搜索算法的FCTB价值评估方法.针对不同FCTB的个性化需求,建立了FCTB综合评估指标体系;结合用户的个性化需求,采用模糊层...针对不同个性化需求的燃料电池测试台(fuel cell test bench,FCTB)难以评价和量化评估的问题,提出一种基于改进和声搜索算法的FCTB价值评估方法.针对不同FCTB的个性化需求,建立了FCTB综合评估指标体系;结合用户的个性化需求,采用模糊层次分析法分配指标权重,构建价值定量评估模型,将权重求取问题转换为约束优化问题;提出一种改进和声搜索算法对问题进行求解,通过设计解向量生成机制和参数自适应调整策略,用于提高传统和声搜索算法的求解效率和搜索能力.仿真结果表明,本文方法在计算效率和精度方面具有优势,并能够根据不同的需求特性实现对FCTB方案做出定量的价值评估.展开更多
Harmony search(HS)is a form of stochastic meta-heuristic inspired by the improvisation process of musicians.In this study,a modified HS with a hybrid cuckoo search(CS)operator,HS-CS,is proposed to enhance global searc...Harmony search(HS)is a form of stochastic meta-heuristic inspired by the improvisation process of musicians.In this study,a modified HS with a hybrid cuckoo search(CS)operator,HS-CS,is proposed to enhance global search ability while avoiding falling into local optima.First,the randomness of the HS pitch disturbance adjusting method is analyzed to generate an adaptive inertia weight according to the quality of solutions in the harmony memory and to reconstruct the fine-tuning bandwidth optimization.This is to improve the efficiency and accuracy of HS algorithm optimization.Second,the CS operator is introduced to expand the scope of the solution space and improve the density of the population,which can quickly jump out of the local optimum in the randomly generated harmony and update stage.Finally,a dynamic parameter adjustment mechanism is set to improve the efficiency of optimization.Three theorems are proved to reveal HS-CS as a global convergence meta-heuristic algorithm.In addition,12 benchmark functions are selected for the optimization solution to verify the performance of HS-CS.The analysis shows that HS-CS is significantly better than other algorithms in optimizing high-dimensional problems with strong robustness,high convergence speed,and high convergence accuracy.For further verification,HS-CS is used to optimize the back propagation neural network(BPNN)to extract weighted fuzzy production rules.Simulation results show that the BPNN optimized by HS-CS can obtain higher classification accuracy of weighted fuzzy production rules.Therefore,the proposed HS-CS is proved to be effective.展开更多
In view of complex optimization problems with high nonlinearity and multiple extremums,harmony search algorithm has problems of slow convergence speed,being easy to be stalled and difficult to combine global search wi...In view of complex optimization problems with high nonlinearity and multiple extremums,harmony search algorithm has problems of slow convergence speed,being easy to be stalled and difficult to combine global search with local search.Therefore,a global competitive harmonic search algorithm based on stochastic attraction is proposed.Firstly,by introducing the stochastic attraction model to adjust the harmonic vector,the harmonic search algorithm was greatly improved and prone to fall into the local optimum.Secondly,the competitive search strategy was made to generate two harmony vectors for each iteration and make competitive selection.The adaptive global adjustment and local search strategy were designed to effectively balance the global and local search capabilities of the algorithm.The typical test function was used to test the algorithm.The experimental results show that the algorithm has high precision and the ability to find the global optimum compared with the existing algorithms.The overall accuracy is increased by more than 50%.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.
文摘针对不同个性化需求的燃料电池测试台(fuel cell test bench,FCTB)难以评价和量化评估的问题,提出一种基于改进和声搜索算法的FCTB价值评估方法.针对不同FCTB的个性化需求,建立了FCTB综合评估指标体系;结合用户的个性化需求,采用模糊层次分析法分配指标权重,构建价值定量评估模型,将权重求取问题转换为约束优化问题;提出一种改进和声搜索算法对问题进行求解,通过设计解向量生成机制和参数自适应调整策略,用于提高传统和声搜索算法的求解效率和搜索能力.仿真结果表明,本文方法在计算效率和精度方面具有优势,并能够根据不同的需求特性实现对FCTB方案做出定量的价值评估.
基金supported by the National Natural Science Foundation of China (No.62066016)the Natural Science Foundation of Hunan Province,China (No.2020JJ5458)+3 种基金the Research Foundation of Education Bureau of Hunan Province,China (Nos.22B0549 and 22B1046)the Fundamental Research Grant Scheme of Malaysia (No.R.J130000.7809.5F524)the UTMFR Grant (No.Q.J130000.2551.20H71)the Research Management Center (RMC)of Universiti Teknologi Malaysia (UTM)。
文摘Harmony search(HS)is a form of stochastic meta-heuristic inspired by the improvisation process of musicians.In this study,a modified HS with a hybrid cuckoo search(CS)operator,HS-CS,is proposed to enhance global search ability while avoiding falling into local optima.First,the randomness of the HS pitch disturbance adjusting method is analyzed to generate an adaptive inertia weight according to the quality of solutions in the harmony memory and to reconstruct the fine-tuning bandwidth optimization.This is to improve the efficiency and accuracy of HS algorithm optimization.Second,the CS operator is introduced to expand the scope of the solution space and improve the density of the population,which can quickly jump out of the local optimum in the randomly generated harmony and update stage.Finally,a dynamic parameter adjustment mechanism is set to improve the efficiency of optimization.Three theorems are proved to reveal HS-CS as a global convergence meta-heuristic algorithm.In addition,12 benchmark functions are selected for the optimization solution to verify the performance of HS-CS.The analysis shows that HS-CS is significantly better than other algorithms in optimizing high-dimensional problems with strong robustness,high convergence speed,and high convergence accuracy.For further verification,HS-CS is used to optimize the back propagation neural network(BPNN)to extract weighted fuzzy production rules.Simulation results show that the BPNN optimized by HS-CS can obtain higher classification accuracy of weighted fuzzy production rules.Therefore,the proposed HS-CS is proved to be effective.
文摘In view of complex optimization problems with high nonlinearity and multiple extremums,harmony search algorithm has problems of slow convergence speed,being easy to be stalled and difficult to combine global search with local search.Therefore,a global competitive harmonic search algorithm based on stochastic attraction is proposed.Firstly,by introducing the stochastic attraction model to adjust the harmonic vector,the harmonic search algorithm was greatly improved and prone to fall into the local optimum.Secondly,the competitive search strategy was made to generate two harmony vectors for each iteration and make competitive selection.The adaptive global adjustment and local search strategy were designed to effectively balance the global and local search capabilities of the algorithm.The typical test function was used to test the algorithm.The experimental results show that the algorithm has high precision and the ability to find the global optimum compared with the existing algorithms.The overall accuracy is increased by more than 50%.
