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
本文提出一种新的K Winners Take All神经网络 :High Speed K Winners Take All(HS K WTA) .HS K WTA是以竞争学习算法为基础 .HS K WTA能够从任何一个数集中 ,识别出K个较大的数 ,或较小的数 .本文给出HS K WTA算法及算法复杂性的分析...本文提出一种新的K Winners Take All神经网络 :High Speed K Winners Take All(HS K WTA) .HS K WTA是以竞争学习算法为基础 .HS K WTA能够从任何一个数集中 ,识别出K个较大的数 ,或较小的数 .本文给出HS K WTA算法及算法复杂性的分析结果 .结果显示HS K WTA要比Winstrons更好 ,更容易硬件实现 ,更稳定 ,尤其所取的数集中的数较大时 .展开更多
为提高光伏发电系统短期出力预测的精度,提出了一种和声搜索(Harmony Search,HS)算法与回声状态网络(Echo State Network,ESN)算法相结合的预测模型。该模型以光伏电站的历史发电量数据和气象数据为基础。首先通过相似日选择算法挑选出...为提高光伏发电系统短期出力预测的精度,提出了一种和声搜索(Harmony Search,HS)算法与回声状态网络(Echo State Network,ESN)算法相结合的预测模型。该模型以光伏电站的历史发电量数据和气象数据为基础。首先通过相似日选择算法挑选出预测日的相似日,将相似日的气象特征向量和预测日的气象特征向量的差值作为预测模型的输入变量;然后选择训练样本,并用和声搜索算法优化后的回声状态网络模型(HS-ESN)对样本进行训练和预测;最后以甘肃某光伏电站为例进行实例验证。实证分析表明,利用和声搜索算法优化回声状态网络预测模型的储备池参数可有效提高回声状态网络的预测精度,因此该模型具有较好的实用价值。展开更多
This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search(HS)algorithm, namely the oppositional global-based HS(OGHS), is proposed for solving the multi-object...This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search(HS)algorithm, namely the oppositional global-based HS(OGHS), is proposed for solving the multi-objective flexible job-shop scheduling problems(MOFJSPs) to minimize makespan, total machine workload and critical machine workload. An initialization program embedded in opposition-based learning(OBL) is developed for enabling the individuals to scatter in a well-distributed manner in the initial harmony memory(HM). In addition, the recursive halving technique based on opposite number is employed for shrinking the neighbourhood space in the searching phase of the OGHS. From a practice-related standpoint, a type of dual vector code technique is introduced for allowing the OGHS algorithm to adapt the discrete nature of the MOFJSP. Two practical techniques, namely Pareto optimality and technique for order preference by similarity to an ideal solution(TOPSIS), are implemented for solving the MOFJSP.Furthermore, the algorithm performance is tested by using different strategies, including OBL and recursive halving, and the OGHS is compared with existing algorithms in the latest studies.Experimental results on representative examples validate the performance of the proposed algorithm for solving the MOFJSP.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.
文摘本文提出一种新的K Winners Take All神经网络 :High Speed K Winners Take All(HS K WTA) .HS K WTA是以竞争学习算法为基础 .HS K WTA能够从任何一个数集中 ,识别出K个较大的数 ,或较小的数 .本文给出HS K WTA算法及算法复杂性的分析结果 .结果显示HS K WTA要比Winstrons更好 ,更容易硬件实现 ,更稳定 ,尤其所取的数集中的数较大时 .
文摘为提高光伏发电系统短期出力预测的精度,提出了一种和声搜索(Harmony Search,HS)算法与回声状态网络(Echo State Network,ESN)算法相结合的预测模型。该模型以光伏电站的历史发电量数据和气象数据为基础。首先通过相似日选择算法挑选出预测日的相似日,将相似日的气象特征向量和预测日的气象特征向量的差值作为预测模型的输入变量;然后选择训练样本,并用和声搜索算法优化后的回声状态网络模型(HS-ESN)对样本进行训练和预测;最后以甘肃某光伏电站为例进行实例验证。实证分析表明,利用和声搜索算法优化回声状态网络预测模型的储备池参数可有效提高回声状态网络的预测精度,因此该模型具有较好的实用价值。
基金supported by the National Key Research and Development Program of China(2016YFD0700605)the Fundamental Research Funds for the Central Universities(JZ2016HGBZ1035)the Anhui University Natural Science Research Project(KJ2017A891)
文摘This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search(HS)algorithm, namely the oppositional global-based HS(OGHS), is proposed for solving the multi-objective flexible job-shop scheduling problems(MOFJSPs) to minimize makespan, total machine workload and critical machine workload. An initialization program embedded in opposition-based learning(OBL) is developed for enabling the individuals to scatter in a well-distributed manner in the initial harmony memory(HM). In addition, the recursive halving technique based on opposite number is employed for shrinking the neighbourhood space in the searching phase of the OGHS. From a practice-related standpoint, a type of dual vector code technique is introduced for allowing the OGHS algorithm to adapt the discrete nature of the MOFJSP. Two practical techniques, namely Pareto optimality and technique for order preference by similarity to an ideal solution(TOPSIS), are implemented for solving the MOFJSP.Furthermore, the algorithm performance is tested by using different strategies, including OBL and recursive halving, and the OGHS is compared with existing algorithms in the latest studies.Experimental results on representative examples validate the performance of the proposed algorithm for solving the MOFJSP.
