As a typical representative of the NP-complete problem, the traveling salesman problem(TSP) is widely utilized in computer networks, logistics distribution, and other fields. In this paper, a discrete lion swarm optim...As a typical representative of the NP-complete problem, the traveling salesman problem(TSP) is widely utilized in computer networks, logistics distribution, and other fields. In this paper, a discrete lion swarm optimization(DLSO) algorithm is proposed to solve the TSP. Firstly, we introduce discrete coding and order crossover operators in DLSO. Secondly, we use the complete 2-opt(C2-opt) algorithm to enhance the local search ability.Then in order to enhance the efficiency of the algorithm, a parallel discrete lion swarm optimization(PDLSO) algorithm is proposed.The PDLSO has multiple populations, and each sub-population independently runs the DLSO algorithm in parallel. We use the ring topology to transfer information between sub-populations. Experiments on some benchmarks TSP problems show that the DLSO algorithm has a better accuracy than other algorithms, and the PDLSO algorithm can effectively shorten the running time.展开更多
This paper introduces a multi-agent system which i nt egrates process planning and production scheduling, in order to increase the fle xibility of manufacturing systems in coping with rapid changes in dynamic market a...This paper introduces a multi-agent system which i nt egrates process planning and production scheduling, in order to increase the fle xibility of manufacturing systems in coping with rapid changes in dynamic market and dealing with internal uncertainties such as machine breakdown or resources shortage. This system consists of various autonomous agents, each of which has t he capability of communicating with one another and making decisions based on it s knowledge and if necessary on information provided by other agents. Machine ag ents which represent the machines play an important role in the system in that t hey negotiate with each other to bid for jobs. An iterative bidding mechanism is proposed to facilitate the process of job assignment to machines and handle the negotiation between agents. This mechanism enables near optimal process plans a nd production schedules to be produced concurrently, so that dynamic changes in the market can be coped with at a minimum cost, and the utilisation of manufactu ring resources can be optimised. In addition, a currency scheme with currency-l ike metrics is proposed to encourage or prohibit machine agents to put forward t heir bids for the jobs announced. The values of the metrics are adjusted iterati vely so as to obtain an integrated plan and schedule which result in the minimum total production cost while satisfying products due dates. To deal with the optimisation problem, i.e. to what degree and how the currencies should be adj usted in each iteration, a genetic algorithm (GA) is developed. Comparisons are made between GA approach and simulated annealing (SA) optimisation technique.展开更多
Due to the complicated background of objectives and speckle noise, it is almost impossible to extract roads directly from original synthetic aperture radar(SAR) images. A method is proposed for extraction of road netw...Due to the complicated background of objectives and speckle noise, it is almost impossible to extract roads directly from original synthetic aperture radar(SAR) images. A method is proposed for extraction of road network from high-resolution SAR image. Firstly, fuzzy C means is used to classify the filtered SAR image unsupervisedly, and the road pixels are isolated from the image to simplify the extraction of road network. Secondly, according to the features of roads and the membership of pixels to roads, a road model is constructed, which can reduce the extraction of road network to searching globally optimization continuous curves which pass some seed points. Finally, regarding the curves as individuals and coding a chromosome using integer code of variance relative to coordinates, the genetic operations are used to search global optimization roads. The experimental results show that the algorithm can effectively extract road network from high-resolution SAR images.展开更多
文摘采用UHPLC-QE-Orbitrap MS技术结合网络分析和化学计量学建立钴胺素C(cblC)缺乏症的临床表型系统表征和预测模型,利用尝试解开其复杂性。基于UHPLC-QE-Orbitrap MS技术在正、负模式下采集的血液非靶向代谢组学图谱,利用数据驱动网络算法Connect the Dots(CTD)快速搜索高连通的扰动代谢物,化学计量学算法学习其组别间复杂微小变化模式。通过对两种临床表型(癫痫和代谢综合征)的研究,结果表明CTD算法识别出的扰动代谢物子集展示出高度的临床表型特异性,且涉及的富集通路扰动均被报道与癫痫和代谢综合征的致病机制密切相关。进一步,CTD算法能够量度高连通扰动代谢物间的协变信息,构建主要疾病模块系统地表征癫痫和代谢综合征的复杂致病机制。识别出的扰动代谢物作为特征变量集,采用5-折交叉验证,偏最小二乘判别分析、支持向量机和随机森林的受试者工作特征曲线下面积预测均值分别为0.849、0.897和0.909(癫痫),0.889、0.931和0.921(代谢综合征),马修斯相关系数预测均值分别为0.667、0.668和0.723(癫痫),0.686、0.696和0.787(代谢综合征)。上述结果表明了提出的计算方法在揭示cblC缺乏症的临床表型复杂性和指导其个性化诊断策略方面的有效性。
基金supported by the National Natural Science Foundation of China(61771293)the Key Project of Shangdong Province(2019JZZY010111)。
文摘As a typical representative of the NP-complete problem, the traveling salesman problem(TSP) is widely utilized in computer networks, logistics distribution, and other fields. In this paper, a discrete lion swarm optimization(DLSO) algorithm is proposed to solve the TSP. Firstly, we introduce discrete coding and order crossover operators in DLSO. Secondly, we use the complete 2-opt(C2-opt) algorithm to enhance the local search ability.Then in order to enhance the efficiency of the algorithm, a parallel discrete lion swarm optimization(PDLSO) algorithm is proposed.The PDLSO has multiple populations, and each sub-population independently runs the DLSO algorithm in parallel. We use the ring topology to transfer information between sub-populations. Experiments on some benchmarks TSP problems show that the DLSO algorithm has a better accuracy than other algorithms, and the PDLSO algorithm can effectively shorten the running time.
文摘This paper introduces a multi-agent system which i nt egrates process planning and production scheduling, in order to increase the fle xibility of manufacturing systems in coping with rapid changes in dynamic market and dealing with internal uncertainties such as machine breakdown or resources shortage. This system consists of various autonomous agents, each of which has t he capability of communicating with one another and making decisions based on it s knowledge and if necessary on information provided by other agents. Machine ag ents which represent the machines play an important role in the system in that t hey negotiate with each other to bid for jobs. An iterative bidding mechanism is proposed to facilitate the process of job assignment to machines and handle the negotiation between agents. This mechanism enables near optimal process plans a nd production schedules to be produced concurrently, so that dynamic changes in the market can be coped with at a minimum cost, and the utilisation of manufactu ring resources can be optimised. In addition, a currency scheme with currency-l ike metrics is proposed to encourage or prohibit machine agents to put forward t heir bids for the jobs announced. The values of the metrics are adjusted iterati vely so as to obtain an integrated plan and schedule which result in the minimum total production cost while satisfying products due dates. To deal with the optimisation problem, i.e. to what degree and how the currencies should be adj usted in each iteration, a genetic algorithm (GA) is developed. Comparisons are made between GA approach and simulated annealing (SA) optimisation technique.
文摘Due to the complicated background of objectives and speckle noise, it is almost impossible to extract roads directly from original synthetic aperture radar(SAR) images. A method is proposed for extraction of road network from high-resolution SAR image. Firstly, fuzzy C means is used to classify the filtered SAR image unsupervisedly, and the road pixels are isolated from the image to simplify the extraction of road network. Secondly, according to the features of roads and the membership of pixels to roads, a road model is constructed, which can reduce the extraction of road network to searching globally optimization continuous curves which pass some seed points. Finally, regarding the curves as individuals and coding a chromosome using integer code of variance relative to coordinates, the genetic operations are used to search global optimization roads. The experimental results show that the algorithm can effectively extract road network from high-resolution SAR images.
