Broadband and omnidirectional antireflection coating is generally an effective way to improve solar cell efficiency, because the destructive interference between the reflected and incident light can maximize the light...Broadband and omnidirectional antireflection coating is generally an effective way to improve solar cell efficiency, because the destructive interference between the reflected and incident light can maximize the light transmission into the absorption layer. In this paper, we report the incident quantum efficiency ηin, not incident energy or power, as the evaluation function by the ant colony algorithm optimization method, which is a swarm-based optimization method. Also, SPCTRL2 is proposed to be incorporated for accurate optimization because the solar irradiance on a receiver plane is dependent on position, season, and time. Cities of Quito, Beijing and Moscow are selected for two-and three-layer antireflective coating optimization over λ = [300,1100] nm and θ = [0°, 90°]. The ηin increases by 0.26%, 1.37% and 4.24% for the above 3 cities, respectively, compared with that calculated by other rigorous optimization algorithms methods, which is further verified by the effect of position and time dependent solar spectrum on the antireflective coating design.展开更多
The problem of joint radio and cloud resources allocation is studied for heterogeneous mobile cloud computing networks. The objective of the proposed joint resource allocation schemes is to maximize the total utility ...The problem of joint radio and cloud resources allocation is studied for heterogeneous mobile cloud computing networks. The objective of the proposed joint resource allocation schemes is to maximize the total utility of users as well as satisfy the required quality of service(QoS) such as the end-to-end response latency experienced by each user. We formulate the problem of joint resource allocation as a combinatorial optimization problem. Three evolutionary approaches are considered to solve the problem: genetic algorithm(GA), ant colony optimization with genetic algorithm(ACO-GA), and quantum genetic algorithm(QGA). To decrease the time complexity, we propose a mapping process between the resource allocation matrix and the chromosome of GA, ACO-GA, and QGA, search the available radio and cloud resource pairs based on the resource availability matrixes for ACOGA, and encode the difference value between the allocated resources and the minimum resource requirement for QGA. Extensive simulation results show that our proposed methods greatly outperform the existing algorithms in terms of running time, the accuracy of final results, the total utility, resource utilization and the end-to-end response latency guaranteeing.展开更多
基金supported by the National Key Research and Development of China (No. 2017YFF0104801)the National Natural Science Foundation of China (Nos. 61675046, 61804012)the Open Fund of IPOC (No. IPOC2017B011)
文摘Broadband and omnidirectional antireflection coating is generally an effective way to improve solar cell efficiency, because the destructive interference between the reflected and incident light can maximize the light transmission into the absorption layer. In this paper, we report the incident quantum efficiency ηin, not incident energy or power, as the evaluation function by the ant colony algorithm optimization method, which is a swarm-based optimization method. Also, SPCTRL2 is proposed to be incorporated for accurate optimization because the solar irradiance on a receiver plane is dependent on position, season, and time. Cities of Quito, Beijing and Moscow are selected for two-and three-layer antireflective coating optimization over λ = [300,1100] nm and θ = [0°, 90°]. The ηin increases by 0.26%, 1.37% and 4.24% for the above 3 cities, respectively, compared with that calculated by other rigorous optimization algorithms methods, which is further verified by the effect of position and time dependent solar spectrum on the antireflective coating design.
基金supported by the National Natural Science Foundation of China (No. 61741102, No. 61471164)China Scholarship Council
文摘The problem of joint radio and cloud resources allocation is studied for heterogeneous mobile cloud computing networks. The objective of the proposed joint resource allocation schemes is to maximize the total utility of users as well as satisfy the required quality of service(QoS) such as the end-to-end response latency experienced by each user. We formulate the problem of joint resource allocation as a combinatorial optimization problem. Three evolutionary approaches are considered to solve the problem: genetic algorithm(GA), ant colony optimization with genetic algorithm(ACO-GA), and quantum genetic algorithm(QGA). To decrease the time complexity, we propose a mapping process between the resource allocation matrix and the chromosome of GA, ACO-GA, and QGA, search the available radio and cloud resource pairs based on the resource availability matrixes for ACOGA, and encode the difference value between the allocated resources and the minimum resource requirement for QGA. Extensive simulation results show that our proposed methods greatly outperform the existing algorithms in terms of running time, the accuracy of final results, the total utility, resource utilization and the end-to-end response latency guaranteeing.
