Availability optimization of a redundant system through dependency modeling

This paper presents a constructive approach to optimize the availability of a system through modeling the dependency of the components. Our goal is to minimize the system cost under the constraint that system availability must not be less than a given level. In particular, the components are dependent of each other. A function noted as the dependence function is introduced to model the dependency. It is demonstrated that, for a general form of the system cost, the dependence function guarantees a finite set of feasible solutions. An approach is then developed with the help of the dependence function to obtain the optimal solution. The resolution is illustrated by an interesting example, in which the system cost depends on the strength of the dependency. Our study reveals that the dependency is an essential and effective option to improve system reliability. Moreover, the modeling of dependency, i.e. the introduction of the dependence function is valuable for resolving the optimization problem.     

随机网络计划在审计中的应用

审计作为市场经济的自我约束机制,在经济发展中有着不可或缺的责任.本文利用随机网络技术进行分析,旨在设计出高效率的审计活动方案.首先,利用PERT技术建立了确定型的审计活动模型,在此基础上给出了时间—资源优化下的最优人员分配方案;其次,利用GERT技术建立了随机型的审计活动模型,引入矩母函数和梅森公式进行GERT解析求解求出所需的工期等指标,同时采用蒙特卡罗模拟求解验证解析求解的准确性,为审计活动的工期控制提供了理论依据.最后对于工作时间确定的GERT模型,结合PERT和GERT两种技术对其进行简化分析,从而得到了时间—资源优化下的最优人员分配方案.     

Bird mating optimizer: An optimization algorithm inspired by bird mating strategies

Thanks to their simplicity and flexibility, evolutionary algorithms (EAs) have attracted significant attention to tackle complex optimization problems. The underlying idea behind all EAs is the same and they differ only in technical details. In this paper, we propose a novel version of EAs, bird mating optimizer (BMO), for continuous optimization problems which is inspired by mating strategies of bird species during mating season. BMO imitates the behavior of bird species metaphorically to breed broods with superior genes for designing optimum searching techniques. On a large set of unimodal and multimodal benchmark functions, BMO represents a competitive performance to other EAs.     

Optimized projections for compressed sensing via rank-constrained nearest correlation matrix

Optimizing the acquisition matrix is useful for compressed sensing of signals that are sparse in overcomplete dictionaries, because the acquisition matrix can be adapted to the particular correlations of the dictionary atoms. In this paper a novel formulation of the optimization problem is proposed, in the form of a rank-constrained nearest correlation matrix problem. Furthermore, improvements for three existing optimization algorithms are introduced, which are shown to be particular instances of the proposed formulation. Simulation results show notable improvements and superior robustness in sparse signal recovery.     

Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems

This paper presents a parameter adaptive harmony search algorithm (PAHS) for solving optimization problems. The two important parameters of harmony search algorithm namely Harmony Memory Consideration Rate (HMCR) and Pitch Adjusting Rate (PAR), which were either kept constant or the PAR value was dynamically changed while still keeping HMCR fixed, as observed from literature, are both being allowed to change dynamically in this proposed PAHS. This change in the parameters has been done to get the global optimal solution. Four different cases of linear and exponential changes have been explored. The change has been allowed during the process of improvization. The proposed algorithm is evaluated on 15 standard benchmark functions of various characteristics. Its performance is investigated and compared with three existing harmony search algorithms. Experimental results reveal that proposed algorithm outperforms the existing approaches when applied to 15 benchmark functions. The effects of scalability, noise, and harmony memory size have also been investigated on four approaches of HS. The proposed algorithm is also employed for data clustering. Five real life datasets selected from UCI machine learning repository are used. The results show that, for data clustering, the proposed algorithm achieved results better than other algorithms.     

Temperature independent length optimization of L-band EDFAs providing flat gain

In this study, single stage (SS), double-stage (DS), and gain flattened (GF) DS L-band erbium-doped fiber amplifier (EDFA) configurations are designed in order to obtain a flat gain amplifier. Temperature dependence of the mentioned configurations is also analyzed. Maximum spectral dependence of EDFA gain with respect to temperature is obtained for SS EDFA design while smaller spectral dependence of gains is obtained for both DS and GF DS L-EDFA configurations. It is observed that the maximum temperature dependence is in the range of 1570-1580 nm band for all configurations. It has also been found that for all configurations, reducing the temperature has greater effect than raising the temperature on EDFA gain. The overall results show that a temperature independent L-band configuration has not been possible. However, for some signal wavelengths, the erbium-doped fiber (EDF) lengths at which the gain is temperature independent are observed.     

Some applications of L2-hedging with a non-negative wealth process

We consider the problem of L ²-hedging of contingent claims in diffusion type models for securities markets. In contrast to a recent paper of Schweizer (1994) we insist on a non-negative wealth process corresponding to the optimal hedge portfolio. For this reason the usual projection methods cannot be applied. We give some applications of L ²-hedging in this setting including hedging under constraints, a problem of approximating the wealth process of a richer investor and a mean-variance version of it.     

A Dynamic Programming Algorithm for the Fused Lasso and L 0-Segmentation

We propose a dynamic programming algorithm for the one-dimensional Fused Lasso Signal Approximator (FLSA). The proposed algorithm has a linear running time in the worst case. A similar approach is developed for the task of least squares segmentation, and simulations indicate substantial performance improvement over existing algorithms. Examples of R and C implementations are provided in the online Supplementary materials, posted on the journal web site.