The paper discusses techniques for designing a seamless architecture of information systems (IS), which the author has developed and used in actual practice for many years. A seamless architecture is an architectural ...
详细信息
The paper discusses techniques for designing a seamless architecture of information systems (IS), which the author has developed and used in actual practice for many years. A seamless architecture is an architectural description of an IS that defines explicit connections between elements of architectural models of various architectural representations. Design techniques are based on the adaptive clustering method developed by the author, which allows one to bridge technological gaps between architectural abstracts of different levels and to link architectural models in such a way as to ensure the design of a more detailed model based on a model of a higher level of abstraction. Seamless architecture also solves the problem of traceability of requirements from the business process level to the functional and logical architecture of systems.
Bayesian optimization algorithm (BOA) utilizes a Bayesian network to estimate the probability distribution of candidate solutions and creates the next generation by sampling the constructed Bayesian network. This pape...
详细信息
Bayesian optimization algorithm (BOA) utilizes a Bayesian network to estimate the probability distribution of candidate solutions and creates the next generation by sampling the constructed Bayesian network. This paper proposes an improved real-coded BOA (IrBOA) for continuous global optimization. In order to create a set of Bayesian networks, the candidate solutions are partitioned by an adaptive clustering method. Each Bayesian network has its own structure and parameters, and the next generation is produced from this set of networks. The adaptive clustering method automatically determines the correct number of clusters so that the probabilistic building-block crossover (PBBC) is effectively preserved. This leads to a better search when the diversity of population is high at the beginning of search. Moreover, it tunes the solutions by automatically decreasing the number of clusters as the diversity of population decreases during the search process. The experimental results demonstrate that the proposed algorithm achieves better performance on well-known benchmark functions in the continuous global optimization.
暂无评论