This paper focuses on the artificial bee colony (ABC) algorithm as one of swarm optimization methods and proposes ABC-alis (ABC algorithm based on adaptive local information sharing) by improving the ABC algorithm for dynamic optimization problems (DOPs). ABC-alis is applied to various types of dynamic changes embedded in DOPs to verify its tracking ability in such dynamic environments. Concretely, the following five types of dynamic changes and one of the high-dimensional problem are employed as the different environments: (A) a periodic change of evaluation values of local optima; (B) a random change of evaluation values of local optima; (C) a random change of local optima coordinates; (D) a combination of two kinds of random changes (B+C); (E) a random speed change of local optima in the environment (D); and (F) a high-dimensional problem in the environment (E). In these experiments, the following three methods are compared: ABC-alis as the proposed method, ABC-lis as our previous method of ABC-alis, and speciation-based particle swarm optimization (SPSO) as the conventional method. The experimental result revealed that the following implications: (1) ABC-alis and ABC-lis can capture the optimal solution more quickly and keep a better solution than SPSO in the various types of dynamic changes; (2) from environments C, D, and E, ABC-alis can adapt to the random change of local optima from the viewpoint of the evaluation value, coordinates, speed, and all of them; and (3) ABC-alis can maintain its performance even in the high dimensional environment F.
This paper proposes an inter-business trading structure model with agent-based simulation. The proposed model can deal with the two kinds of dynamic changes: (1) supply and demand volumes and the resulting production lead time, and (2) the combination of both decentralized and centralized inter-business structure. The model is applied to analyzing recent rapid market changes of Japanese firm data. The data is taken from the TDB (Teikoku Databank) business trading database, which contains firm information with four million trading items, business volumes and relations over many years. Using this information, we focused on changes in the trading of firms involved in the Japanese software industry. The experimental results applied to real data suggest that the dynamic structural changes determined by the model have a critical role in various cases of performance of Japanese software industry firms in the TDB database.
There is a trade-off between resolution and acquisition range of voltage while performance of feedback control depends on the resolution. Many signals in motion control change quickly with various range and hence both of saturation avoidance and high-resolution data-acquisition are one of the challenges. This research proposes a multi-amplification technique which provides a wide-range and high-resolution A/D conversion. A high-frequency-operated circuit and a fast processor on an FPGA are developed and then a signal-processing module is designed. Experimental results show the validity of the module.
This paper pursues to construct a theoretical framework which can efficiently capture the dynamics of large-scale heterogeneous power grids. We formulate a networked nonlinear descriptor system consisting of subsystems and network system as a mathematical abstraction of such grids. This descriptor representation of the system enables us to consider efficient analysis and control of the system while preserving its network topology. As a main result, we clarify the dissipativity of the systems and derive a sufficient condition for local asymptotic stability of partial states and synchronization based on the dissipativity. We apply these results to a power grid described by a structure-preserving model, showing their effectiveness in an engineering problem.