Total Optimization of Energy Networks in whole Smart City by Global-best Brain Storm Optimization with Differential Evolution Strategies

摘要

This paper proposes an optimization method of a whole smart city by global-best brain storm optimization with differential evolution strategies (GBSODE). The smart city optimization problem can be categorized into mixed integer nonlinear programming (MINLP) problems. Therefore, many optimization methods, especially evolutionary computation methods have been utilized. The results by the conventional methods can be improved than those by the conventional methods. Nevertheless, the results can be still improved for reducing CO2 emission or energy costs. The authors have proposed a new evolutionary computation method, called GBSODE and the GBSODE have a possibility to diverse individuals and concentrate to explore the attractive space. Considering these background, this paper proposes application of GBSODE to the optimization problem in a whole smart city. The simulation results confirm that of the proposed method can improve the most comparing with the results of the conventional, DEEPSO, Brain Storm Optimization (BSO), BSO with Differential Evolution Strategies (BSODE), and Global-best BSO (GBSO) based methods.