The computation of image segmentation has become more complicated with the increasing number of thresholds, and the option and application of the thresholds in image thresholding fields have become an NP problem at the same time. The paper puts forward the modified discrete grey wolf optimizer algorithm (MDGWO), which improves on the optimal solution updating mechanism of the search agent by the weights. Taking Kapur’s entropy as the optimized function and based on the discreteness of threshold in image segmentation, the paper firstly discretizes the grey wolf optimizer (GWO) and then proposes a new attack strategy by using the weight coefficient to replace the search formula for optimal solution used in the original algorithm. The experimental results show that MDGWO can search out the optimal thresholds efficiently and precisely, which are very close to the result examined by exhaustive searches. In comparison with the electromagnetism optimization (EMO), the differential evolution (DE), the Artifical Bee Colony (ABC), and the classical GWO, it is concluded that MDGWO has advantages over the latter four in terms of image segmentation quality and objective function values and their stability.

Rapid economic development has been observed worldwide, which has caused environmental problems to worsen. Thus, the Energy Internet (EI), which accesses renewable energy and provides high-quality power services, has recently become a hot issue. As a subnet of the EI, an energy local area network (ELAN) consists of renewable power generation equipment, controllable distributed power generation equipment, storage systems, electric vehicles, and a large number of loads. Energy management is required for economic, environmental, and safety considerations. This paper proposes an energy management optimization model that addresses ELAN operations and includes pollution treatment fees; this model provides intelligent control of the charging and discharging of plug-in hybrid electric vehicles (PHEVs). This model achieves a nonlinear energy management optimization for an ELAN. To promote optimal performance, an improved comprehensive learning particle swarm optimization (CLPSO) algorithm is presented; it combines Tabu Search (TS) and CLPSO to avoid local optima. To verify the performance of our model, two experimental scenarios are built. The simulation results show that our energy management optimization model fulfills the optimal allocation of energy and that the PHEV intelligent charging/discharging strategy promotes economic benefits for the network.

With the rapid development of society and the economy and the increasingly serious environmental problems, low-carbon communities accessing renewable energy and providing high-quality energy services have become popular research topics. However, a large number of volatile distributed generation (DG) power systems in the community are connected to the grid. It is difficult to stabilize and efficiently interact with fragmented and isolated energy management, and it is difficult to meet the low-carbon, stability and intelligent needs of energy management. Therefore, considering the operation cost, the pollution control cost, energy stability and plug-in hybrid electric vehicles (PHEVs), this paper proposes a new regional energy supply model-community energy Internet-and builds a low-carbon community energy adaptive management model for smart services. Then, based on the problem of energy supply instability, an adaptive feedback control mechanism developed on model predictive control (MPC) is introduced to adapt to the changing environment. Finally, a LSTM-RNN based tabu search (TS) is introduced, which overcomes the defect that the multi-objective particle swarm optimization (MOPSO) algorithm easily falls into a local optimum. The simulation results show that the proposed model can effectively realize the optimal allocation of energy, which effectively solves the problem of fragmented energy islands caused by distributed power access. It creates quality of service (QoS) benefits for users, such as cost, time and stability, and realizes wide interconnections, high intelligence and low-carbon efficiency of community energy management.