Nature-Inspired Algorithm Applied to a Renewable Energy-Integrating Hydro-Thermal Power Plant

Due to the rising requirement on energy sources and the global doubts for using fossil fuel because of its consequences on the climate changes and the global warming caused by hazardous gases, the scientific research has shifted to the renewable energy. To minimize the usage of thermal power generation plants and to meet the rising load demand, a thermal-integrated wind-hydro-system is taking an important role in renewable power systems. A proficient nature-inspired optimization is proposed for solving economic and emission dispatch for the hydro-thermal-wind (HTW) scheduling problem. Further, the opposition-based learning have been incorporated with the chemical reaction optimization for improving the performance of the algorithm. To investigate the performance of oppositional chemical reaction optimization algorithm, the algorithm is tested on two different cases. Along with this, some statistical tests have also been performed. The results obtained by the OCRO algorithm are compared with other recently proposed methods to establish its robustness.

Implementation of bi-directional searching Chemical Reaction Optimization (CRO) algorithm towards achieving cutting tool indexing time minimization in Automatic Tool Changer(ATC)

Determining the optimum cutting tool positions in a tool magazine is a challenging task which involves a huge searching space. Reduction of manufacturing time even by nanoseconds will highly be beneficial for all process industries. The prime focus of this research work is to reduce the total turret or tool indexing time in Automatic Tool Changer (ATC) which in turn curtails the idle non-cutting time of the bi-directional turret or tool magazine in a CNC machine. This variant of optimization problem falls under the category of non-deterministic hard polynomial (NP-hard) problem. Chemical Reaction Optimization (CRO) algorithm is proposed to determine the near optimal/global optimal solution with lesser computational cost. Five benchmark case studies are considered in this work to assess the robustness of the proposed algorithm. From the results, it is evident that the proposed algorithm yields solutions which matches or exceeds with the solution quality.

Study of Node Distribution and Density Optimization in Mobile Sensor Network 3D Space

Mobile sensor network is applied in information collection in emergencies. As the mobile sensor network in real environment is widely deployed with different height and the redundancy of the sensor node needs to be as low as possible, therefore, it is necessary to effectively deploy mobile sensor nodes in the 3D space to have reasonable layout and optimized density. To this end, we established the optimization model of mobile sensor network deployment and solved the model with chemical reaction optimization (CRO). The experimental results have shown that compared with traditional particle swarm optimization (PSO), CRO algorithm can achieve reasonable deployment more rapidly and enhance the network performance evaluation value effectively. The reasonable deployment of mobile sensor network node is very significant to information collecting, postperiod decision-making, and rapid rescuing work in emergencies.