The objective of this study is to prepare a bio-based and lightweight electromagnetic interference (EMI) shielding material in the range of 8-12 GHz. Organic castor oil-based polyurethane (PU) foam was synthesized by the mechanical stirrer mixing process, whereas absorption and hydrothermal reduction processes have been used to reinforce the multi-walled carbon nanotube (MWCNT), cupric oxide (CuO) and bamboo charcoal (BC) nanoparticles in the organic PU foam. The EMI shielding properties of the PU foam composite were tested using a vector analyzer test setup. Identification of the structural property of the nanocomposite was analyzed using field-emission scanning electron microscopy images. The density of the organic PU foam composite reinforced with nanoparticles was calculated with the help of mass and volume. Response surface methodology has been used to systematically design and analyze the experiments of EMI shielding effectiveness (EMI SE) and the physical properties of the reinforced foam. Using the EMI SE experimental results, mathematical models were developed to forecast the results and validate them with error estimation. An optimization study has revealed that 0.75 wt.% of MWCNT, 1.5 wt.% of CuO, and 1.5 wt.% of BC are the optimum parameters with 0.063750 g/cm 3 density for obtaining the maximum EMI SE.
As an influential technology of swarm evolutionary computing (SEC), the particle swarm optimization (PSO) algorithm has attracted extensive attention from all walks of life. However, how to rationally and effectively utilize the population resources to equilibrate the exploration and utilization is still a key dispute to be resolved. In this paper, we propose a novel PSO algorithm called Chaos Adaptive Particle Swarm Optimization (CAPSO), which adaptively adjust the inertia weight parameter w and acceleration coefficients c 1 , c 2 , and introduces a controlling factor γ based on chaos theory to adaptively adjust the range of chaotic search. This makes the algorithm have favorable adaptability, and then the particles can not only effectively prevent missing the global optimal solution, but also have a high probability of jumping out of the local optimal solution. To verify the stability, convergence speed, and accuracy of CAPSO, we conduct ample experiments on 6 test functions. In addition, to further verify the effectiveness and scalability of CAPSO, comparative experiments are carried out on the CEC2013 test suite. Finally, the results prove that CAPSO outperforms other peer algorithms to achieve satisfactory performance.
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