Optimization efficiency is a major challenge for electromagnetic (EM) device, circuit, and machine design. Although both surrogate model-assisted evolutionary algorithms (SAEAs) and parallel computing are playing important roles in addressing this challenge, there is little research that investigates their integration to benefit from both techniques. In this paper, a new method, called parallel SAEA for electromagnetic design (PSAED), is proposed. A state-of-the-art SAEA framework, surrogate model-aware evolutionary search, is used as the foundation of PSAED. Considering the landscape characteristics of EM design problems, three differential evolution mutation operators are selected and organized in a particular way. A new SAEA framework is then proposed to make use of the selected mutation operators in a parallel computing environment. PSAED is tested by a micromirror and a dielectric resonator antenna as well as four mathematical benchmark problems of various complexity. Comparisons with state-of-the-art methods verify the advantages of PSAED in terms of efficiency and optimization capacity.

Treatment with pulsed electromagnetic fields (PEMFs) is emerging as an interesting therapeutic option for patients with cancer. The literature has demonstrated that low-frequency/low-energy electromagnetic fields do not cause predictable effects on DNA; however, they can epigenetically act on gene expression. The aim of the present work was to study a possible epigenetic effect of a PEMF, mediated by miRNAs, on a human glioblastoma cell line (T98G). We tested a PEMF (maximum magnetic induction, 2 mT; frequency, 75 Hz) that has been demonstrated to induce autophagy in glioblastoma cells. In particular, we studied the effect of PEMF on the expression of genes involved in cancer progression and a promising synergistic effect with temozolomide, a frequently used drug to treat glioblastoma multiforme. We found that electromagnetic stimulation in combination with temozolomide can elicit an epigenetic pro-apoptotic effect in the chemo- and radioresistant T98G glioblastoma cell line.

This paper proposes a new benchmark for multi-objective optimization. A solution is furnished which has enabled an extensive search and reliable estimation of the shape of the Pareto front. Field uniformity and sensitivity are considered in the context of robust design. It is argued that the benchmark will provide a challenging target for new algorithms, especially those involving numerical modeling using finite-element codes where the number of objective function calls needs to be minimized for practical design processes.

We have implemented field models and performed a detailed numerical dosimetry inside our extremely-low-frequency electromagnetic bioreactor which has been successfully used inin vitroBiotechnology and Tissue Engineering researches. The numerical dosimetry permitted to map the magnetic induction field (maximum module equal to about 3.3 mT) and to discuss its biological effects in terms of induced electric currents and induced mechanical forces (compression and traction). So, in the frame of the tensegrity-mechanotransduction theory of Ingber, the study of these electromagnetically induced mechanical forces could be, in our opinion, a powerful tool to understand some effects of the electromagnetic stimulation whose mechanisms remain still elusive.

In this Letter, the micro biogeography‐inspired multi‐objective optimisation (μBiMO) method, an extension of the biogeography‐inspired multi‐objective optimisation (BiMO) algorithm, is proposed. It is suitable for solving multi‐objective optimisations in the field of industrial design because it is based on a small number of islands (hence the name μBiMO), i.e. few objective function calls are required. An analytical test function is used for evaluating the performances of the standard BiMO method and to show the need for the proposed μBiMO algorithm. To test its performances in industrial applications, an optimal shape design problem in electromagnetics, i.e. an electromagnet optimal design, has been chosen as a case study. To the best knowledge of the author, the main element of novelty of the Letter is that the BiMO algorithm has been applied for the first time to a multi‐objective design with a reduced number of islands (μBiMO). The algorithm so modified leads to a strong reduction of computational time, and, at the same time, improves the quality of results.