Abstract:In this paper, a multi-objective hybrid firefly and particle swarm optimization (MOHFPSO) was proposed for different multi-objective optimal power flow (MOOPF) problems. Optimal power flow (OPF) was formulated as a non-linear problem with various objectives and constraints. Pareto optimal front was obtained by using non-dominated sorting and crowding distance methods. Finally, an optimal compromised solution was selected from the Pareto optimal set by applying an ideal distance minimization method. The efficiency of the proposed MOHFPSO technique was tested on standard IEEE 30-bus and IEEE 57-bus test systems with various conflicting objectives. Simulation results were also compared with non-dominated sorting based multi-objective particle swarm optimization (MOPSO) and different optimization algorithms reported in the current literature. The achieved results revealed the potential of the proposed algorithm for MOOPF problems.Keywords: optimal power flow; multi-objective optimization; non-dominated sorting; ideal distance minimization; total fuel cost minimization; voltage profile enhancement; real power loss minimization; hybrid firefly and particle swarm optimization
Laser guided control drug delivery using MoS2 for cancer treatment. a Schematic illustration of high-throughput synthesis of MoS2-CS nanosheets as an NIR photothermal-triggered drug delivery system for efficient cancer therapy. (i, ii) Oleum treatment exfoliation process to produce single-layer MoS2 nanosheets that are then modified with CS, (iii) DOX loading process, and (iv) NIR photothermal-triggered drug delivery of the MoS2 nanosheets to the tumor site. b Release profile of DOX in PBS buffer (pH 5.00) in the absence and presence of an 808-nm NIR laser. c Fluorescence images of KB cells treated with free DOX, MoS2-CS-DOX, and MoS2-CS-DOX under 808-nm NIR irradiation (inset: high magnification of the rectangle area)181
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This is defined as incorporating new information into a plain DL model, made possible by interfering with the learned information. For instance, consider a case where there are 1000 types of flowers and a model is trained to classify these flowers, after which a new type of flower is introduced; if the model is fine-tuned only with this new class, its performance will become unsuccessful with the older classes [183, 184]. The logical data are continually collected and renewed, which is in fact a highly typical scenario in many fields, e.g. Biology. To address this issue, there is a direct solution that involves employing old and new data to train an entirely new model from scratch. This solution is time-consuming and computationally intensive; furthermore, it leads to an unstable state for the learned representation of the initial data. At this time, three different types of ML techniques, which have not catastrophic forgetting, are made available to solve the human brain problem founded on the neurophysiological theories [185, 186]. Techniques of the first type are founded on regularizations such as EWC [183] Techniques of the second type employ rehearsal training techniques and dynamic neural network architecture like iCaRL [187, 188]. Finally, techniques of the third type are founded on dual-memory learning systems [189]. Refer to [190,191,192] in order to gain more details.
To obtain well-trained models that can still be employed productively, DL models have intensive memory and computational requirements due to their huge complexity and large numbers of parameters [193, 194]. One of the fields that is characterized as data-intensive is the field of healthcare and environmental science. These needs reduce the deployment of DL in limited computational-power machines, mainly in the healthcare field. The numerous methods of assessing human health and the data heterogeneity have become far more complicated and vastly larger in size [195]; thus, the issue requires additional computation [196]. Furthermore, novel hardware-based parallel processing solutions such as FPGAs and GPUs [197,198,199] have been developed to solve the computation issues associated with DL. Recently, numerous techniques for compressing the DL models, designed to decrease the computational issues of the models from the starting point, have also been introduced. These techniques can be classified into four classes. In the first class, the redundant parameters (which have no significant impact on model performance) are reduced. This class, which includes the famous deep compression method, is called parameter pruning [200]. In the second class, the larger model uses its distilled knowledge to train a more compact model; thus, it is called knowledge distillation [201, 202]. In the third class, compact convolution filters are used to reduce the number of parameters [203]. In the final class, the information parameters are estimated for preservation using low-rank factorization [204]. For model compression, these classes represent the most representative techniques. In [193], it has been provided a more comprehensive discussion about the topic.
Opposite to the vanishing problem is the one related to gradient. Specifically, large error gradients are accumulated during back-propagation [216,217,218]. The latter will lead to extremely significant updates to the weights of the network, meaning that the system becomes unsteady. Thus, the model will lose its ability to learn effectively. Grosso modo, moving backward in the network during back-propagation, the gradient grows exponentially by repetitively multiplying gradients. The weight values could thus become incredibly large and may overflow to become a not-a-number (NaN) value. Some potential solutions include:
In addition to the computational load cost, the memory bandwidth and capacity have a significant effect on the entire training performance, and to a lesser extent, deduction. More specifically, the parameters are distributed through every layer of the input data, there is a sizeable amount of reused data, and the computation of several network layers exhibits an excessive computation-to-bandwidth ratio. By contrast, there are no distributed parameters, the amount of reused data is extremely small, and the additional FC layers have an extremely small computation-to-bandwidth ratio. Table 3 presents a comparison between different aspects related to the devices. In addition, the table is established to facilitate familiarity with the tradeoffs by obtaining the optimal approach for configuring a system based on either FPGA, GPU, or CPU devices. It should be noted that each has corresponding weaknesses and strengths; accordingly, there are no clear one-size-fits-all solutions.
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