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In distribution networks, the operation of two parallel transformers in small different tap positions, i.e., known as tap stagger, can provide a means of absorbing reactive power. The aggregated VAr absorption from many pairs of parallel transformers could help mitigate the high voltages in the upstream transmission system during periods of low demand. Considering distribution networks as reactive power support providers, this paper presents an optimal control strategy for the tap stagger to minimize the introduced network losses, as well as the tap switching operations. A genetic algorithm-based solution procedure is developed to find the optimal dispatch for the transformer tap positions in order to achieve the VAr absorption required by the transmission system. The proposed method is compared with two alternative control approaches based on the rule-based method and the branch-and-bound algorithm. Two practical U.K. distribution networks are modeled from real data and used to demonstrate the effectiveness of the proposed control strategy under different operating conditions. [ABSTRACT FROM PUBLISHER]

Optimization metaheuristics is a powerful way to deal with many electromagnetic optimization problems. Their main advantages are that they don’t require gradient computation, they are more likely to give a global optimum solution and have a higher degree of exploration and exploitation ability. Recently, the symbiotic organisms search (SOS) algorithm was proposed to solve single-objective optimization problems. SOS mimics the symbiotic relationship among the living beings. In order to extend the classical mono-objective SOS algorithm approach, this paper proposes a new multiobjective SOS (MOSOS) based on nondominance and crowding distance criterion. Furthermore, an improved MOSOS (IMOSOS) based on normal (Gaussian) probability distribution function also was proposed and evaluated. Results on a multiobjective constrained brushless direct current (dc) motor design show that the MOSOS and IMOSOS present promising performance. [ABSTRACT FROM AUTHOR]

While Pareto-based multiobjective optimization algorithms continue to show effectiveness for a wide range of practical problems that involve mostly two or three objectives, their limited application for many-objective problems, due to the increasing proportion of nondominated solutions and the lack of sufficient selection pressure, has also been gradually recognized. In this paper, we revive an early developed and computationally expensive strength Pareto-based evolutionary algorithm by introducing an efficient reference direction-based density estimator, a new fitness assignment scheme, and a new environmental selection strategy, for handling both multiobjective and many-objective problems. The performance of the proposed algorithm is validated and compared with some state-of-the-art algorithms on a number of test problems. Experimental studies demonstrate that the proposed method shows very competitive performance on both multiobjective and many-objective problems considered in this paper. Besides, our extensive investigations and discussions reveal an interesting finding, that is, diversity-first-and-convergence-second selection strategies may have great potential to deal with many-objective optimization. [ABSTRACT FROM AUTHOR]

Managing the aggregated demand of large heterogeneous clusters of thermostatically controlled loads (TCLs) is considered a sequential decision-making problem under uncertainty. Recent research indicates that using reduced-order models in combination with a broadcasted control signal offers a viable solution to the tradeoff between computational feasibility, and accurately describing the steady-state and transient cluster response. In this paper, we propose a novel control strategy based on tracer devices, which we define as a limited amount of virtual TCLs that represent the entire cluster of heterogeneous TCLs. These second-order model devices are identified in a nonintrusive manner, and capture both steady-state and transient population dynamics, as well as cluster heterogeneity. Additionally, the dispatch mechanism is included in the optimization, further improving the tracking performance. The parameterizable number of tracer devices enables a covering of the tradeoff domain. Both approaches have been evaluated in two scenarios. In the first small-scale scenario, improvements in price and power deviations are evaluated when using increasing numbers of tracer devices and integrating the dispatch dynamics. Results from the second large-scale scenario show that root mean square dispatch errors can be reduced by more than 10% when integrating the dispatch mechanism in the resulting high-fidelity model. [ABSTRACT FROM PUBLISHER]

In large-scale radio frequency identification (RFID)-enabled applications, we sometimes only pay attention to a small set of key tags, instead of all. This paper studies the problem of key tag population tracking, which aims at estimating how many key tags in a given set exist in the current RFID system and how many of them are absent. Previous work is slow to solve this problem due to the serious interference replies from a large number of ordinary (i.e., non-key) tags. However, time-efficiency is a crucial metric to the studied key tag tracking problem. In this paper, we propose a singleton slot-based estimator, which is time-efficient, because the RFID reader only needs to observe the status change of expected singleton slots corresponding to key tags instead of the whole time frame. In practice, the ratio of key tags to all current tags is small, because key members are usually rare. As a result, even when the whole time frame is long, the number of expected singleton slots is limited and the running of our protocol is very fast. To obtain good scalability in large-scale RFID systems, we exploit the sampling idea in the estimation process. A rigorous theoretical analysis shows that the proposed protocol can provide guaranteed estimation accuracy to end users. Extensive simulation results demonstrate that our scheme outperforms the prior protocols by significantly reducing the time cost. [ABSTRACT FROM PUBLISHER]

This paper proposes an application of an evolutionary game dynamics called the escort dynamics (ED) for the decentralized load management of plug-in electric vehicles (PEV). Different from earlier contributions, in the present approach, PEVs work together in a fair scheme in order to provide several ancillary services to the grid: Load shifting, active power balancing, and partial supply of reactive power demand on each phase of the distribution transformer. Meanwhile, batteries are guaranteed to be fully charged according to the constraints imposed by the owners. In the proposed formulation, chargers can be either three phase or single phase; however, in this paper, only three-phase chargers are considered. The key concepts behind ED, especially for escort functions, are provided at the beginning of this paper. Based on these concepts, the assumptions and analogies followed for the construction of the proposed approach are explained in detail, especially for the proposed definition of escort functions. A multipopulation scenario is proposed for the interaction of several PEVs using local ED routines. This interaction among populations follows another well-known evolutionary game dynamics called the best reply dynamics. Performance is evaluated using real data measured from a distribution transformer from the SOREA utility grid company in the region of Savoie, France. [ABSTRACT FROM AUTHOR]

Ageing concepts are very important in lifetime modeling and analysis. Monotonic failure rates are the simplest and most popular manifestations of aging properties of items in applications (IFR and DFR, accordingly). However, they are usually discussed for homogeneous populations, whereas heterogeneity can change dramatically the shape of the corresponding failure rate. In this paper, we suggest a new measure of ageing for items from heterogeneous populations based on the described process of information-based minimal repairs. We also show and illustrate, by several examples, that the conventional measure of aging based on the shape of the (population) failure rate is unrealistic in this case and can lead to wrong decisions, e.g., in burn-in procedures and optimal maintenance. [ABSTRACT FROM PUBLISHER]

The performance of mid-infrared Er3+-doped fiber lasers has dramatically improved in the last few years. In this paper, we present a numerical model that provides valuable insight into the dynamics of a dual-wavelength pumped fiber laser that can operate on the 3.5- and 2.8- \mu \textm bands. This model is a much needed tool for optimizing and understanding the performance of these laser systems. Comparisons between simulation and experimental results for three different systems are presented. [ABSTRACT FROM PUBLISHER]

In the modern world, due to an increased aging population, hand disability is becoming increasingly common. The prevalence of conditions such as stroke is placing an ever-growing burden on the limited fiscal resources of health care providers and the capacity of their physical therapy staff. As a solution, this paper presents a novel design for a wearable and adaptive glove for patients so that they can practice rehabilitative activities at home, reducing the workload for therapists and increasing the patient’s independence. As an initial evaluation of the design’s feasibility the prototype was subjected to motion analysis to compare its performance with the hand in an assessment of grasping patterns of a selection of blocks and spheres. The outcomes of this paper suggest that the theory of design has validity and may lead to a system that could be successful in the treatment of stroke patients to guide them through finger flexion and extension, which could enable them to gain more control and confidence in interacting with the world around them. [ABSTRACT FROM PUBLISHER]

The millimeter wave (mmWave) band, a prime candidate for 5G cellular networks, seems attractive for wireless energy harvesting since it will feature large antenna arrays and extremely dense base station (BS) deployments. The viability of mmWave for energy harvesting though is unclear, due to the differences in propagation characteristics, such as extreme sensitivity to building blockages. This paper considers a scenario where low-power devices extract energy and/or information from the mmWave signals. Using stochastic geometry, analytical expressions are derived for the energy coverage probability, the average harvested power, and the overall (energy-and-information) coverage probability at a typical wireless-powered device in terms of the BS density, the antenna geometry parameters, and the channel parameters. Numerical results reveal several network and device level design insights. At the BSs, optimizing the antenna geometry parameters, such as beamwidth, can maximize the network-wide energy coverage for a given user population. At the device level, the performance can be substantially improved by optimally splitting the received signal for energy and information extraction, and by deploying multi-antenna arrays. For the latter, an efficient low-power multi-antenna mmWave receiver architecture is proposed for simultaneous energy and information transfer. Overall, simulation results suggest that mmWave energy harvesting generally outperforms lower frequency solutions. [ABSTRACT FROM PUBLISHER]

Vehicle-to-vehicle (V2V) communications is an important technology in vehicular ad hoc network (VANET) to support autonomous data exchange among vehicles. Multiple V2V communications modes have been investigated for VANET, including dedicated short-range communications (DSRC) based on IEEE 802.11p and LTE-V2X, which are suitable for different packet transmission cases. In order to fully exploit the strengths of various modes, hybrid V2V communications strategies are designed in this paper, where each vehicle is allowed to choose different modes for different kinds of transmissions in separate frequency bands for transmission throughput improvement. Furthermore, since it is usually impractical to decide all the vehicles’ communications strategies globally due to high computational complexity and heavy overhead to broadcast the decisions, we model the selection of hybrid V2V communications strategies for vehicles as an evolutionary game. A strategy selection algorithm is then proposed, where each vehicle can select its hybrid V2V communications strategy locally based on its evaluation of the payoffs for different strategies and limited signalling from the base station. Simulation results demonstrate that the proposed algorithm can converge to an asymptotic stable state, which can improve the transmission throughput of vehicles, and is robust to the slight evaluation errors of payoffs and the strategy mutations of a few vehicles. [ABSTRACT FROM AUTHOR]

With the ever-increasing urbanization process, modeling people's spatiotemporal activities from their online traces has become a crucial task. State-of-the-art methods for this task rely on cross-modal embedding, which maps items from different modalities (e.g., location, time, text) into the same latent space. Despite their inspiring results, existing cross-modal embedding methods merely capture co-occurrences between items without modeling their high-order interactions. In this paper, we first construct two graphs from raw data records to represent the user interaction graph layer and activity graph layer and propose a hierarchical cross-modal embedding method that takes the high-order relationships into consideration. The key notion behind our method is a novel hierarchical embedding framework with meta-graphs connecting different layers. We introduce both inter-record and intra-record meta-graph structures, which enable learning distributed representations that preserve high-order proximities across graphs from different layers. Our empirical experiments on three real-world datasets demonstrate that our method not only outperforms state-of-the-art methods for spatiotemporal activity prediction, but also captures cross-modal proximity at a finer granularity. [ABSTRACT FROM AUTHOR]

The study of genetic variants (GVs) can help find correlating population groups and to identify cohorts that are predisposed to common diseases and explain differences in disease susceptibility and how patients react to drugs. Machine learning techniques are increasingly being applied to identify interacting GVs to understand their complex phenotypic traits. Since the performance of a learning algorithm not only depends on the size and nature of the data but also on the quality of underlying representation, deep neural networks (DNNs) can learn non-linear mappings that allow transforming GVs data into more clustering and classification friendly representations than manual feature selection. In this paper, we propose convolutional embedded networks (CEN) in which we combine two DNN architectures called convolutional embedded clustering (CEC) and convolutional autoencoder (CAE) classifier for clustering individuals and predicting geographic ethnicity based on GVs, respectively. We employed CAE-based representation learning to 95 million GVs from the ‘1000 genomes’ (covering 2,504 individuals from 26 ethnic origins) and ‘Simons genome diversity’ (covering 279 individuals from 130 ethnic origins) projects. Quantitative and qualitative analyses with a focus on accuracy and scalability show that our approach outperforms state-of-the-art approaches such as VariantSpark and ADMIXTURE. In particular, CEC can cluster targeted population groups in 22 hours with an adjusted rand index (ARI) of 0.915, the normalized mutual information (NMI) of 0.92, and the clustering accuracy (ACC) of 89 percent. Contrarily, the CAE classifier can predict the geographic ethnicity of unknown samples with an F1 and Mathews correlation coefficient (MCC) score of 0.9004 and 0.8245, respectively. Further, to provide interpretations of the predictions, we identify significant biomarkers using gradient boosted trees (GBT) and SHapley Additive exPlanations (SHAP). Overall, our approach is transparent and faster than the baseline methods, and scalable for 5 to 100 percent of the full human genome. [ABSTRACT FROM AUTHOR]

Federated Learning (FL) is a privacy-preserving collaborative learning approach that trains artificial intelligence (AI) models without revealing local datasets of the FL workers. While FL ensures the privacy of the FL workers, its performance is limited by several bottlenecks, which become significant given the increasing amounts of data generated and the size of the FL network. One of the main challenges is the straggler effects where the significant computation delays are caused by the slow FL workers. As such, Coded Federated Learning (CFL), which leverages coding techniques to introduce redundant computations to the FL server, has been proposed to reduce the computation latency. In CFL, the FL server helps to compute a subset of the partial gradients based on the composite parity data and aggregates the computed partial gradients with those received from the FL workers. In order to implement the coding schemes over the FL network, incentive mechanisms are important to allocate the resources of the FL workers and data owners efficiently in order to complete the CFL training tasks. In this paper, we consider a two-level incentive mechanism design problem. In the lower level, the data owners are allowed to support the FL training tasks of the FL workers by contributing their data. To model the dynamics of the selection of FL workers by the data owners, an evolutionary game is adopted to achieve an equilibrium solution. In the upper level, a deep learning based auction is proposed to model the competition among the model owners. [ABSTRACT FROM AUTHOR]

The $k\text {-means}$ clustering problem concerns finding a partition of the data points into $k$ clusters such that the total within-cluster squared distance is minimized. This optimization objective is non-convex, and not everywhere differentiable. In general, there exist spurious local solutions other than the global optimum. Moreover, the simplest and most popular algorithm for $k\text {-means}$ , namely Lloyd’s algorithm, generally converges to such spurious local solutions both in theory and in practice. In this paper, we investigate the structures of these spurious local solutions under a probabilistic generative model with $k$ ground truth clusters. As soon as $k=3$ , spurious local minima provably exist, even for well-separated clusters. One such local minimum puts two centers at one true cluster, and the third center in the middle of the other two true clusters. We prove that this is essentially the only type of spurious local minima under a separation condition. In particular, any local minimum solution only involves a configuration that puts multiple centers at a true cluster, and one center in the middle of multiple true clusters. Our results pertain to the $k\text {-means}$ formulation for mixtures of Gaussians or bounded distributions, and hold in the over- and under-parametrization regimes where the number of centers in $k\text {-means}$ may not equal to the number of true clusters. Our theoretical results corroborate existing empirical observations and provide justification for popular heuristics for $k\text {-means}$ clustering. [ABSTRACT FROM AUTHOR]

Air pollution and increasing traffic congestion means the current way of navigating through a city needs to be rethought. One of the possible solutions is to move away from internal combustion engines and embrace electric and hybrid vehicles. Electric Bicycles can offer an alternative to traditional modes of transport and support an environmentally friendly way to navigate an urban environment, with the benefit of encouraging physical exercise. There are still several issues that constrain a large-scale acceptance of Electric Bicycles, including the need for personalised controller strategies and the energy efficiencies. Current strategies do not include any analysis of rider’s capabilities, physiological factors or pedalling techniques. The research outlined in this paper involved 30 participants that volunteered to take part in an Incremental Sub-Maximal Ramp Test with the aim of understanding and quantifying pedalling characteristics and demonstrating that a better motor controller strategy tailored towards individual requirements is possible. Gender and Cycling Experience were the most prominent factors that differentiate the capabilities of the population. Three novel controller techniques (i.e. Fixed Percentage, Torque Filling and Real-Time Power mapping) are analysed and presented as innovative methods for next generation personalised controller strategies for Electric Bicycles. [ABSTRACT FROM AUTHOR]

Researches have shown difficulties in obtaining proximity while maintaining diversity for many-objective optimization problems. Complexities of the true Pareto front pose challenges for the reference vector-based algorithms for their insufficient adaptability to the diverse characteristics with no priori. This paper proposes a many-objective optimization algorithm with two interacting processes: cascade clustering and reference point incremental learning (CLIA). In the population selection process based on cascade clustering (CC), using the reference vectors provided by the process based on incremental learning, the nondominated and the dominated individuals are clustered and sorted with different manners in a cascade style and are selected by round-robin for better proximity and diversity. In the reference vector adaptation process based on reference point incremental learning, using the feedbacks from the process based on CC, proper distribution of reference points is gradually obtained by incremental learning. Experimental studies on several benchmark problems show that CLIA is competitive compared with the state-of-the-art algorithms and has impressive efficiency and versatility using only the interactions between the two processes without incurring extra evaluations. [ABSTRACT FROM AUTHOR]

A physically unclonable function (PUF) is a circuit of which the input–output behavior is designed to be sensitive to the random variations of its manufacturing process. This building block hence facilitates the authentication of any given device in a population of identically laid-out silicon chips, similar to the biometric authentication of a human. The focus and novelty of this paper is the development of efficient impersonation attacks on the following five Arbiter PUF–based authentication protocols: 1) the so-called Poly PUF protocol of Konigsmark et al. as published in the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems in 2016; 2) the so-called OB-PUF protocol of Gao et al. as presented at the IEEE Conference PerCom 2016; 3) the so-called RPUF protocol of Ye et al. as presented at the IEEE Conference AsianHOST 2016; 4) the so-called LHS-PUF protocol of Idriss and Bayoumi as presented at the IEEE Conference RFID-TA 2017; and 5) the so-called PUF–FSM protocol of Gao et al. as published in the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems in 2018. The common flaw of all five designs is that the use of lightweight obfuscation logic provides insufficient protection against machine-learning attacks. [ABSTRACT FROM AUTHOR]

The increasing availability and adoption of shared vehicles as an alternative to personally owned cars presents ample opportunities for achieving more efficient transportation in cities. With private cars spending on the average over 95% of the time parked, one of the possible benefits of shared mobility is the reduced need for parking space. While widely discussed, a systematic quantification of these benefits as a function of mobility demand and sharing models is still mostly lacking in the literature. As a first step in this direction, this paper focuses on a type of private mobility which, although specific, is a major contributor to traffic congestion and parking needs, namely, home–work commuting. We develop a data-driven methodology for estimating commuter parking needs in different shared mobility models, including a model where self-driving vehicles are used to partially compensate flow imbalance typical of commuting, and further reduce parking infrastructure at the expense of the increased traveled kilometers. We consider the city of Singapore as a case study and produce very encouraging results showing that the gradual transition to shared mobility models will bring tangible reductions in parking infrastructure. In the future-looking, self-driving vehicle scenario, our analysis suggests that up to 50% reduction in parking needs can be achieved at the expense of the increasing total traveled kilometers of less than 2%. [ABSTRACT FROM AUTHOR]

As a consequence of industrial development, large quantities of hazardous materials are shipped everyday. Most related studies consider the risks as fixed weights on edges. However, the loads of the vehicles are important factors about the risks. Thus, considering the risks as fixed parameters cannot reflect actual practice. In addition, the vehicle routing models in previous studies simultaneously minimize the total transportation cost and the total risk. However, simply considering the total risk of a fleet as a whole cannot evaluate each vehicle's risk. Although the total risk is low, the risk of a specific vehicle may still be very high. In this paper, the actual load dependent risk is introduced, and a corresponding bi-objective vehicle routing model for hazardous materials transportation is developed, minimizing the maximum risk of vehicles and the total transportation cost simultaneously. An exact algorithm is developed based on the $\varepsilon $ -constraint method with several improvements. For large-scale problems, an approximation approach is proposed. Computational experiments on a series of instances are reported and several managerial insights are derived from the sensitivity analysis. [ABSTRACT FROM AUTHOR]

The development of a trip distribution model requires a massive data collection task, such as expensive travel surveys to identify trip makers’ origin and destination zones. Intra-zonal trips are usually ignored in the development of trip distribution models because of the difficulty associated with measuring travel costs. However, ignoring intra-zonal trips leads to incomplete model estimation especially when the zone size is large, and thus, the number of intra-zonal trips is substantial. This is especially important, with the growing interest around the world on creating more walkable and bikeable cities where a high share of those trips is intra-zonal. In this paper, we use mobile phone data to derive country-wide mobility trends. A set of doubly constrained trip distribution models that integrates intra-zonal trips was estimated for 123-district-level traffic analysis zones. We present two approaches to estimate the intra-zonal travel costs measured in terms of trip distance. Our analysis reveals that the average intra-zonal trip distances obtained from the two approaches provide different levels of sensitivity to the distance-decay effect. Our result also shows that model estimation in the absence of intra-zonal trips produces biased estimates. [ABSTRACT FROM AUTHOR]

The dimensioning of the prime mover of a wave energy converter (WEC) is a key step in the entire WEC design process. This paper presents an automatic optimization methodology that focuses on the preliminary dimensioning of a point absorber WEC. By means of the proposed methodology, the engineering problem of dimensioning a WEC becomes a mathematical optimization problem. The methodology is based on a multi-objective differential evolutionary algorithm that optimizes the geometry of a heaving two-body point absorber WEC where the power take-off (PTO), its control, and location are preselected. The algorithm considers two cost functions (WEC surface and annual energy extraction) and several constraints related to PTO characteristics or wave climate. This paper proposes a case study based on a real WEC development project in order to present the methodology. In addition, the impact on the results of the definition of the optimization problem is evaluated. The changes in cost functions and constraints evaluated have led to different geometry solutions. This automatic methodology could be easily integrated in the entire WEC design process, providing a set of properly selected base cases to focus on the detail engineering, among other potential applications. [ABSTRACT FROM AUTHOR]

Band selection refers to finding the most representative channels from hyperspectral images. Usually, certain objective functions are designed and combined via regularization terms. A possible drawback of these methods is that they can only generate one solution in a single run with a given band number. To overcome this problem, multiobjective (MO)-based methods, which were able to simultaneously obtain a series of subsets with different band numbers, were investigated for band selection. However, because the range of band selection problem is discrete, recently proposed weighted Tchebycheff (WT)-based MO methods may suffer weakly Pareto optimal problem. In this case, the solutions for each band number will be nonunique and no optimal solution exists. Decision makers have to manually select a unique solution for each band number. In this paper, we provide a theoretical analysis about the weakly Pareto optimal problem in band selection, and quantitatively give the boundary conditions. Moreover, we further summarize the suggestions which will help users avoid the weakly Pareto optimal problem. According to these criteria, we develop a new adaptive-penalty-based boundary intersection (APBI) framework to improve the MO algorithm in hyperspectral band selection. APBI mainly includes two advantages: 1) avoiding weakly Pareto optimum and 2) reducing the sensibility of the penalty factor. The theoretical analysis is further validated by contrast experiments. The results demonstrate that the weakly Pareto optimal solutions really exist in WT methods, while APBI can overcome this problem. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have been the popular targets for cyberattacks these days. One type of network topology for WSNs, the scale-free topology, can effectively withstand random attacks in which the nodes in the topology are randomly selected as targets. However, it is fragile to malicious attacks in which the nodes with high node degrees are selected as targets. Thus, how to improve the robustness of the scale-free topology against malicious attacks becomes a critical issue. To tackle this problem, this paper proposes a Robustness Optimization scheme with multi-population Co-evolution for scale-free wireless sensor networKS (ROCKS) to improve the robustness of the scale-free topology. We build initial scale-free topologies according to the characteristics of WSNs in the real-world environment. Then, we apply our ROCKS with novel crossover operator and mutation operator to optimize the robustness of the scale-free topologies constructed for WSNs. For a scale-free WSNs topology, our proposed algorithm keeps the initial degree of each node unchanged such that the optimized topology remains scale-free. Based on a well-known metric for the robustness against malicious attacks, our experiment results show that ROCKS roughly doubles the robustness of initial scale-free WSNs, and outperforms two existing algorithms by about 16% when the network size is large. [ABSTRACT FROM AUTHOR]

With a global search mechanism, particle swarm optimization (PSO) has shown promise in feature selection (FS). However, most of the current PSO-based FS methods use a fix-length representation, which is inflexible and limits the performance of PSO for FS. When applying these methods to high-dimensional data, it not only consumes a significant amount of memory but also requires a high computational cost. Overcoming this limitation enables PSO to work on data with much higher dimensionality which has become more and more popular with the advance of data collection technologies. In this paper, we propose the first variable-length PSO representation for FS, enabling particles to have different and shorter lengths, which defines smaller search space and therefore, improves the performance of PSO. By rearranging features in a descending order of their relevance, we facilitate particles with shorter lengths to achieve better classification performance. Furthermore, using the proposed length changing mechanism, PSO can jump out of local optima, further narrow the search space and focus its search on smaller and more fruitful area. These strategies enable PSO to reach better solutions in a shorter time. Results on ten high-dimensional datasets with varying difficulties show that the proposed variable-length PSO can achieve much smaller feature subsets with significantly higher classification performance in much shorter time than the fixed-length PSO methods. The proposed method also outperformed the compared non-PSO FS methods in most cases. [ABSTRACT FROM AUTHOR]

In multiobjective optimization, defining a good solution is a multifactored process. Most existing evolutionary multi- or many-objective optimization (EMO) algorithms have utilized two factors: 1) domination and 2) crowding levels of each solution. Although these two coarse-grained factors are found to be adequate in many EMO algorithms, their relative importance in an algorithm has been a matter of great concern to many current studies. We argue that beside these issues, other more fine-grained factors are of importance. For example, since extreme objective-wise solutions are important in establishing a noise-free and stable normalization process, reaching extreme solutions is more crucial than finding other solutions. In this paper, we propose an integrated algorithm, B-NSGA-III, that produces much better convergence and diversity preservation. For this purpose, in addition to emphasizing extreme objective-wise solutions, B-NSGA-III tries to find solutions near intermediate undiscovered regions of the front. B-NSGA-III addresses critical algorithmic issues of convergence and diversity-preservation directly through recent progresses in literature and integrates all these critical fine-grained factors seamlessly in an alternating phases scheme. The proposed algorithm is shown to perform better than a number of commonly used existing methods. [ABSTRACT FROM AUTHOR]

This paper suggests a novel clustering-based evolutionary algorithm for many-objective optimization problems. Its main idea is to classify the population into a number of clusters, which is expected to solve the difficulty of balancing convergence and diversity in high-dimensional objective space. The individuals showing high similarities on the vector angles are gathered into the same cluster, such that the population’s distribution can be well portrayed by the clusters. To efficiently find these clusters, partitional clustering is first used to classify the union population into ${m}$ main clusters based on the ${m}$ axis vectors (${m}$ is the number of objectives), and then hierarchical clustering is further run on these ${m}$ main clusters to get ${N}$ final clusters (${N}$ is the population size and ${N>m}$). At last, in environmental selection, one individual from each of ${N}$ clusters closest to the axis vectors is selected to maintain diversity, while one individual from each of the other clusters is preferred by a simple convergence indicator to ensure convergence. When tackling some well-known test problems with 5–15 objectives, extensive experiments validate the superiority of our algorithm over six competitive many-objective EAs, especially on problems with incomplete and irregular Pareto-optimal fronts. [ABSTRACT FROM AUTHOR]

Tissue homeostasis (feedback control) is an important mechanism that regulates the population of different cell types within a tissue. In type-1 diabetes, auto-immune attack and consequent death of pancreatic β cells result in the failure of homeostasis and loss of organ function. Synthetically engineered adult stem cells with homeostatic control based on digital logic have been proposed as a solution for regenerating β cells. Such previously proposed homeostatic control circuits have thus far been unable to reliably control both stem-cell proliferation and stem-cell differentiation. Using analog circuits and feedback systems analysis, we have designed an in silico circuit that performs homeostatic control by utilizing a novel scheme with both symmetric and asymmetric division of stem cells. The use of a variety of feedback systems analysis techniques, which is common in analog circuit design, including root-locus techniques, Bode plots of feedback-loop frequency response, compensation techniques for improving stability, and robustness analysis help us choose design parameters to meet desirable specifications. For example, we show that lead compensation in analog circuits instantiated as an incoherent feed-forward loop in the biological circuit improves stability, whereas simultaneously reducing steady-state tracking error. Our symmetric and asymmetric division scheme also improves phase margin in the feedback loop, and thus improves robustness. This paper could be useful in porting an analog-circuit design framework to synthetic biological applications of the future. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach to erythropoietin dosing for anemia management in patients with chronic kidney disease is introduced. A weighted constrained quadratic programming problem is proposed to recursively estimate patient models providing better estimation results than standard system identification methods. The constrained optimization modeling method provides a stable autoregressive with exogenous inputs model that mimics parameter patterns learned from a more complex nonlinear physiologically based model. Zone model predictive control (ZMPC) is compared with standard MPC, digital PID controllers, and economic ZMPC for computer simulations using a designed patient simulator. The results show that weighted recursive ZMPC offers the most desirable controller characteristics while offering significant improvements over the proprietary anemia management protocol used for comparison in the computer simulations. [ABSTRACT FROM AUTHOR]

Both convergence and diversity are crucial to evolutionary many-objective optimization, whereas most existing dominance relations show poor performance in balancing them, thus easily leading to a set of solutions concentrating on a small region of the Pareto fronts. In this paper, a novel dominance relation is proposed to better balance convergence and diversity for evolutionary many-objective optimization. In the proposed dominance relation, an adaptive niching technique is developed based on the angles between the candidate solutions, where only the best converged candidate solution is identified to be nondominated in each niche. Experimental results demonstrate that the proposed dominance relation outperforms existing dominance relations in balancing convergence and diversity. A modified NSGA-II is suggested based on the proposed dominance relation, which shows competitiveness against the state-of-the-art algorithms in solving many-objective optimization problems. The effectiveness of the proposed dominance relation is also verified on several other existing multi- and many-objective evolutionary algorithms. [ABSTRACT FROM AUTHOR]

When solving constrained multiobjective optimization problems, an important issue is how to balance convergence, diversity, and feasibility simultaneously. To address this issue, this paper proposes a parameter-free constraint handling technique, a two-archive evolutionary algorithm, for constrained multiobjective optimization. It maintains two collaborative archives simultaneously: one, denoted as the convergence-oriented archive (CA), is the driving force to push the population toward the Pareto front; the other one, denoted as the diversity-oriented archive (DA), mainly tends to maintain the population diversity. In particular, to complement the behavior of the CA and provide as much diversified information as possible, the DA aims at exploring areas under-exploited by the CA including the infeasible regions. To leverage the complementary effects of both archives, we develop a restricted mating selection mechanism that adaptively chooses appropriate mating parents from them according to their evolution status. Comprehensive experiments on a series of benchmark problems and a real-world case study fully demonstrate the competitiveness of our proposed algorithm, in comparison to five state-of-the-art constrained evolutionary multiobjective optimizers. [ABSTRACT FROM AUTHOR]

Through introducing the divide-and-conquer strategy, cooperative co-evolution (CC) has been successfully employed by many evolutionary algorithms (EAs) to solve large-scale optimization problems. In practice, it is common that different subcomponents of a large-scale problem have imbalanced contributions to the global fitness. Thus, how to utilize such imbalance and concentrate efforts on optimizing important subcomponents becomes an important issue for improving performance of cooperative co-EA, especially in distributed computing environment. In this paper, we propose a two-layer distributed CC (dCC) architecture with adaptive computing resource allocation for large-scale optimization. The first layer is the dCC model which takes charge of calculating the importance of subcomponents and accordingly allocating resources. An effective allocating algorithm is designed which can adaptively allocate computing resources based on a periodic contribution calculating method. The second layer is the pool model which takes charge of making fully utilization of imbalanced resource allocation. Within this layer, two different conformance policies are designed to help optimizers use the assigned computing resources efficiently. Empirical studies show that the two conformance policies and the computing resource allocation algorithm are effective, and the proposed distributed architecture possesses high scalability and efficiency. [ABSTRACT FROM AUTHOR]

The physical unclonable function (PUF) based on resistive random-access memory (RRAM) possesses a distinctive advantage that can offer higher security and lower cost than the traditional complementary metal–oxide–semiconductor-based cryptographic devices and other conventional PUFs. The intrinsic stochasticity of RRAM devices successfully provides attractive properties to implement PUF. In this paper, we present a novel multistate-based RRAM PUF to realize strong tolerance against attack. By applying multilevel states with bit shuffling to the RRAM PUF, the randomness and uniqueness were enhanced close to ideal values. In addition, the bit error rate was dramatically reduced using the temperature compensation mechanism. Moreover, our new method not only enables the generation of larger challenge–response pairs (CRPs) with less footprint but also can reconfigure CRPs. [ABSTRACT FROM AUTHOR]

This paper develops a decentralized competitive charging coordination algorithm for a large population of plug-in electric vehicles (PEVs) using the concept of a mean-field (MF) game. The aim of each PEV is to find its optimal charging strategy by minimizing an objective function consisting of charging cost, battery degradation cost, and benefit from charging, subject to the input and state constraints. The strategy of a PEV affects the objective functions of other PEVs through the electricity price, and therefore, we can model the interactions among PEVs as a game problem. No information exchange is considered among the PEVs. Each PEV only sends its own control action value to a population coordinator, and the coordinator just broadcasts a common signal to all the PEVs. This common signal is an estimate of the average control actions of PEVs and is called the MF term. Utilizing an adjustment mechanism for the MF term, a decentralized MF-optimal control algorithm is proposed, and it is shown that the algorithm converges to the $\varepsilon _N$ -Nash equilibrium point of the game, with $\varepsilon _N$ uniformly converging to zero as the population sizes of the PEVs go to infinity. Simulation results and comparison with other methods are performed to clarify the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

Metaheuristics, being tailored to each particular domain by experts, have been successfully applied to many computationally hard optimization problems. However, once implemented, their application to a new problem domain or a slight change in the problem description would often require additional expert intervention. There is a growing number of studies on reusable cross-domain search methodologies, such as selection hyper-heuristics, which are applicable to problem instances from various domains, requiring minimal expert intervention or even none. This paper introduces a new learning automata-based selection hyper-heuristic controlling a set of multiobjective metaheuristics. The approach operates above three well-known multiobjective evolutionary algorithms and mixes them, exploiting the strengths of each algorithm. The performance and behavior of two variants of the proposed selection hyper-heuristic, each utilizing a different initialization scheme are investigated across a range of unconstrained multiobjective mathematical benchmark functions from two different sets and the real-world problem of vehicle crashworthiness. The empirical results illustrate the effectiveness of our approach for cross-domain search, regardless of the initialization scheme, on those problems when compared to each individual multiobjective algorithm. Moreover, both variants perform significantly better than some previously proposed selection hyper-heuristics for multiobjective optimization, thus significantly enhancing the opportunities for improved multiobjective optimization. [ABSTRACT FROM AUTHOR]

The current shift toward the virtualization of network infrastructure components enables a dynamic instantiation, deployment and configuration of virtual network functions (VNFs), which can be offered "as-a-service" to multiple tenants, thus enabling 5G architectures. Simultaneously, the recent high throughput satellite (HTS) systems can play an important role in the 5G era thanks to their characteristics, such as their large coverage, fast deployment of the ground infrastructure and native broadcast/multicast broadband capabilities. In this context, this paper proposes a review of the satellite service delivery models in order to identify viable alternatives to deploy converged satellite-terrestrial services. This objective is pursued by taking as a reference a satellite-assisted IP streaming service for the enhancement of current Content Delivery Network (CDN) infrastructures, as tackled by the European Space Agency within the SHINE ("Secure Hybrid In Network caching Environment") project. SHINE aims at efficiently extending terrestrial CDN services to satellite-enabled scenarios, by designing innovative mechanisms for the secure distribution of real-time multimedia information across hybrid channels, leveraging both unicast and multicast communication paradigms. The original contribution of the paper is the analysis of satellite architectures and configurations tailored to efficiently support the SHINE solution, together with a high-level applicability assessment taking into account different satellite-enabled service models. [ABSTRACT FROM AUTHOR]

Multimodal optimization problem (MMOP), which targets at searching for multiple optimal solutions simultaneously, is one of the most challenging problems for optimization. There are two general goals for solving MMOPs. One is to maintain population diversity so as to locate global optima as many as possible, while the other is to increase the accuracy of the solutions found. To achieve these two goals, a novel dual-strategy differential evolution (DSDE) with affinity propagation clustering (APC) is proposed in this paper. The novelties and advantages of DSDE include the following three aspects. First, a dual-strategy mutation scheme is designed to balance exploration and exploitation in generating offspring. Second, an adaptive selection mechanism based on APC is proposed to choose diverse individuals from different optimal regions for locating as many peaks as possible. Third, an archive technique is applied to detect and protect stagnated and converged individuals. These individuals are stored in the archive to preserve the found promising solutions and are reinitialized for exploring more new areas. The experimental results show that the proposed DSDE algorithm is better than or at least comparable to the state-of-the-art multimodal algorithms when evaluated on the benchmark problems from CEC2013, in terms of locating more global optima, obtaining higher accuracy solution, and converging with faster speed. [ABSTRACT FROM AUTHOR]

Trust and trustworthiness are of great importance in social and human systems, especially when considering managerial and economic decision-making. In this paper, we investigate the emergent dynamics of an evolutionary game-theoretic model—the ${N}$ -player evolutionary trust game—consisting of three types of players: 1) an investor; 2) a trustee who is trustworthy; and 3) a trustee who is untrustworthy. Here, we limit the interactions between players to local neighborhoods defined by a specific spatial topology or social network. Players are able to adjust their game-playing strategies using an evolutionary update rule based on the payoffs obtained by their neighbors. Through comprehensive simulation experiments, we find that trust can be promoted when players interact on a social network despite a substantial number of untrustworthy individuals in the initial population. These results differ from findings reported for an unstructured population of the same game, where the existence of a single untrustworthy individual would eliminate trust completely. We compare the dynamics of the model with different social network densities and structures (e.g., from regular lattices to scale-free and random networks). We observe that the levels of trust vary under different network structures, and the level is correlated with how “difficult” the game is. When game conditions are easy (i.e., low temptation to defect and/or almost no initial untrustworthy trustees), homogeneous networks with higher densities can promote higher levels of trust. However, when the game becomes harder, heterogeneous social networks with lower densities are able to promote higher levels of trust and global net wealth. [ABSTRACT FROM AUTHOR]

Dynamic multi-objective optimization problems (DMOPs) not only involve multiple conflicting objectives, but these objectives may also vary with time, raising a challenge for researchers to solve them. This paper presents a cooperative co-evolutionary strategy based on environment sensitivities for solving DMOPs. In this strategy, a new method that groups decision variables is first proposed, in which all the decision variables are partitioned into two subcomponents according to their interrelation with environment. Adopting two populations to cooperatively optimize the two subcomponents, two prediction methods, i.e., differential prediction and Cauchy mutation, are then employed respectively to speed up their responses on the change of the environment. Furthermore, two improved dynamic multi-objective optimization algorithms, i.e., DNSGAII-CO and DMOPSO-CO, are proposed by incorporating the above strategy into NSGA-II and multi-objective particle swarm optimization, respectively. The proposed algorithms are compared with three state-of-the-art algorithms by applying to seven benchmark DMOPs. Experimental results reveal that the proposed algorithms significantly outperform the compared algorithms in terms of convergence and distribution on most DMOPs. [ABSTRACT FROM AUTHOR]

Automatic drowsiness detection system plays a vital role to prevent the road accidents caused by drowsiness. In this regard, the electroencephalogram (EEG) signal provides valuable information of brain physiology for detection of drowsiness. EEG signals exhibit non-stationary nature which is tough to explore by prior defined and fixed number of basis functions. In this paper, an adaptive Hermite decomposition is proposed for the detection of drowsiness EEG signals. In the proposed decomposition, Hermite functions are used as basis functions, which are adaptively selected for each EEG signal by evolutionary optimization algorithms (EOAs). The mean square error of decomposition is proposed as an objective function to EOAs. The minimum decomposition error provided artificial bee colony EOA is considered to Hermite coefficients-based feature extraction for EEG signals. The extracted features are tested with the extreme learning machine (ELM), decision tree, $k$ -nearest neighbor, least squares support vector machine, artificial neural network, and naive Bayes for detection of alertness and drowsiness EEG signals. The proposed method with ELM classifier obtained 95.45% and 87.92% detection rates for alertness and drowsiness states, respectively, which are better as compared to other existing methods. [ABSTRACT FROM AUTHOR]

Contribution: This paper shows that participating in a year-round program for African–American male middle school students (ages 10–14) can positively impact their attitudes toward STEM, their confidence in problem solving and team work, and their interest in STEM careers, but may not impact the interest in taking STEM classes to the same extent. Background: Increasing the number of under-represented minorities pursuing degrees in science and engineering through formal and informal learning activities has been a focus in the United States for many years. Efforts to diversify the engineering workforce have often targeted students in high school and college (ages 15–22), with varying success. Intended Outcomes: This program was designed to increase participants’ content knowledge, interest in STEM courses, interest in STEM careers, interest in attending college, and improve their attitudes about STEM. Application Design: Approaches used included: 1) introducing software tools that encouraged critical thinking, creativity and independent learning; 2) integrating engineering and software design processes; 3) providing projects that were age- and culturally-appropriate; and 4) using ethnically matched mentors. Findings: After almost two years in the program participants show improved attitudes toward STEM, more interest and career choices in computing and engineering, and increased interest in attending college. These findings suggested that summer and academic year programs can be effective for cultivating interest in computing and engineering careers, but their impact on interest in studying related subjects may need further study. [ABSTRACT FROM AUTHOR]

Broadband penetration in rural areas of the developing countries is significantly low. The unique challenges in enabling rural connectivity are sparsity of population locations and modest income of the villagers, which induce low return on investment to the conventional cellular network providers. In this paper, we propose a novel cluster-based network architecture and protocols for efficient rural broadband coverage which requires minimal infrastructure setup by the service provider. Multiple customer premise equipments (CPEs) in a village form clusters and transmit collaboratively over unused television bands to the base station. A two-tier uplink access protocol is proposed and its performance in terms of network throughput and energy efficiency are obtained analytically. The cluster size is optimized to maximize the uplink network throughput and energy-efficiency. A distributed clustering algorithm is proposed for the CPEs to form clusters, while channels are allocated to the clusters using the proposed channel allocation algorithm to minimize inter-cluster interference. Via network simulation studies we demonstrate that the proposed architecture can be cost-effective and energy-efficient, while being scalable at the same time. [ABSTRACT FROM AUTHOR]

Genetic programming (GP) is one of the commonly used tools for symbolic regression. In the field of GP, the use of semantics and an external library of subexpressions for designing better search operators has recently gained significant attention. A notable example is semantic backpropagation, which has demonstrated an ability to obtain expressions with extremely small prediction errors. However, these expressions often tend to be long and difficult to interpret, which may restrict their applicability in real-life problems. In this paper, we propose a GP framework that includes two key elements, a new library construction scheme and a novel semantic operator based on mixed-integer linear programming (MILP). The proposed library construction scheme maintains diverse subexpressions and keeps the library size in check by imposing an upper limit. The proposed semantic operator constructs new expressions by effectively combining a given number of subexpressions from the library. These improvements have been integrated in a bi-objective GP framework with random desired operator (RDO), which attempts to simultaneously reduce the complexity and improve the fitness of the evolving expressions. The contributions of individual components are studied in detail using 15 benchmarks. It is observed that the use of the proposed scheme with RDO leads to shorter expressions without sacrificing accuracy of approximation. The addition of MILP further improves the results for certain types of problems. [ABSTRACT FROM AUTHOR]

Recently, by taking advantage of evolutionary multiobjective optimization techniques in diversity preservation, the means of multiobjectivization has attracted increasing interest in the studies of multimodal optimization (MMO). While most existing work of multiobjectivization aims to find all optimal solutions simultaneously, in this paper, we propose to approximate multimodal fitness landscapes via multiobjectivization, thus providing an estimation of potential optimal areas. To begin with, an MMO problem is transformed into a multiobjective optimization problem (MOP) by adding an adaptive diversity indicator as the second optimization objective, and an approximate fitness landscape is obtained via optimization of the transformed MOP using a multiobjective evolutionary algorithm. Then, on the basis of the approximate fitness landscape, an adaptive peak detection method is proposed to find peaks where optimal solutions may exist. Finally, local search is performed inside the detected peaks on the approximate fitness landscape. To assess the performance of the proposed algorithm, extensive experiments are conducted on 20 multimodal test functions, in comparison with three state-of-the-art algorithms for MMO. Experimental results demonstrate that the proposed algorithm not only shows promising performance in benchmark comparisons, but also has good potential in assisting preference-based decision-making in MMO. [ABSTRACT FROM AUTHOR]

In our present society, the cinema has become one of the major forms of entertainment providing unlimited contexts of emotion elicitation for the emotional needs of human beings. Since emotions are universal and shape all aspects of our interpersonal and intellectual experience, they have proved to be a highly multidisciplinary research field, ranging from psychology, sociology, neuroscience, etc., to computer science. However, affective multimedia content analysis work from the computer science community benefits but little from the progress achieved in other research fields. In this paper, a multidisciplinary state-of-the-art for affective movie content analysis is given, in order to promote and encourage exchanges between researchers from a very wide range of fields. In contrast to other state-of-the-art papers on affective video content analysis, this work confronts the ideas and models of psychology, sociology, neuroscience, and computer science. The concepts of aesthetic emotions and emotion induction, as well as the different representations of emotions are introduced, based on psychological and sociological theories. Previous global and continuous affective video content analysis work, including video emotion recognition and violence detection, are also presented in order to point out the limitations of affective video content analysis work. [ABSTRACT FROM AUTHOR]

The most common scheme for estimating the orientation field of space-varying directional textures is based on a local nonlinear spatial averaging of the gradient field. This leads to locally biased orientations, especially in regions of nonlinearly distributed convergence, asymmetrically distributed curvature, or geometry superposition. In this paper, we propose an orientation estimation framework that is invariant toward the local geometry. Instead of applying the spatial averaging in the initial space, it is performed in a flattened space which is obtained from a parametric space transformation model deduced from the reconstruction of local hypersurfaces. The flattening can either be applied to the initial gradient field or to the intensity image, on which the flattened gradient field is computed. The process is iterated in order to define more accurate space transformations leading to refined orientation estimation. The computational efficiency is improved without noticeable loss of orientation accuracy by a patch-based approach. Experiments, both on synthetic as well as real-life fingerprint and fibrous material images, exhibit enhanced performance in comparison with conventional methods such as the structure tensor method. [ABSTRACT FROM AUTHOR]

We improve the quality of cryptographically privacy-preserving genome-wide association studies by correctly handling population stratification—the inherent genetic difference of patient groups, e.g., people with different ancestries. Our approach is to use principal component analysis to reduce the dimensionality of the problem so that we get less spurious correlations between traits of interest and certain positions in the genome. While this approach is commonplace in practical genomic analysis, it has not been used within a privacy-preserving setting. In this paper, we use cryptographically secure multi-party computation to tackle principal component analysis, and present an implementation and experimental results showing the performance of the approach. [ABSTRACT FROM AUTHOR]

Thermostatically controlled loads (TCLs) represent a valuable source of flexibility for the system. Depending on network needs, these devices could alter their nominal energy consumption and provide multiple ancillary services, facilitating the cost-effective transition to a low-carbon power system. Previous work mainly focused on investigating single service provision from TCLs, while intersections among different services have not been considered. Furthermore, the intrinsic energy payback effect was not fully included within optimisation tools for TCL scheduling. This paper presents a novel demand side response model (DSRM), which enables the optimal scheduling of energy/power consumption of a heterogeneous population of TCLs and the simultaneous allocation of multiple ancillary services. The model explicitly considers the effect of the energy recovery after delivering the services so that the deliverability of scheduled services from TCLs is always guaranteed. The proposed DSRM is integrated into an advanced stochastic unit commitment model to investigate the system benefits of the flexibility from TCLs. Case studies demonstrate that: 1) time-varying provision of multiple services from TCLs significantly increases their benefits; 2) TCL operation which aims to minimise the amplitude of the energy recovery causes sub-optimal utilisation of the devices; and 3) ignoring the energy payback leads to overestimate the TCL value. [ABSTRACT FROM AUTHOR]

Group testing refers to the process of testing pooled samples to reduce the total number of tests. Given the current pandemic, and the shortage of test supplies for COVID-19, group testing can play a critical role in time and cost efficient diagnostics. In many scenarios, samples collected from users are also accompanied with auxiliary information (such as demographics, history of exposure, onset of symptoms). Such auxiliary information may differ across patients, and is typically not considered while designing group testing algorithms. In this paper, we abstract such heterogeneity using a model where the population can be categorized into clusters with different prevalence rates. The main result of this work is to show that exploiting knowledge heterogeneity can further improve the efficiency of group testing. Motivated by the practical constraints and diagnostic considerations, we focus on two-stage group testing algorithms, where in the first stage, the goal is to detect as many negative samples by pooling, whereas the second stage involves individual testing to detect any remaining samples. For this class of algorithms, we prove that the gain in efficiency is related to the concavity of the number of tests as a function of the prevalence. We also show how one can choose the optimal pooling parameters for one of the algorithms in this class, namely, doubly constant pooling. We present lower bounds on the average number of tests as a function of the population heterogeneity profile, and also provide numerical results and comparisons. [ABSTRACT FROM AUTHOR]

Transcriptomic profiling plays an important role in post-genomic analysis. Especially, the single-cell RNA-seq technology has advanced our understanding of gene expression from cell population level into individual cell level. Many computational methods have been proposed to decipher transcriptomic profiles from those RNA-seq data. However, most of the related algorithms suffer from realistic restrictions such as high dimensionality and premature convergence. In this paper, we propose and formulate an evolutionary multiobjective blind compressed sensing (EMOBCS) to address those problems for evolving transcriptomic profiles from single-cell RNA-seq data. In the proposed framework, to characterize various gene expression profile models, two objective functions including chi-squared kernel score and euclidean distance of different gene expression profiles are formulated. After that, multiobjective blind compressed sensing based on artificial bee colony is designed to optimize the two objective functions on single-cell RNA-seq data by proposing a rank probability model and two new search strategies into the cooperative convolution framework in an unbiased manner. To demonstrate its effectiveness, extensive experiments have been conducted, comparing the proposed algorithm with 14 algorithms including eight state-of-the-art algorithms and six different EMOBCS algorithms under different search strategies on 10 single-cell RNA-seq datasets and one case study. The experimental results reveal that the proposed algorithm is better than or comparable with those compared algorithms. Furthermore, we also conduct the time complexity analysis, convergence analysis, and parameter analysis to demonstrate various properties of EMOBCS. [ABSTRACT FROM AUTHOR]