Search

Deep learning for electroencephalogram-based classification is confronted with data scarcity, due to the time-consuming and expensive data collection procedure. Data augmentation has been shown as an effective way to improve data efficiency. In addition, contrastive learning has recently been shown to hold great promise in learning effective representations without human supervision, which has the potential to improve the electroencephalogram-based recognition performance with limited labeled data. However, heavy data augmentation is a key ingredient of contrastive learning. In view of the limited number of sample-based data augmentation in electroencephalogram processing, three methods, performance-measure-based time warp, frequency noise addition and frequency masking, are proposed based on the characteristics of electroencephalogram signal. These methods are parameter learning free, easy to implement, and can be applied to individual samples. In the experiment, the proposed data augmentation methods are evaluated on three electroencephalogram-based classification tasks, including situation awareness recognition, motor imagery classification and brain-computer interface steady-state visually evoked potentials speller system. Results demonstrated that the convolutional models trained with the proposed data augmentation methods yielded significantly improved performance over baselines. In overall, this work provides more potential methods to cope with the problem of limited data and boost the classification performance in electroencephalogram processing. [ABSTRACT FROM AUTHOR]

Hospitals can predetermine the admission rate and facilitate resource allocation based on valid emergency requests and bed capacity estimation. The excess unoccupied beds can be determined with the help of forecasting the number of discharged patients. Extracting predictive features and mining the temporal patterns from historical observations are crucial for accurate and reliable forecasts. Machine learning algorithms have demonstrated the ability to learn temporal knowledge and make predictions for unseen inputs. This paper utilizes several machine learning algorithms to forecast the inpatient discharges of Singapore hospitals and compare them with statistical methods. A novel ensemble deep learning algorithm based on random vector functional links is established to predict inpatient discharges. The ensemble deep learning framework is optimized in a greedy layer-wise fashion. Several forecasting metrics and statistical tests are utilized to demonstrate the proposed method’s superiority. The proposed algorithm statistically outperforms the benchmark with a ranking of 1.875. Finally, practical implications and future directions are discussed. [ABSTRACT FROM AUTHOR]

The widespread usage of Android-powered devices in the Internet of Things (IoT) makes them susceptible to evolving cybersecurity threats. Most healthcare devices in IoT networks, such as smart watches, smart thermometers, biosensors, and more, are powered by the Android operating system, where preserving the privacy of user-sensitive data is of utmost importance. Detecting Android malware is thus vital for protecting sensitive information and ensuring the reliability of IoT networks. This article focuses on AI-enabled Android malware detection for improving zero trust security in IoT networks, which requires Android applications to be verified and authenticated before providing access to network resources. The zero trust security model requires strict identity verification for every entity trying to access resources on a private network, regardless of whether they are inside or outside the network perimeter. Our proposed solution, DP-RFECV-FNN, an innovative approach to Android malware detection that employs Differential Privacy (DP) within a Feedforward Neural Network (FNN) designed for IoT networks under the zero trust model. By integrating DP, we ensure the confidentiality of data during the detection process, setting a new standard for privacy in cybersecurity solutions. By combining the strengths of DP and zero trust security with the powerful learning capacity of the FNN, DP-RFECV-FNN demonstrates the ability to identify both known and novel malware types and achieves higher accuracy while maintaining strict privacy controls compared with recent papers. DP-RFECV-FNN achieves an accuracy ranging from 97.78% to 99.21% while utilizing static features and 93.49% to 94.36% for dynamic features of Android applications to detect whether it is malware or benign. These results are achieved under varying privacy budgets, ranging from ϵ = 0. 1 to ϵ = 1. 0. Furthermore, our proposed feature selection pipeline enables us to outperform the state-of-the-art by significantly reducing the number of selected features and training time while improving accuracy. To the best of our knowledge, this is the first work to categorize Android malware based on both static and dynamic features through a privacy-preserving neural network model. [ABSTRACT FROM AUTHOR]

In this study, we propose an efficient optimization algorithm that is a hybrid of the iterated greedy and simulated annealing algorithms (hereinafter, referred to as IGSA) to solve the flexible job shop scheduling problem with crane transportation processes (CFJSP). Two objectives are simultaneously considered, namely, the minimization of the maximum completion time and the energy consumptions during machine processing and crane transportation. Different from the methods in the literature, crane lift operations have been investigated for the first time to consider the processing time and energy consumptions involved during the crane lift process. The IGSA algorithm is then developed to solve the CFJSPs considered. In the proposed IGSA algorithm, first, each solution is represented by a 2-D vector, where one vector represents the scheduling sequence and the other vector shows the assignment of machines. Subsequently, an improved construction heuristic considering the problem features is proposed, which can decrease the number of replicated insertion positions for the destruction operations. Furthermore, to balance the exploration abilities and time complexity of the proposed algorithm, a problem-specific exploration heuristic is developed. Finally, a set of randomly generated instances based on realistic industrial processes is tested. Through comprehensive computational comparisons and statistical analyses, the highly effective performance of the proposed algorithm is favorably compared against several efficient algorithms. Note to Practitioners—The flexible job shop scheduling problem (FJSP) can be extended and applied to many types of practical manufacturing processes. Many realistic production processes should consider the transportation procedures, especially for the limited crane resources and energy consumptions during the transportation operations. This study models a realistic production process as an FJSP with crane transportation, wherein two objectives, namely, the makespan and energy consumptions, are to be simultaneously minimized. This study first considers the height of the processing machines, and therefore, the crane lift operations and lift energy consumptions are investigated. A hybrid iterated greedy algorithm is proposed for solving the problem considered, and several problem-specific heuristics are embedded to balance the exploration and exploitation abilities of the proposed algorithm. In addition, the proposed algorithm can be generalized to solve other types of scheduling problems with crane transportations. [ABSTRACT FROM AUTHOR]

In power systems, optimal power flow (OPF) is a complex and constrained optimization problem in which quite often multiple and conflicting objectives are required to be optimized. The traditional way of dealing with multi-objective OPF (MOOPF) is the weighted sum method which converts the multi-objective OPF into a single-objective problem and provides a single solution from the set of Pareto solutions. This paper presents MOOPF study applying multi-objective evolutionary algorithm based on decomposition (MOEA/D) where a set of non-dominated solutions (Pareto solutions) can be obtained in a single run of the algorithm. OPF is formulated with two or more objectives among fuel (generation) cost, emission, power loss and voltage deviation. The other important aspect in OPF problem is about satisfying power system constraints. As the search process adopted by evolutionary algorithms is unconstrained, for a constrained optimization problem like OPF, static penalty function approach has been extensively employed to discard infeasible solutions. This approach requires selection of a suitable penalty coefficient, largely done by trial-and-error, and an improper selection may often lead to violation of system constraints. In this paper, an effective constraint handling method, superiority of feasible solutions (SF), is used in conjunction with MOEA/D to handle network constraints in MOOPF study. The algorithm MOEA/D-SF is applied to standard IEEE 30-bus and IEEE 57-bus test systems. Simulation results are analyzed, especially for constraint violation and compared with recently reported results on OPF. [ABSTRACT FROM AUTHOR]

In recent years, several population-based evolutionary and swarm algorithms have been developed and used in the literature. This work introduces an improved Cultural Algorithm with a modified selection function and a dynamic α -cognition procedure to handle a variety of challenging numerical optimization problems. The modified selection function is used to support a balanced evolutionary search. A process that starts with a clearer exploration early in the search process and gradually begins to focus on exploitation towards the end of the search process. This work uses the elites of each knowledge source that are at a certain distance from each other. The dynamic α -cognition procedure assists in providing effective learning of individuals through preserving the diversity of the population during the evolution process. In this procedure, each individual is able to learn from the top α % individuals controlled by its knowledge source in the belief space, where the proportion of the affecting subpopulation (α) is adaptively modified during the evolution. The performance of the proposed work has been evaluated on the CEC'2010 and CEC'2013 benchmark suites developed for the special sessions on large-scale global optimization problems. An appropriate comparative study with the best results in the literature is presented. The results confirm how the merits of the improved Cultural Algorithm can achieve superior performance over other cutting-edge algorithms for these data sets. • m CA- α C is an enhanced version of CAs with a modified selection function. • CAs uses a dynamic α -cognition procedure that is proposed to introduce effective learning of the individuals though preserving the diversity of the population during the evolution process. • m CA- α C performs competitively against other variants and state-of-the-art algorithms from the literature. • m CA- α C is employed to effectively solve a variety of challenging large scale numerical optimization problems. [ABSTRACT FROM AUTHOR]

In recent years, the research community has witnessed an explosion of literature dealing with the mimicking of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques. [ABSTRACT FROM AUTHOR]

Abstract Optimal active–reactive power dispatch problems (OARPD) are non-convex and highly nonlinear complex optimization problems. Typically, such problems are expensive in terms of computational time and cost due to the load variations over the scheduling period. The conventional constraint-based solvers that are generally used to tackle such problems require a considerable high budget and may not provide high quality solutions. In the last decade, complexity of OARPD has further increased due to the incorporation of renewable energy sources such as: wind, solar and small-hydro generators. More specifically, the incorporation of renewable sources introduces uncertainty in generation on top of the load variations in conventional OARPD, making the problem more complicated. Recently, Differential Evolution (DE) is viewed as an excellent algorithm to solve OARPD problems, due to its effectiveness to optimize the objective function which is subject to many operational constraints. A new efficient Differential Evolution algorithm, denoted as DE a -AR, is propounded to solve the contemporary stochastic optimal power flow OARPD problems considering the renewable generators. DE a -AR uses arithmetic recombination crossover and adapts the scaling factor based on Laplace distribution. In addition, an efficient archive strategy that acts as a corresponding image of the population and stores the inferior individuals for later use, is also incorporated. The target behind using this strategy is to consider the information of inferior individuals as a direction toward finding new good solutions. The IEEE 57-bus system is used to evaluate the OARPD problems with different stochastic scenarios based on different probability distributions employed to model parameters of renewable energy sources. The performance of the proposed work is compared with other state-of-the-art algorithms. Simulation results indicate that the proposed technique can solve the OARPD problems with renewable sources effectively and can provide high quality solutions. The proposed algorithm is ranked the first with a Friedman rank equals to 1.8333 with a clear statistical significant difference compared with the most recent studies on the used problems. Highlights • A new efficient differential evolution algorithm is propounded to solve OPF problems. • The contemporary stochastic OPF with renewable generators is considered. • The performance is compared with the best performing algorithms. • The simulation results indicate that the new technique can provide competitive performance. [ABSTRACT FROM AUTHOR]

Many parameter adaptation methods were proposed for Differential Evolution (DE) algorithm. Although these methods succeed in enhancing the performance of DE when solving a diverse set of optimization problems, locating the optimal solution is still a challenging task in most of these methods for complex optimization problems. To improve the performance of DE, this study presents a new enhanced algorithm based on our published work namely LSHADE with ensemble parameter sinusoidal adaptation, LSHADE-EpSin, which ranked the joint winner in IEEE CEC2016 competition on real-parameter single objective optimization. The method proposes a mixture of two sinusoidal formulas and a Cauchy distribution to balance the exploration and the exploitation of already found best solutions. A restart method is used at later generations to enhance the quality of the found solutions. The proposed algorithm also introduces a novel approach to adapt the population size by using a niching-based reduction scheme. In this mechanism, two separate niches are used before performing the population reduction, to reduce the population size in an effective manner. The proposed algorithm namely ensemble sinusoidal differential evolution with niching reduction, E s DE r -NR, is tested on the IEEE CEC2014 problems used in the special session and competitions on real-parameter single objective optimization of the IEEE CEC2016. The results statistically affirm the efficiency of the proposed approach to obtain better results compared to the other state-of-the-art algorithms from the literature including CMA-ES variants. [ABSTRACT FROM AUTHOR]

Portfolio optimization problems involve selection of different assets to invest in order to maximize the overall return and minimize the overall risk simultaneously. The complexity of the optimal asset allocation problem increases with an increase in the number of assets available to select from for investing. The optimization problem becomes computationally challenging when there are more than a few hundreds of assets to select from. To reduce the complexity of large-scale portfolio optimization, two asset preselection procedures that consider return and risk of individual asset and pairwise correlation to remove assets that may not potentially be selected into any portfolio are proposed in this paper. With these asset preselection methods, the number of assets considered to be included in a portfolio can be increased to thousands. To test the effectiveness of the proposed methods, a Normalized Multiobjective Evolutionary Algorithm based on Decomposition (NMOEA/D) algorithm and several other commonly used multiobjective evolutionary algorithms are applied and compared. Six experiments with different settings are carried out. The experimental results show that with the proposed methods the simulation time is reduced while return-risk trade-off performances are significantly improved. Meanwhile, the NMOEA/D is able to outperform other compared algorithms on all experiments according to the comparative analysis. [ABSTRACT FROM AUTHOR]

Transfer Optimization has gained a remarkable attention from the Swarm and Evolutionary Computation community in the recent years. It is undeniable that the concepts underlying Transfer Optimization are formulated on solid grounds. However, evidences observed in recent contributions confirm that there are critical aspects that are not properly addressed to date. This short communication aims to engage the readership around a reflection on these issues, and to provide rationale why they remain unsolved. Specifically, we emphasize on three critical points of Evolutionary Multitasking Optimization: (i) the plausibility and practical applicability of this paradigm; (ii) the novelty of some proposed multitasking methods; and (iii) the methodologies used for evaluating newly proposed multitasking algorithms. As a result of this research, we conclude that some important efforts should be directed by the community in order to keep the future of this promising field on the right track. Our ultimate purpose is to unveil gaps in the current literature, so that prospective works can attempt to fix these gaps, avoiding to stumble on the same stones and eventually achieve valuable advances in the area. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is a simple, yet very effective, population-based search technique. However, it is challenging to maintain a balance between exploration and exploitation behaviors of the DE algorithm. In this paper, we boost the population diversity while preserving simplicity by introducing a multi-population DE to solve large-scale global optimization problems. In the proposed algorithm, called m DE- b ES, the population is divided into independent subgroups, each with different mutation and update strategies. A novel mutation strategy that uses information from either the best individual or a randomly selected one is used to produce quality solutions to balance exploration and exploitation. Selection of individuals for some of the tested mutation strategies utilizes fitness-based ranks of these individuals. Function evaluations are divided into epochs. At the end of each epoch, individuals between the subgroups are exchanged to facilitate information exchange at a slow pace. The performance of the algorithm is evaluated on a set of 19 large-scale continuous optimization problems. A comparative study is carried out with other state-of-the-art optimization techniques. The results show that m DE- b ES has a competitive performance and scalability behavior compared to the contestant algorithms. [ABSTRACT FROM AUTHOR]

Wind speed forecasting is challenging due to its intermittent nature. The wind speed time series (TS) has nonlinear and nonstationary characteristics and not normally distributed, which make it difficult to be predicted by statistical or computational intelligent methods. Empirical mode decomposition (EMD) and its improved versions are powerful tools to decompose a complex TS into a collection of simpler ones. The improved versions discussed in this paper include ensemble EMD (EEMD), complementary EEMD (CEEMD), and complete EEMD with adaptive noise (CEEMDAN). The EMD and its improved versions are hybridized with two computational intelligence-based predictors: support vector regression (SVR) and artificial neural network (ANN). The EMD-based hybrid forecasting methods are evaluated with 12 wind speed TS. The performances of the hybrid methods are compared and discussed. It shows that EMD and its improved versions enhance the performance of SVR significantly but marginally on ANN, and among the EMD-based hybrid methods, the proposed CEEMDAN-SVR is the best method. Possible future works are also recommended for wind speed forecasting. [ABSTRACT FROM PUBLISHER]