This article is the second part of a two-part survey series on large-scale global optimization. The first part covered two major algorithmic approaches to large-scale optimization, namely, ...decomposition methods and hybridization methods, such as memetic algorithms and local search. In this part, we focus on sampling and variation operators, approximation and surrogate modeling, initialization methods, and parallelization. We also cover a range of problem areas in relation to large-scale global optimization, such as multiobjective optimization, constraint handling, overlapping components, the component imbalance issue and benchmarks, and applications. The article also includes a discussion on pitfalls and challenges of the current research and identifies several potential areas of future research.
Scalability of optimization algorithms is a major challenge in coping with the ever-growing size of optimization problems in a wide range of application areas from high-dimensional machine learning ...to complex large-scale engineering problems. The field of large-scale global optimization is concerned with improving the scalability of global optimization algorithms, particularly, population-based metaheuristics. Such metaheuristics have been successfully applied to continuous, discrete, or combinatorial problems ranging from several thousand dimensions to billions of decision variables. In this two-part survey, we review recent studies in the field of large-scale black-box global optimization to help researchers and practitioners gain a bird's-eye view of the field, learn about its major trends, and the state-of-the-art algorithms. Part I of the series covers two major algorithmic approaches to large-scale global optimization: 1) problem decomposition and 2) memetic algorithms. Part II of the series covers a range of other algorithmic approaches to large-scale global optimization, describes a wide range of problem areas, and finally, touches upon the pitfalls and challenges of current research and identifies several potential areas for future research.
Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. The manufacturers and users of these drives are now keen to include diagnostic features in the ...software to improve salability and reliability. Apart from locating specific harmonic components in the line current (popularly known as motor current signature analysis), other signals, such as speed, torque, noise, vibration etc., are also explored for their frequency contents. Sometimes, altogether different techniques, such as thermal measurements, chemical analysis, etc., are also employed to find out the nature and the degree of the fault. In addition, human involvement in the actual fault detection decision making is slowly being replaced by automated tools, such as expert systems, neural networks, fuzzy-logic-based systems; to name a few. It is indeed evident that this area is vast in scope. Hence, keeping in mind the need for future research, a review paper describing different types of faults and the signatures they generate and their diagnostics' schemes will not be entirely out of place. In particular, such a review helps to avoid repetition of past work and gives a bird's eye view to a new researcher in this area.
Multiobjective evolutionary algorithms based on decomposition (MOEA/D) have attracted tremendous attention and achieved great success in the fields of optimization and decision-making. MOEA/Ds work ...by decomposing the target multiobjective optimization problem (MOP) into multiple single-objective subproblems based on a set of weight vectors. The subproblems are solved cooperatively in an evolutionary algorithm framework. Since weight vectors define the search directions and, to a certain extent, the distribution of the final solution set, the configuration of weight vectors is pivotal to the success of MOEA/Ds. The most straightforward method is to use predefined and uniformly distributed weight vectors. However, it usually leads to the deteriorated performance of MOEA/Ds on solving MOPs with irregular Pareto fronts. To deal with this issue, many weight vector adjustment methods have been proposed by periodically adjusting the weight vectors in a random, predefined, or adaptive way. This article focuses on weight vector adjustment on a simplex and presents a comprehensive survey of these weight vector adjustment methods covering the weight vector adaptation strategies, theoretical analyses, benchmark test problems, and applications. The current limitations, new challenges, and future directions of weight vector adjustment are also discussed.
With rising energy concerns, efficient energy conversion and storage devices are required to provide a sustainable, green energy supply. Solar cells hold promise as energy conversion devices due to ...their utilization of readily accessible solar energy; however, the output of solar cells can be non-continuous and unstable. Therefore, it is necessary to combine solar cells with compatible energy storage devices to realize a stable power supply. To this end, supercapacitors, highly efficient energy storage devices, can be integrated with solar cells to mitigate the power fluctuations. Here, we report on the development of a solar cell-supercapacitor hybrid device as a solution to this energy requirement. A high-performance, cotton-textile-enabled asymmetric supercapacitor is integrated with a flexible solar cell via a scalable roll-to-roll manufacturing approach to fabricate a self-sustaining power pack, demonstrating its potential to continuously power future electronic devices.
Identification of variable interaction is essential for an efficient implementation of a divide-and-conquer algorithm for large-scale black-box optimization. In this paper, we propose an improved ...variant of the differential grouping (DG) algorithm, which has a better efficiency and grouping accuracy. The proposed algorithm, DG2, finds a reliable threshold value by estimating the magnitude of roundoff errors. With respect to efficiency, DG2 reuses the sample points that are generated for detecting interactions and saves up to half of the computational resources on fully separable functions. We mathematically show that the new sampling technique achieves the lower bound with respect to the number of function evaluations. Unlike its predecessor, DG2 checks all possible pairs of variables for interactions and has the capacity to identify overlapping components of an objective function. On the accuracy aspect, DG2 outperforms the state-of-the-art decomposition methods on the latest large-scale continuous optimization benchmark suites. DG2 also performs reliably in the presence of imbalance among contribution of components in an objective function. Another major advantage of DG2 is the automatic calculation of its threshold parameter (\epsilon ), which makes it parameter-free. Finally, the experimental results show that when DG2 is used within a cooperative co-evolutionary framework, it can generate competitive results as compared to several state-of-the-art algorithms.
Vascular endothelial dysfunction, a characteristic of the aging process, is an important risk factor for cardiovascular disease in aging. Although, vascular inflammation and oxidative stress are ...major contributors to endothelial dysfunction in aging, the underlying mechanisms during the aging process are not fully understood. Accumulating evidence reveals that gut microbiota-dependent metabolite trimethylamine-N-oxide (TMAO) is implicated in the pathogenesis of many cardiovascular diseases. We tested the hypothesis that aging increases circulating TMAO levels, which induce vascular inflammation and oxidative stress, resulting in age-associated endothelial dysfunction. Old (22-mo-old) and young (4-mo-old) Fischer-344 rats were treated without (control) or with 1.0% 3,3-Dimethyl-1-butanol (DMB, an inhibitor of trimethylamine formation) in drinking water for 8 weeks. Compared with young control group, old control group had markedly higher plasma TMAO levels, which were reduced by DMB treatment. Endothelium-dependent relaxation of aorta in response to acetylcholine was impaired in old control group compared with young control group as indicated by decreased maximal relaxation (E
) and reduced area under the curve (AUC). E
and AUC were both normalized in old rats treated with DMB. No difference in endothelial-independent relaxation in response to sodium nitroprusside was observed among groups. Molecular studies revealed that old control group exhibits increased expression of proinflammatory cytokines and superoxide production, and decreased expression of endothelial nitric-oxide synthase (eNOS) in the aorta, all of which were restored by DMB treatment. These results suggest that aging increases circulating TMAO levels, which may impair eNOS-derived NO bioavailability by increasing vascular inflammation and oxidative stress, contributing to aging-associated endothelial dysfunction.
In this study, we investigated the pollution degree and spatial distribution of heavy metals and determined their sources in topsoil in a typical coal mine city, Lianyuan, Hunan Province, China. We ...collected 6078 soil surface samples in different land use types. And the concentrations of Zn, Cd, Cu, Hg, Pb, Sb, As, Mo, V, Mn, Fe and Cr were measured. The average contents of all heavy metals were lower than their corresponding Grade II values of Chinese Soil Quality Standard with the exception of Hg. However, average contents of twelve heavy metals, except for Mn, exceeded their background level in soils in Hunan Province. Based on one-way analysis of variance (ANOVA), the contents of Cu, Zn, Cd, Pb, Hg, Mo and V were related to the anthropogenic source and there were statistically significant differences in their concentrations among different land use patterns. The spatial variation of heavy metal was visualized by GIS. The PMF model was used to ascertain contamination sources of twelve heavy metals and apportion their source contributions in Lianyuan soils. The results showed that the source contributions of the natural source, atmospheric deposition, industrial activities and agricultural activities accounted for 33.6%, 26.05%, 23.44% and 16.91%, respectively.
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•The degree of heavy metals enrichment in soils in a typical coal mine city, Lianyuan.•The heavy metals spatial distributions were closely related with human activities.•Comparison of heavy metal contents in soils among different land use patterns.•Source apportionments of heavy metals were identified using PMF.
Main finding: Heavy metal contents were different among different land use patterns. Various sources of heavy metals in soils were determined by the PMF model.
Financial news articles are believed to have impacts on stock price return. Previous works model news pieces in bag-of-words space, which analyzes the latent relationship between word statistical ...patterns and stock price movements. However, news sentiment, which is an important ring on the chain of mapping from the word patterns to the price movements, is rarely touched. In this paper, we first implement a generic stock price prediction framework, and plug in six different models with different analyzing approaches. To take one step further, we use Harvard psychological dictionary and Loughran–McDonald financial sentiment dictionary to construct a sentiment space. Textual news articles are then quantitatively measured and projected onto the sentiment space. Instance labeling method is rigorously discussed and tested. We evaluate the models’ prediction accuracy and empirically compare their performance at different market classification levels. Experiments are conducted on five years historical Hong Kong Stock Exchange prices and news articles. Results show that (1) at individual stock, sector and index levels, the models with sentiment analysis outperform the bag-of-words model in both validation set and independent testing set; (2) the models which use sentiment polarity cannot provide useful predictions; (3) there is a minor difference between the models using two different sentiment dictionaries.
The first cooperative co-evolutionary algorithm (CCEA) was proposed by Potter and De Jong in 1994 and since then many CCEAs have been proposed and successfully applied to solving various complex ...optimization problems. In applying CCEAs, the complex optimization problem is decomposed into multiple subproblems, and each subproblem is solved with a separate subpopulation, evolved by an individual evolutionary algorithm (EA). Through cooperative co-evolution of multiple EA subpopulations, a complete problem solution is acquired by assembling the representative members from each subpopulation. The underlying divide-and-conquer and collaboration mechanisms enable CCEAs to tackle complex optimization problems efficiently, and hence CCEAs have been attracting wide attention in the EA community. This paper presents a comprehensive survey of these CCEAs, covering problem decomposition, collaborator selection, individual fitness evaluation, subproblem resource allocation, implementations, benchmark test problems, control parameters, theoretical analyses, and applications. The unsolved challenges and potential directions for their solutions are discussed.
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