Enhancing Cooperative Coevolution for Large Scale Optimization by Adaptively Constructing Surrogate Models

It has been shown that cooperative coevolution (CC) can effectively deal with large scale optimization problems (LSOPs) through a divide-and-conquer strategy. However, its performance is severely restricted by the current context-vector-based sub-solution evaluation method since this method needs to access the original high dimensional simulation model when evaluating each sub-solution and thus requires many computation resources. To alleviate this issue, this study proposes an adaptive surrogate model assisted CC framework. This framework adaptively constructs surrogate models for different sub-problems by fully considering their characteristics. For the single dimensional sub-problems obtained through decomposition, accurate enough surrogate models can be obtained and used to find out the optimal solutions of the corresponding sub-problems directly. As for the nonseparable sub-problems, the surrogate models are employed to evaluate the corresponding sub-solutions, and the original simulation model is only adopted to reevaluate some good sub-solutions selected by surrogate models. By these means, the computation cost could be greatly reduced without significantly sacrificing evaluation quality. Empirical studies on IEEE CEC 2010 benchmark functions show that the concrete algorithm based on this framework is able to find much better solutions than the conventional CC algorithms and a non-CC algorithm even with much fewer computation resources.Comment: arXiv admin note: text overlap with arXiv:1802.0974

The presentation and regulation of the IL-8 network in the epithelial cancer stem-like cell niche in patients with colorectal cancer

Background: Accumulative evidence suggests that the biological behavior of cancer stem-like cells (CSCs) is regulated by their surrounding niche, in which cytokines function as one of the main mediators for the interaction between CSCs and their microenvironment in the colorectal cancer (CRC). Methods: We characterized the presentation of CSCs and the interleukin (IL)− 8 network in the adenoma/CRC epithelium using quantitative real-time PCR (q-PCR), immunohistochemistry (IHC) and double immunofluorescence. In addition, the capacity of IL-1β to stimulate epithelial IL-8 production in colon cancer Caco-2 cells was examined in vitro and the IL-8 product was measured by enzyme-linked immunosorbent assay (ELISA). Results: IHC observation showed increased expression of both CSCs and IL-8 in the adenoma and CRC epithelium, and q-PCR results revealed that increased expression of IL-1β transcript was strongly correlated with increased IL-8 transcript levels in both adenoma and CRC tissues. Double immunofluorescence images demonstrated the coexpression of the IL-8 receptors IL-8RA and IL-8RB with LGR5 labeled CSCs in CRC tissue sections. Consistently, in vitro experiments showed that coculture of Caco-2 cells with IL-1β at concentrations of 1, 5, 10 and 20 ng/ml resulted in a dose-dependent release of IL-8, which could be specifically inhibited by cotreatment with the IL-1β receptor antagonist. Conclusions: These results demonstrate activation of the IL-8 network in the niche of CSCs from the precancerous adenoma stage to the CRC stage, which is potentially stimulated by IL-1β in CRC cells

Exploring links between industrialization, urbanization, and Chinese inflammatory bowel disease

publishedVersio

Temporal and spatial changes of cells positive for stem-like markers in different compartments and stages of human colorectal adenoma-carcinoma sequence

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Pattern Classification and PSO Optimal Weights Based Sky Images Cloud Motion Speed Calculation Method for Solar PV Power Forecasting

The motion of cloud over a photovoltaic (PV) power station will directly cause the change of solar irradiance, which indirectly affects the prediction of minute-level PV power. Therefore, the calculation of cloud motion speed is very crucial for PV power forecasting. However, due to the influence of complex cloud motion process, it is very difficult to achieve accurate result using a single traditional algorithm. In order to improve the computation accuracy, a pattern classification and particle swarm optimization optimal weights based sky images cloud motion speed calculation method for solar PV power forecasting (PCPOW) is proposed. The method consists of two parts. First, we use a k-means clustering method and texture features based on a gray-level co-occurrence matrix to classify the clouds. Second, for different cloud classes, we build the corresponding combined calculation model to obtain cloud motion speed. Real data recorded at Yunnan Electric Power Research Institute are used for simulation; the results show that the cloud classification and optimal combination model are effective, and the PCPOW can improve the accuracy of displacement calculation.© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed