Nature-inspired optimization is a modern technique in the past decades. Researchers report their successful applications in various fields such as manufacturing, biomedical, and environmental engineering, while other researchers doubt its applicability. In this paper, we collect newly emerging nature-inspired optimization algorithms proposed after 2008, present them in a unified way, implement them, and evaluate them on benchmark functions. Moreover, we optimize the behavioural parameters for these algorithms. Since it is impossible to cover all interesting topics regarding nature-inspired optimization, this paper only focuses on the continuous encoding algorithms for single objective global problems, which is fundamental for other related topics.

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Abstract:In this paper, a second-order Hash retrieval approach is proposed based on SIFT feature of pictures and applied to search similar images. Firstly, extract features of an image by the method of SIFT. Then, cluster the key words through K-Means algorithm and create a word frequency table of the features by utilizing bag of word algorithm. Finally, match familiar images by the method of second-order Hash retrieval algorithm based on the word frequency table. The second-order Hash retrieval algorithm includes two steps. The first-order Hash retrieval aims to search similarities of feature distribution structure. And the second-order Hash retrieval implements accurate search, which depends on the ratio of the quantity of the two images' features belong to the same feature category to the total feature points of the image itself. The experiment results indicate that this approach performs well on accuracy and efficiency.

Text clustering is an effective approach to collect and organize text documents into meaningful groups for mining valuable information on the Internet. However, there exist some issues to tackle such as feature extraction and data dimension reduction. To overcome these problems, we present a novel approach named deep-learning vocabulary network. The vocabulary network is constructed based on related-word set, which contains the “cooccurrence” relations of words or terms. We replace term frequency in feature vectors with the “importance” of words in terms of vocabulary network and PageRank, which can generate more precise feature vectors to represent the meaning of text clustering. Furthermore, sparse-group deep belief network is proposed to reduce the dimensionality of feature vectors, and we introduce coverage rate for similarity measure in Single-Pass clustering. To verify the effectiveness of our work, we compare the approach to the representative algorithms, and experimental results show that feature vectors in terms of deep-learning vocabulary network have better clustering performance.

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