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Most of the existing node importance ranking methods only study the topology of the network, ignoring the attribute information contained in the network nodes themselves. Attribute information is very important, but there is widespread incompleteness. Incomplete attribute information is closely related to the importance of nodes. Aiming at these problems, a network node importance analysis method based on superior rough set theory and TOPSIS method is proposed, which combines network structure characteristics and node attribute information and overcomes the limitations of single topological structure analysis. Finally, the proposed method is applied to the user importance evaluation in Weibo social network and compared with other methods. The results show that the ranking results of this method can well synthesize the importance of each node in attribute information and structural characteristics and can fully reflect the importance of each node. [ABSTRACT FROM AUTHOR]

Ｔｈｅ ｃｏｒａｌ ｒｅｅｆ ａｌｇｏｒｉｔｈｍ ｈａｓ ｔｈｅ ｄｉｓａｄｖａｎｔａｇｅｓ ｏｆ ｂｅｉｎｇ ｅａｓｙ ｔｏ ｆａｌｌ ｉｎｔｏ ｌｏｃａｌ ｏｐｔｉｍｕｍ ａｎｄ ｌｏｗ ｐｒｅｃｉｓｉｏｎ. Ｉｎ ｖｉｅｗ ｏｆ ｔｈｅ ｓｈｏｒｔｃｏｍｉｎｇｓ ｏｆ ｃｏｒａｌ ｒｅｅｆ ａｌｇｏｒｉｔｈｍꎬ ｔｈｉｓ ｐａｐｅｒ ｐｒｏｐｏｓｅｓ ａｎ ｉｍｐｒｏｖｅｄ ｃｏｒａｌ ｒｅｅｆ ａｌｇｏｒｉｔｈｍ. Ｂｙ ｒｅｆｅｒｒｉｎｇ ｔｏ ｐａｒｔｉｃｌｅ ｓｗａｒｍ ｏｐｔｉｍｉｚａｔｉｏｎꎬ Ｇａｕｓｓｉａｎ ｍｕｔａｔｉｏｎ ａｎｄ ｓｉｍｕｌａｔｅｄ ａｎｎｅａｌｉｎｇ ａｌｇｏｒｉｔｈｍꎬ ｔｈｉｓ ａｌｇｏｒｉｔｈｍ ｉｍｐｒｏｖｅｓ ｔｈｅ ｂｒｏａｄｃａｓｔ ｓｐａｗｎｉｎｇꎬ ａｓｅｘｕａｌ ｒｅｐｒｏｄｕｃｔｉｏｎ ａｎｄ ｓｅｔｔｉｎｇ(ｒｅｐｌａｃｅｍｅｎｔ)ｍｅｃｈａｎｉｓｍｓ ｏｆ ｃｏｒａｌ ｒｅｅｆ ａｌｇｏｒｉｔｈｍꎬ ｗｈｉｃｈ ｇｒｅａｔｌｙ ｉｍｐｒｏｖｅｓ ｔｈｅ ｏｐｔｉｍｉｚａｔｉｏｎ ｐｒｅｃｉｓｉｏｎ ｏｆ ｔｈｅ ａｌｇｏｒｉｔｈｍ ａｎｄ ｃａｎ ｊｕｍｐ ｏｕｔ ｏｆ ｔｈｅ ｌｏｃａｌ ｏｐｔｉｍｕｍ. Ｉｎ ｔｈｅ ｓｉｍｕｌａｔｉｏｎ ｅｘｐｅｒｉｍｅｎｔꎬ ｔｈｅ ｉｍｐｒｏｖｅｄ ｃｏｒａｌ ｒｅｅｆ ａｌｇｏｒｉｔｈｍ ｉｓ ｒｅｓｐｅｃｔｉｖｅｌｙ ｃｏｍｐａｒｅｄ ｗｉｔｈ ｔｅｎ ａｌｇｏｒｉｔｈｍｓ ｓｕｃｈ ａｓ ｂａｓｉｃ ｃｏｒａｌ ｒｅｅｆ ａｌｇｏｒｉｔｈｍ ａｎｄ ｐａｒｔｉｃｌｅ ｓｗａｒｍ ａｌｇｏｒｉｔｈｍ ｉｎ ｈｉｇｈ￣ｄｉｍｅｎｓｉｏｎａｌ ａｎｄ ｌｏｗ￣ｄｉｍｅｎｓｉｏｎａｌ ｔｅｓｔ ｆｕｎｃｔｉｏｎｓ. Ｔｈｅ ｅｘｐｅｒｉｍｅｎｔａｌ ｒｅｓｕｌｔｓ ｓｈｏｗ ｔｈａｔ ｔｈｅ ｉｍｐｒｏｖｅｄ ｃｏｒａｌ ｒｅｅｆ ａｌｇｏｒｉｔｈｍ ｈａｓ ｂｅｔｔｅｒ ｃｏｎｖｅｒｇｅｎｃｅ ｒａｔｅ ａｎｄ ａｃｃｕｒａｃｙ ｔｈａｎ ｏｔｈｅｒ ａｌｇｏｒｉｔｈｍｓꎬ ｗｈｉｃｈ ｃａｎ ｓｔｉｌｌ ｂｅ ｍａｉｎｔａｉｎｅｄ ｉｎ ｔｈｅ ｈｉｇｈ￣ｄｉｍｅｎｓｉｏｎａｌ ｔｅｓｔ ｆｕｎｃｔｉｏｎｓ. [ABSTRACT FROM AUTHOR]

The whale optimization algorithm(WOA) has slow convergence speed and low convergence accuracy and tends to fall into local optimum.In order to solve these problems， a whale optimization algorithm(AWOA) based on adaptive adjustment of weight and search strategy was proposed.An adaptive adjustment of weight with the current distribution of whale population was designed to improve the convergence speed of the algorithm， and an adaptive adjustment of search strategy was designed to improve the ability of the algorithm to jump out of local optimum.Using 23 standard test functions， the algorithm was tested for high-dimensional and low-dimensional problems， respectively.The simulation results showed that the AWOA is generally superior to other improved whale optimization algorithms in terms of convergence accuracy and convergence speed. [ABSTRACT FROM AUTHOR]