Purpose The purpose of this paper is to propose a new hybrid algorithm, named improved plant growth simulation algorithm and genetic hybrid algorithm (PGSA-GA), for solving structural optimization problems. Design/methodology/approach PGSA-GA is based on PGSA and three improved strategies, namely, elitist strategy of morphactin concentration calculation, strategy of intelligent variable step size and strategy of initial growth point selection based on GA. After a detailed formulation and explanation of its implementation, PGSA-GA is verified using the examples of typical truss and single-layer lattice shell. Findings Improved PGSA-GA was implemented and optimization was carried out for two typical optimization problems; then, a comparison was made between the PGSA-GA and other methods. The results show that the method proposed in the paper has the advantages of high efficiency and rapid convergence, which enable it to be used for the optimization of various types of steel structures. Originality/value Through the examples of typical truss and single-layer lattice shell, it shows that the optimization efficiency and effect of PGSA-GA are better than those of other algorithms and methods, such as GA, secondary optimization method, etc. The results show that PGSA-GA is quite suitable for structural optimization.

Semantic relatedness measures are used in many applications in natural language processing and we propose a Wikipedia-based method to compute it. Unlike existed methods that only focus on a small section of Wikipedia (e.g. info box or hyperlinks), our method makes full use of the rich information contained in the Wikipedia page and could get a higher accuracy within reasonable time. In our method, we first use some special sections (e.g. synonyms and hyponyms) in the Wikipedia page to judge whether two concepts are closely related. If they are not, we then use pattern matching to find whether they are related through usual relatedness (e.g.-a part of‖,-result in‖, and-is a member of ‖). And if the relatedness score hasn't been computed out through former steps, we then use a method which makes some improvement on the famous explicit semantic analysis method to compute the relatedness.

Domain ontology is a collection of domain-specific concepts and their interrelationships, which provide an abstract view of the application domain and is used in many areas such as semantic mining(SM) and natural language processing(NLP). But the direct construction of Domain ontology manually is labor intensive and time consuming, while auto-generated Domain-specific Lexical Repository can be used to build domain ontology as an indispensable component. In this paper, we propose a two-stage method to build domain-specific lexical repository making use of the dump service of Chinese Wikipedia. The main idea is that only concepts strongly semantic-related to the multi roots we choose are incorporate into the repository. First we use the dump service for all pages(zhwiki-all-pages.xml) of Chinese Wikipedia to generate a graph of all Wikipedia concepts, we call it pre-stage. Then we enter stage one by selecting three top-level nodes as roots, traversing the graph generated in the pre-stage using BFS-like algorithm to form spanning trees and computing rough domain relatedness of these nodes at the same time. Finally, in stage two we use the novel Modified Explicit Semantic Analysis method combined with the results we got in stage one to compute the ultimate domain relatedness. The experimental results shows that our method could get a high-quality domain-specific lexical repository.