In this paper, we are devoted to the construction and analysis of research network. Firstly, on the basis of data Erdos1, a co-author network is built and two novel measures are proposed to analyze properties of the co-author network. A data extraction method using string matching technique is developed and the network is visualized using UCINET. Then, the first-order and second-order entropy are defined to depict the complexity of the network, and the node’s invalidity probability and load-bearing capacity are defined to depict the robustness of the network.

The combat environment of Unmanned Aerial Vehicles (UAVs) is filled with uncertain factors, which is complex and dynamic. This paper is devoted to the UAV mission planning problem under uncertain environment with three optimization objectives, such as flight time, fuel usage and threat imposed by enemy. Based on the uncertainty theory and multiobjective programming method, the UAV uncertain multiobjective mission plaaning model is built and solved.

Abstract:The double evolutional artificial bee colony algorithm (DEABC) is proposed for solving the single depot multiple traveling salesman problem (MTSP). The proposed DEABC algorithm, which takes advantage of the strength of the upgraded operators, is characterized by its guidance in exploitation search and diversity in exploration search. The double evolutional process for exploitation search is composed of two phases of half stochastic optimal search, and the diversity generating operator for exploration search is used for solutions which cannot be improved after limited times. The computational results demonstrated the superiority of our algorithm over previous state-ofthe-art methods.

Travelling salesman problem is a fundamental combinatorial optimization model studied in the operations research community, and yet, there is surprisingly little literature that addresses stochastic uncertainties and multiple objectives in it simultaneously. This paper is devoted to a novel TSP variation called stochastic multiobjective TSP (SMOTSP) with random variables on the arc, and a new solution approach is proposed to obtain Pareto efficient route in it, whose validity is proved finally.

Based on the credibility theory, this paper is devoted to the fuzzy programming problem. The expected-value model of fuzzy programming problem is provided under credibility theory. For solving the fuzzy programming problem efficiently, Latin Hypercube Sampling, fuzzy simulation, Support Vector Machine and Artificial Bee Colony algorithm are integrated to build a hybrid intelligent algorithm. The proposed method has excellent consistency and efficiency in solving fuzzy programming problem, and is particularly useful for expensive systems.