This special issue comprises of eight selected papers from the WASE Global Congress on Science Engineering (GCSE 2009), Shanxi, China, 25-27 December 2009. The conference received more than 1000 paper submissions from 12 countries and regions, of which 273 were selected for presentation after a rigorous review process. From these 273 research papers, through two rounds of reviewing, the guest editors selected ten as the best papers on the computer network track of the Conference. The candidates of the Special Issue are all the authors, whose papers have been accepted and presented at the 2009 GCSE, with the contents not been published elsewhere before.
   The 2009 GCCS are sponsored by World Association of Science Engineering, Technical Co-Sponsors of the conference are University of London and Taiyuan University of Technology.
“An Effective Adaptive Multi-objective Particle Swarm for Multimodal Constrained Function Optimization”, by Yongquan Zhou and Shengyu Pei, presents a novel adaptive multi-objective particle swarm algorithm for solving constrained function optimization problems, in which Pareto non-dominated ranking, tournament selection, crowding distance method were introduced, simultaneously the rate of crowding distance changing were integrated into the algorithm. Simulation results based on four test functions demonstrate the effectiveness, efficiency and practical.
   “Research on Formal Verification Technique for Aircraft Safety-Critical Software”, by Yongfeng Yin, Bin Liu and Duo Su, grounding on abundant experience and research on real-time embedded software testing technique to propose a new and effective formal verification method for aircraft safety-critical software.
“Determination of Optimal SVM Parameters by Using GA/PSO”, by Yuan Ren and Guangchen Bai, proposes automated, reliable, and relatively fast approaches for determining the SVM parameters based on genetic algorithm and particle swarm optimization.  
   “Determination of Optimal SVM Parameters by Using GA/PSO”, by Yuan Ren and Guangchen Bai, proposes automated, reliable, and relatively fast approaches for determining the SVM parameters based on genetic algorithm and particle swarm optimization.
   “A Large-Scale Device Collaboration Performance Evaluation Approach Based-on Dynamics”, by Xiaohui Rong, Feng Chen, Pan Deng, and Shilong Ma, establishes a dynamics model of large-scale device collaboration performance and presented a large-scale device collaboration performance evaluation approach based-on dynamical system theory.
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