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中文摘要：
针对利用粒子群优化算法进行多极值函数优化时存在早熟收敛和搜索效率低的问题，提出混合的PSO-BFGS算法，并增强了混合算法的变异能力使算法能逃出局部极值点。通过对三种Benchmark函数的测试结果表明，PSO-BFGS算法不仅具有有效的全局收敛性能，而且还具有较快的收敛速度，是求解最优化问题的一种有效算法。
英文摘要：
To overcome the problem of premature convergence on Particle Swarm Optimization （PSO） in optimizing multimodal function, this paper proposed a hybrid algorithm of PSO-BFGS, and used a special mutation to make particles escape local minima. Three benchmark functions were selected as the test functions. The experimental results show that the PSO-BFGS algorithm not only can effectively locate the global optimum, but also have a rather high convergence speed, The PSO-BFGS algorithm is a promising approach for solving global optimization problems.
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Design and Realization of Software for Guard Against DDoS Based on Self_similar and Optimization Fil
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