A Novel Improved Bat Algorithm in UAV Path Planning

Path planning algorithm is the key point to UAV path planning scenario.Many traditional path planning methods still suffer from low convergence rate and insufficient robustness.In this paper,three main methods are contributed to solving these problems.First,the improved artificial potential field(APF)method is adopted to accelerate the convergence process of the bat’s position update.Second,the optimal success rate strategy is proposed to improve the adaptive inertia weight of bat algorithm.Third chaos strategy is proposed to avoid falling into a local optimum.Compared with standard APF and chaos strategy in UAV path planning scenarios,the improved algorithm CPFIBA(The improved artificial potential field method combined with chaotic bat algorithm,CPFIBA)significantly increases the success rate of finding suitable planning path and decrease the convergence time.Simulation results show that the proposed algorithm also has great robustness for processing with path planning problems.Meanwhile,it overcomes the shortcomings of the traditional meta-heuristic algorithms,as their convergence process is the potential to fall into a local optimum.From the simulation,we can see also obverse that the proposed CPFIBA provides better performance than BA and DEBA in problems of UAV path planning.

The Practicalities of Developing A Successful E-Business Strategy

本文作者是美国BDO Seidman LLP公司的商务和技术研究部主管并领导该公司在美国西部地区的电子商务。据Forrester Rearch预测,到2003年美国商务部门之间的Internet销售额将达到1.3兆美元;到2004年商务部门与顾客之间的Internet销售额也将达到1000亿美元。任何商业组织想生存和发展,都必须从事和开发电子商务。然而E-business风险大,陷阱多。75％的E-Business可能以失败而告终。本文作者在文中指出了避免失败的关键。