An adaptation of the oscars algorithm for bound constrained global optimization is presented, and numerically tested. The algorithm is a stochastic direct search method, and has low overheads which are constant per sample point. Some sample points are drawn randomly in the feasible region from time to time, ensuring global convergence almost surely under mild conditions. Additional sample points are preferentially placed near previous good sample points to improve the rate of convergence. Connections with partitioning strategies are explored for oscars and the new method, showing these methods have a reduced risk of sample point redundancy. Numerical testing shows that the method is viable in practice, and is substantially faster than oscars in 4 or more dimensions. Comparison with other methods shows good performance in moderately high dimensions. A power law test for identifying and avoiding proper local minima is presented and shown to give modest improvement.

提出了一种用于约束约束全局最优化的oscars算法，并进行了数值测试。该算法是一种随机直接搜索方法，具有较低的开销（每个采样点恒定）。不时在可行区域随机抽取一些采样点，以确保在温和条件下几乎可以肯定地收敛。优先将其他采样点放在先前的好采样点附近，以提高收敛速度。探索了与分区策略的联系来评估奥斯卡奖和新方法，这表明这些方法降低了采样点冗余的风险。数值测试表明，该方法在实践中是可行的，并且比奥斯卡更快4个或更多尺寸。与其他方法的比较表明，在中等尺寸的情况下性能良好。提出了用于确定和避免适当的局部最小值的幂定律测试，该测试表明该测试可以带来适度的改进。