Optimal Inverse Functions Created via Population-Based Optimization
Optimal Inverse Functions Created via Population-Based Optimization
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DOI：
10.1109/tcyb.2013.2278102
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发表时间：
2014-06
影响因子：
11.8
通讯作者：
A. Jennings;R. Ordóñez
中科院分区：
文献类型：
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作者：
A. Jennings;R. Ordóñez
为多输入单输出系统寻找最优输入对系统操作员来说是一项繁重的工作。基于群体的优化用于创建基于期望输出产生局部最优输入的函数集。操作员或更高级别的计划员可以在真实的时间中使用其中一个功能。对于优化，群体中的每个代理使用成本和输出梯度来采取降低成本的步骤，同时保持其当前输出。当一个代理达到其当前输出的最佳输入时，在输出梯度方向上生成额外的代理。然后，新的代理将新的输出值设置为局部最优值。相关联的最佳点的集合经由样条插值形成从期望输出到最佳输入的反函数。以这种方式，可以创建多个局部最优函数。这些函数自然地聚集在输入和输出空间中，从而允许连续的反函数。操作员在期望输出的预期范围内选择最佳集群，并在保持最优性的同时调整设定点（期望输出）。这减少了从控制多个输入到控制单个设定点而不损失性能的需求。结果表明，在一个样本集的功能和机器人控制问题。
Finding optimal inputs for a multiple-input, single-output system is taxing for a system operator. Population-based optimization is used to create sets of functions that produce a locally optimal input based on a desired output. An operator or higher level planner could use one of the functions in real time. For the optimization, each agent in the population uses the cost and output gradients to take steps lowering the cost while maintaining their current output. When an agent reaches an optimal input for its current output, additional agents are generated in the output gradient directions. The new agents then settle to the local optima for the new output values. The set of associated optimal points forms an inverse function, via spline interpolation, from a desired output to an optimal input. In this manner, multiple locally optimal functions can be created. These functions are naturally clustered in input and output spaces allowing for a continuous inverse function. The operator selects the best cluster over the anticipated range of desired outputs and adjusts the set point (desired output) while maintaining optimality. This reduces the demand from controlling multiple inputs, to controlling a single set point with no loss in performance. Results are demonstrated on a sample set of functions and on a robot control problem.