Bio-inspired Computing for Problems with Chance Constraints

仿生算法已经成功地应用于各种优化问题。实际应用中的不确定性可能会导致生产计划或安全关键系统的严重故障。机会约束模拟了这种不确定性，并允许限制此类失败的可能性。这一未来的奖学金为解决有机会约束的问题建立了生物启发计算领域。它为随机问题开发了高性能的仿生算法，其中约束只能以很小的概率被违反。研究结果将为采矿和制造业等国家重点领域的复杂规划流程提供更有效、更可靠的优化方法。

Bio-inspired algorithms have successfully been applied to a wide range of optimisation problems. Uncertainties in real-world applications can lead to critical failures of production schedules or safe critical systems. Chance constraints model such uncertainties and allow to limit the possibility of such failures. This future fellowship builds up the area of bio-inspired computing for problems with chance constraints. It develops high performing bio-inspired algorithms for stochastic problems where the constraints can only be violated with a small probability. The outcomes will lead to more effective and reliable optimisation methods for complex planning processes in areas of national priority such as mining and manufacturing.