Bio-inspired Computing for Problems with Chance Constraints

生物启发算法已经成功地应用于广泛的优化问题。现实应用中的不确定性可能导致生产计划或安全关键系统的重大故障。机会约束为这种不确定性建模，并允许限制这种失败的可能性。这个未来的奖学金建立了生物启发计算的领域，以解决有机会限制的问题。它为随机问题开发了高性能的生物启发算法，其中约束只能以小概率被违反。研究结果将为采矿和制造业等国家优先领域的复杂规划过程带来更有效和可靠的优化方法。

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.