Stochastic differential equations (SDEs) have been applied by engineers and economists because it can express the behavior of stochastic processes in compact expressions. In this paper, by using Grunwald-Letnikov fractional derivative, the stochastic differential model is improved. Two numerical examples are presented to show efficiency of the proposed model. A numerical optimization approach b...

Here, issues connected with characteristic stochastic practices are considered. In the first part, the plausibility of covering the arrangements of an improvement issue on subjective subgraphs is studied. The impulse for this strategy is a state where an advancement issue must be settled as often as possible for discretionary illustrations. Then, a preprocessing stage is considered that would q...

We study contextual stochastic optimization problems, where we leverage rich auxiliary observations (e.g., product characteristics) to improve decision making with uncertain variables demand). show how train forest policies for this problem by growing trees that choose splits directly optimize the downstream quality rather than split prediction accuracy as in standard random algorithm. realize ...

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Colliding bodies optimization (CBO) is a new population-based stochastic optimization algorithm based on the governing laws of one dimensional collision between two bodies from the physics. Each agent is modeled as a body with a specified mass and velocity. A collision occurs between pairs of objects to find the global or near-global solutions. Enhanced colliding bodies optimization (ECBO) uses...

This paper proposes a stochastic framework for demand response (DR) aggregator to procure DR from customers and sell it to purchasers in the wholesale electricity market. The aggregator assigns fixed DR contracts with customers based on three different load reduction strategies. In the presented problem the uncertainty of market price is considered and the risk of aggregator participation is ma...

Stochastic compositional optimization generalizes classic (non-compositional) stochastic to the minimization of compositions functions. Each composition may introduce an additional expectation. The series expectations be nested. is gaining popularity in applications such as reinforcement learning and meta learning. This paper presents a new Stochastically Corrected Compositional gradient method...